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Enhancing Schizophrenia Prediction Using Class Balancing and SHAP Explainability Techniques on EEG Data 13

Enhancing Schizophrenia Prediction Using Class Balancing and...

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IEEE 13th international conference on pattern recognition Systems (ICPRS)

作者： Arias, Javiera T. Astudillo, Cesar A. Univ Talca Engn Syst Curico Chile Univ Talca Fac Engn Dept Comp Sci Curico Chile

ISBN: (纸本)9798350333374

machine learning (ML) makes predictions or supports decision making based on data, achieving high accuracy, saving time and resources, and even running real-time analysis. However, one drawback of these models is the lack of transparency in complex models, reducing confidence in sensitive fields such as health. This paper analyzes electroencephalogram (EEG) data to predict schizophrenia in patients. Three classifiers are compared, considering Support Vector machines(SVM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Three metrics are used to measure the classification process, including accuracy (ACC), area under the curve (AUC) and F-1 score (F1). XAI is incorporated into the pipeline to identify relevant features. XGBoost is the model with the best performance in predicting schizophrenia cases, reaching an ACC = 0.93, AUC = 0.93 and F1 score = 0.92, outperforming the SVM and AdaBoost algorithms. The SHAP explainability technique was applied on the XGBoost model, identifying the sex, IQ, delta T6, and delta Pz waves as the most relevant characteristics in the prediction processes. Based on the data analysis, we found that schizophrenia causes an alteration of the delta wave in an EEG, which is different to other mental illnesses. On the other hand, the study generates a specific impact by showing that XGBoost presents better results in 3 validation metrics (ACC, AUC, and F1 Score) compared to SVM and AdaBoost. It is shown that balancing classes is essential to obtain better ML predictions.

关键词： Applied machine Learning Explainable Artificial intelligence Mental Disorders Schizophrenia

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A Novel Link Prediction Approach for MOOC Forum Thread Recommendation Using Personalized PageRank and machine Learning 3

A Novel Link Prediction Approach for MOOC Forum Thread Recom...

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3rd international conference on Educational Technology, ICET 2023

作者： He, Junfu Liu, Zhi Kong, Xi Central China Normal University Wollongong Joint Institute Central China Normal University WuHan China Central China Normal University Faculty of Artificial Intelligence in Education WuHan China

ISBN: (纸本)9798350302332

MOOCs are online learning platforms that offer free and open educational resources for global learners. Its online forums enable learners to interact with instructors and peers and thus improve their learning outcomes and social presence. However, learners may struggle to find posts that match their goals and preferences due to the large scale and diversity of MOOCs. A well-designed MOOC forum thread recommendation system can help learners overcome information overload and improve their learning experience. In this paper, we consider the recommendation of forum threads as a link prediction problem and propose a novel link prediction approach based on various Heterogeneous graph feature extraction methods with LightGBM. We conduct experiments on real-world data from a MOOC platform to evaluate the effectiveness of our model. Results show that our approach outperforms various baselines in terms of precision and recall. Our findings demonstrate the effectiveness of combining PageRank with machine learning and our model's potential to increase student engagement in learning discussion and foster socio-collaborative learning. © 2023 IEEE.

关键词： Recommender systems

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Fingerprint Identification using Ridge Lines 3

Fingerprint Identification using Ridge Lines

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Liu, Ziqi Niu, Yucheng Qu, Qianhou University of Sheffield Department of Information Management Sheffield United Kingdom University of Delaware Department of Engineering NewarkDE19711 United States Hebei University of Technology Department of Artificial Intelligence and Data Science Tianjin300000 China

ISBN: (纸本)9781665459112

This design is based on MATLAB fingerprint identification system. This designed system mainly carries of three consisting of fingerprint image processing, image preprocessing, feature extraction, and feature matching. Image preprocessing includes four steps: image graying, filtering enhancement, binarization, and refinement. After the fingerprint image is preprocessed, the redundant part of the original image is removed, which is convenient for subsequent recognition processing. Feature extraction is mainly to extract the endpoints and bifurcation points of the fingerprint image after refinement. Feature matching is to compare the feature points of two fingerprint images to determine whether the two images are from the same finger. Experiments on real datasets validate the effectiveness of our method. © 2022 IEEE.

关键词： Feature extraction

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ICPR 2024 Competition on Multilingual Claim-Span Identification 27th

ICPR 2024 Competition on Multilingual Claim-Span Identifica...

