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Outlier quantification for multibeam data

Informatik-Spektrum 2022年第4期45卷 218-222页

作者： Ziolkowski, Tobias Koschmider, Agnes Kröger, Peer Devey, Colin Helmholtz Centre for Ocean Research Kiel Kiel Germany Group Process Analytics Computer Science Department Kiel University Kiel Germany Group Information Systems and Data Mining Computer Science Department Kiel University Kiel Germany

This paper discusses the challenges of applying a data analytics pipeline for a large volume of data as can be found in natural and life sciences. To address this challenge, we attempt to elaborate an approach for an improved detection of outliers. We discuss an approach for outlier quantification for bathymetric data. As a use case, we selected ocean science (multibeam) data to calculate the outlierness for each data point. The benefit of outlier quantification is a more accurate estimation of which outliers should be removed or further analyzed. To shed light on the subject, this paper is structured as follows: first, a summary of related works on outlier detection is provided. The usefulness for a structured approach of outlier quantification is then discussed using multibeam data. This is followed by a presentation of the challenges for a suitable solution, and the paper concludes with a summary. © 2022, The Author(s).

关键词： Data Analytics

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Machine Learning-Based Automated Medical Diagnosis for Healthcare 6

Machine Learning-Based Automated Medical Diagnosis for Healt...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Khatter, Harsh Yadav, Ankit Srivastava, Ayush Group of Institutions Delhi-NCR Department of Computer Science Kiet Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350346961

A sizable segment of the world's population lacks access to quality healthcare. The success of healthcare ultimately depends on the doctor's skill. In this study, we investigate if this knowledge may be represented as an information corpus, or as data that has been retrieved using data mining methods, particularly the Machine Learning & Deep Learning Model, to make a diagnosis. When the medical diagnosis is made widely available, coverage increases, and life quality improves. In order to determine whether inferences about the causes of various diseases can be made from the data, this paper provides an overview of machine learning approaches used in the classification of various diseases. We outline a few of our findings from the trials we ran before offering some suggestions for the future. The difference between the current state of health and an acceptable or desirable health condition is the health problem. By lowering doctor visits, hospital stays, and diagnostic testing procedures, monitoring systems are designed to lower health care expenditures. Using the data mining modeling technique, the integration of clinical decision support with computer-based patient records could decrease medical errors, increase patient safety, stop unwelcome practice variance, and improve practice outcomes. Connecting patients and doctors through a user- friendly interface will make it easier for patients to use in emergency situations.. © 2023 IEEE.

关键词： Diagnosis

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Automatic Detection of Hope Speech 6

Automatic Detection of Hope Speech

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6th Doctoral Symposium on Natural Language Processing from the Proyecto ILENIA, PLN-DS 2023

作者： García-Baena, Daniel Computer Science Department SINAI research group CEATIC Universidad de Jaén Spain

Hope speech is a type of discourse that has the power to help, inspire people for good and even relax hostile environments. The automatic detection of hope speech is an open challenge in Natural Language Processing that has been generally eclipsed by hate speech detection. Rather than simply deleting hate speech from the Internet, restricting freedom of speech, according to the outstanding importance that psychology gives to hope and the success that some social experiments had when they highlighted hope speech over the rest of the texts, we find specially necessary to study in depth the automatic identification of hope speech. In this work, we describe a thesis project that focuses on the development of new datasets and systems that allow the automatic detection, by means of different classical machine learning techniques and new deep learning architectures, of hope speech, mainly in Spanish. © 2023 Copyright for this paper by its authors.

关键词： Deep learning

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Heart Disease Prediction Model using various Supervised Learning Algorithm 12

Heart Disease Prediction Model using various Supervised Lear...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Upadhyay, Sachin Dwivedi, Anjali Verma, Ashutosh Tiwari, Vatsya Gla University Department of Computer Engineering & Application Mathura India Government Girls Polytechnic Computer Science and Engineering Amethi India G.L. Bajaj Group of Institutions Computer Science & Engineering Mathura India Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India

ISBN: (纸本)9781665462617

A computer can learn from data and improve its predictive abilities without requiring human interaction through artificial intelligence (AI) derived from machine learning (ML). Machine learning's core concept is to replicate how the brain functions. ML approaches have proven to be effective predictors in a variety of application fields, including healthcare and medicine. In this study, supervised machine learning approaches for predicting cardiac disorders were analyzed and compared using medical records from the UCI Machine Learning repository. This research examines the effectiveness of various models, including K-Nearest Neighbor (KNN), Logistic Regression (LR) models and Support Vector Machines (SVM), and The (Area Under ROC Curve) AUC score was used to evaluate the effectiveness of various algorithms. AUC is a measurement. In order to evaluate the effectiveness of various algorithms, we used the (Area Under ROC Curve) AUC score. The choice to adopt a machine learning algorithm is made if the AUC score is more than 0.5, which is an evaluation metric that aids in validating how effective the algorithm is. AUC scores for logistic regression are the highest of all ML algorithms in the trial (0.87). © 2023 IEEE.