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27th international conference on pattern recognition, ICPR 2024

作者： Poddar, Soham Paul, Biswajit Basu, Moumita Ghosh, Saptarshi Indian Institute of Technology Kharagpur Kharagpur721302 India Centre for Artificial Intelligence and Robotics DRDO Bangalore560093 India NSHM Institute of Computing and Analytics NSHM Knowledge Campus Kolkata700053 India

ISBN: (纸本)9783031801389

A lot of claims are made in social media posts, which may contain misinformation or fake news. Hence, it is crucial to identify claims as a first step towards claim verification. Given the huge number of social media posts, the task of identifying claims needs to be automated. This competition deals with the task of ‘Claim Span Identification’ in which, given a text, parts/spans that correspond to claims are to be identified. This task is more challenging than the traditional binary classification of text into claim or not-claim, and requires state-of-the-art methods in pattern recognition, Natural Language Processing and machine Learning. For this competition, we used a newly developed dataset called HECSI containing about 8K posts in English and about 8K posts in Hindi with claim-spans marked by human annotators. This paper gives an overview of the competition, and the solutions developed by the participating teams. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Claim detection Claim-span identification English Hindi social media

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Analyzing the Multifaceted Interactions of Modern Digital Dreamscapes and Deep Data Insights 3

Analyzing the Multifaceted Interactions of Modern Digital Dr...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Mothilal, T. Balaji, V. Natrayan, L. Yamsani, Nagendar Kcg College of Technology Department of Automobile Engineering Karapakkam TN Chennai India Loyola Institute of Technology Department of Mechanical Engineering TN Chennai India Saveetha School of Engineering Simats Department of Mechanical Engineering TN Chennai India School of Computer Science & Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

Through a methodologically exhaustive approach, this study investigates the complex relationships between contemporary digital dreamscapes and deep data insights. Natural Language Processing (NLP), Social Network Analysis (SNA), machine Learning Algorithms (MLA), Data Mining (DM), Topic Modeling (TM), and Sentiment Analysis (SA) are among the six original methods compared to the proposed method, which combines ethnographic research with cutting-edge data analysis techniques. To emphasize the relative performance and ethical implications of the methodologies, four different visualizations are offered, including a bar chart, scatter plot, pie chart, and box plot. According to the results, the suggested technique is superior to the conventional ones in many respects, including accuracy, precision, recall, F1-Score, processing time, privacy, data security, ethics, cultural sensitivity, scalability, and adaptability. Considering these findings, an interdisciplinary approach is necessary for evaluating digital dreamscapes, one that takes into consideration cultural, ethical, and data-driven factors. © 2023 IEEE.

关键词： Scalability

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Drowsy Driver Detection System using Computer Vision and Transfer Learning for Preventing Road Accidents 3

Drowsy Driver Detection System using Computer Vision and Tra...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Bagal, Naisargi Dingreja, Kashish Atterkar, Atharva Bachhav, Prasad Sable, Nilesh P. Yenkikar, Anuradha Alkunte, Ritesh Vishwakarma Institute of Information Technology Department of Computer Science and Engineering - Artificial Intelligence Pune India

ISBN: (纸本)9798350354348

Major traffic accidents are attributed to driver fatigue, according to study on the topic. Driver drowsiness is a state in which the driver of a car is on the verge of falling asleep or losing consciousness. It can be brought on by a number of variables, including biological, physical, psychological, and other factors, all of which can impede and inhibit safe driving. The several methods that can be applied to the implementation of a system for the detection of driver sleepiness will be reviewed in this paper. Improving the ability to identify drowsiness in real time through computer vision is the main objective of all technologies. Thus, our work also pertains to the identification of driver drowsiness, whereby a motorist's tiredness can be ascertained by first recognizing their face, followed by eye tracking. The system compares the extracted eye image with the dataset. The system used the dataset and predicts using transfer learning with our base model as InceptonV3 to identify that it could alert the driver with an alarm if the driver's eyes were closed for a predetermined amount of time, and it could resume monitoring if the driver's eyes were open following the alarm alert. We established a score that grew if the eyes were closed and dropped if they were open. This study aims to reduce traffic accidents by solving the sleepiness detection problem with InceptionV3 with an accuracy of 95.46% compare to 92.11% of ***, driving drowsiness detection - which assesses a driver's degree of fatigue using facial recognition and eye tracking becomes the main focus of our research. © 2024 IEEE.

关键词： Face recognition

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3rd international conference on River Corridor Research and Management, RCRM 2023

3rd International Conference on River Corridor Research and ...

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3rd international conference on River Corridor Research and Management, RCRM 2023

ISBN: (纸本)9789819712267

The proceedings contain 21 papers. The special focus in this conference is on River Corridor Research and Management. The topics include: LULC Changes: Implications for Reclamation of Ecosystem Hydrological Services: An Empirical Study;artificial intelligence-Based Monthly Rainfall-Runoff Modeling in India’s Bardha Watershed;what Controls the Runoff Generation in River Basins?;study River Dynamics: Backwater Effect in the Upstream of Kelo Dam in Mahanadi River, Chhattisgarh, India;quantifying Changes in Downstream Flow Characteristics in Sinuous Channels Due to the Series of Floodplain Sand Mining Pits;geo-Morphological Assessment in the Upper Reaches of Krishna River (India) Using Multi-temporal Satellite Data During 1991–2021;geoinformatics-Based Morphologic Analysis of the Ramis River, an Endorheic Andean River;hydrological and Morphlogical Considerations for Deciding Location, Waterway, Afflux and Scour in River Bridges;erosion and Accretion in the Netravati River Stretch: Spatiotemporal Analysis Using Geospatial Approach;laboratory Investigation on Rainfall-Runoff-Erosion Characteristics Under Varying Vegetation Layouts;influence of Microplastics on the Hydraulic Conductivity of Riverbed System;review on Nature-Like Step-Pool Fishways;occurrence of Sedimentary Iron and Arsenic Along the Beas River and Implications for Arsenic Enrichment in the Sutlej-Indus River Basin, India;distribution of Arsenic and Iron in Hyporheic Zone Sediments Along the Hooghly River;flood Inundation Mapping of Krishnaraja Nagar, Mysore Using Sentinel-1 Sar Images;applications of machine Learning Algorithms via Google Earth Engine Interface to Interpret Snowline Altitudes: A Case Study in Chandra Basin;river and Lake Monitoring Through Underwater Remotely Operated Vehicle;in-House Developed IoT-Enabled Water Quality Measuring Device.

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Discovering Innovative Applications for Real-Time Data Analysis with machine Learning 3

Discovering Innovative Applications for Real-Time Data Analy...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jadhav, Prranjali Raja Praveen, K.N. Aggarwal, Prateek Gautam, Sandeep Naval, Preeti Nirmala, D. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

This paper investigates the capability for discovering modern programs for actual-time information analysis the use of system learning. We will take a look at the technology and strategies used in system getting to know and talk their adaptive potential to interpret and analyze dynamically changing facts inputs. Especially, we can examine existing technological methods to understand the capacity of AI to discover creative solutions to complicated challenges. We will also explore capability techniques to contain gadget gaining knowledge of into present real-time records analysis platforms and architectures. Finally, we are able to speak the programs of machine studying in real-time records evaluation and its potential to allow fast and correct decision making. This paper gives a theoretical framework to apprehend and harness the power of device gaining knowledge of to investigate and interpret real-time facts in modern approaches. © 2023 IEEE.

关键词： Information analysis

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An Extensive machine Learning Framework Employing Ensemble Classifiers for Heart Disease Detection 3

An Extensive Machine Learning Framework Employing Ensemble C...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chandralekha, E. Vinodhini, S. Kandasamy, V. Rama, P. Ravikumar, S. Saravanan, T.R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept.of CSE Chennai India Velammal Engineering College Dept. of Information Technology Chennai India Panimalar Engineering College Dept. of Information Technology Chennai India College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Chennai India SRM Institute of Science and Technology Dept. of Computational Intelligence Chennai India

ISBN: (纸本)9798350343632

Heart disease is a significant global health challenge, impacting many lives and often being a leading cause of mortality and illness. Detecting heart disease accurately and promptly is essential for effective medical care. Due to the complex interplay of risk factors and the intricate nature of diagnosis, advanced technologies like machine learning are being explored. This study introduces a comprehensive framework that employs machine learning techniques for heart disease detection. The framework involves critical stages such as data preprocessing, feature selection, and training ensemble classifiers. Experimental results highlight the importance of specific attributes in each classifier, offering insights into predictions. Notably, attributes like oldpeak, thalach, ca, cp, and thal are key features in the Random Forest classifier, while Gradient Boosting underscores ca, cp, thal, oldpeak, and age. AdaBoost and the Extra Trees Classifier reveal similar significant attributes. Evaluation metrics emphasize the models' robust performance, with the Extra Trees Classifier achieving the highest accuracy. This research contributes to a practical heart disease detection framework with potential for improving medical decision-making. © 2023 IEEE.

关键词： AdaBoost Extra Trees Classifier Gradient Boosting machine Learning Random Forest

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Ethereum Cryptocurrency Prediction using ML procedures on Recurrent Neural Network using LSTM Model 3

Ethereum Cryptocurrency Prediction using ML procedures on Re...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Garg, Ashish Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350319125

Forecasting the value of Ethereum (ETH) or any other cryptocurrency is a formidable undertaking owing to the inherent volatility and speculative characteristics shown by these digital assets. Nevertheless, it is possible to create price forecasts by using machine learning techniques, namely Recurrent Neural Networks (RNNs), which are capable of capturing temporal relationships within the data. The challenge of forecasting the price of Ethereum (ETH) or any cryptocurrency is a multifaceted endeavour that encompasses aspects of finance, economics, and data science. The practise of technical analysis is the examination of past price charts, patterns, and technical indicators in order to make forecasts about future price fluctuations. The underlying assumption is that previous pricing patterns had the capacity to provide valuable insights into future developments. Nevertheless, it is essential to acknowledge that the effectiveness of technical analysis within the realm of cryptocurrency trading is a subject that engenders much scholarly discourse. The primary objective of this research is to examine the utilisation of Ethereum cryptocurrency and forecast its behaviour via the use of machine learning methodologies, namely Recurrent Neural Networks. The suggested approach demonstrates a high level of accuracy, reaching 95 percent. This significant level of precision will be beneficial for future academics working on this technology. © 2023 IEEE.

关键词： Artificial intelligence Classification Deep Learning Ethereum Cryptocurrency Analysis LSTM Model Training

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