关键词： Support vector machines

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Software Systems Using Variability Approaches 5

Software Systems Using Variability Approaches

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Arya, Ashima Gupta, Prince Singhal, Swasti Goel, Akash Kiet Group of Institutions Department of Computer Science and Information Technology Uttar Pradesh Ghaziabad201206 India Kiet Group of Institutions Delhi-NCR Departmrnt of Computer Science Uttar Pradesh Ghaziabad201206 India

ISBN: (纸本)9798350321425

A system's ability to adjust and adapt to different events and surroundings is known as variability. Variability is required to increase the product's flexibility and potential for reuse. Researchers have so far examined variation in the software product line, but it is increasingly necessary in software systems as well. In every stage of software engineering, variation is a planned action that is carried out utilizing variant features and variation points. It is now a crucial characteristic of many systems. This study reviews the literature on software system variability. The research investigates variability managing process that is implemented in each phase of software development life cycle. Variability is classified into eight dimensions that describe its types and mechanism. This classification provides an integrated approach of variability in a unifying manner in software systems. From the survey, the implementing techniques on various system software such as service-based systems, web-based systems, and adaptation systems and the effect of variability in each system have been determined. © 2023 IEEE.

关键词： Web services

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GDPR-Compliant Personal Health Record Sharing Mechanism with Redactable Blockchain and Revocable IPFS

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IEEE Transactions on Dependable and Secure Computing 2024年第4期21卷 3342-3356页

作者： Yeh, Lo-Yao Hsu, Wan-Hsin Shen, Chih-Ya National Central University Department of Information Management Taoyuan320 Taiwan National Yang Ming Chiao Tung University Institute of Computer Science and Engineering Hsinchu30010 Taiwan Amber Group Web3 Security Team Singapore National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan

The use of IoT technology in collecting personal health records (PHR) within the eHealth environment is a growing trend. However, data integrity is a concern as cloud service providers (CSPs) often cannot guarantee it. Blockchain technology offers a solution to guarantee data integrity and traceability. However, the immutability of traditional blockchain conflicts with GDPR's requirements. To address scalability and privacy concerns, we have designed a comprehensive scheme that integrates the redactable blockchain with the existing revocable IPFS mechanism. Our scheme overcomes the disadvantage of residual downloading information in the traditional blockchain. Additionally, we have developed an enhanced proxy re-encryption scheme that simplifies access control for physicians without the need for complex group key management. Unlike traditional blockchains and P2P file sharing systems, our PHR platform allows for selective removal of records and files while maintaining auditable logs. Evaluation results demonstrate that our proposed scheme effectively enhances the exclusive revocation feature with acceptable overheads. To the best of our knowledge, this is the first work to provide the merit of fully complete record and file revocation on a blockchain-based system. © 2023 IEEE.

关键词： Hash functions

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Mitigating Racial Bias in Facial Expression Recognition Using Enhanced Data Diversification Techniques 15

Mitigating Racial Bias in Facial Expression Recognition Usin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sidana, Sagar Thakur, Sujata Negi Kumar, Sandeep Gomathi, R.D. Sinha, Anurag Saini, Arshroop Singh Raj, Petheru Dixit, Karan Kumar, Vikas Al-Khayyat, Ahmed Department of computer science and engineering Maharshi Dayanand University Haryana Rohtak India Department of computer science and engineering Graphic Era Hill University India Department of computer science and engineering Aryan Institute of Technology India Department of English Kongu Engineering College Perundurai Tamil Nadu Erode India School of Computing and information Science IGNOU India New Delhi India Secondary education Emerald Heights School India Reliance Jio Platforms Ltd Bangalore India Department of computer science and engineering Kiet Group of Institutions India Department of computer science and engineering Islamic University Iraq

ISBN: (纸本)9798350370249

With countless new methods and further advancements in computer vision, applying the technology to the everyday lives of people has become inevitable. One such use of computer vision models is recognizing facial patterns and expressions. However, as recent studies have indicated, many models interpreting facial expressions have developed biases towards certain races. This is likely a result of skewed training data;the model may have been trained with more images of a certain race than others. Overall, this can cause inaccuracy when the model is presented with people from different backgrounds. To promote a diverse group of data that the model can process, I present a way to balance biases. First, I fine-tuned a hybrid facial recognition model of state-of-the-art convolutional neural network (CNN) models called DeepFace on the emotion recognition dataset FER2013 to recognize the emotions on people’s faces. Next, using FairFace, which is modeled after the large-scale CNN ResNet-34, I extrapolated the races and facial structures for each type of individual, which I then used to determine the bias and skew within the dataset. I did this by comparing the model’s accuracy when applied to different races. Then, I used a user-instructed stable diffusion model called InstructPix2Pix to modify the images and create a more well-represented and diverse dataset. With this method, I was able to balance the dataset effectively and reduce bias in the image recognition model. However, due to issues with the FER2013 dataset images, many of the transformed images were disfigured, meaning the emotion data within the images was lost. This caused the overall accuracy of the model to drop significantly. Future research can evaluate this method using InstructPix2Pix on different computer vision tasks or with different datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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Visual Analytics to Support Treatment Decisions in Late-Stage Melanoma Patients

Visual Analytics to Support Treatment Decisions in Late-Stag...

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2023 Eurographics Workshop on Visual Computing for Biology and Medicine, EG VCBM 2023

作者： Pereira, C. Niemann, U. Braun, A. Mengoni, M. Tüting, T. Preim, B. Meuschke, M. Department of Simulation and Graphics University of Magdeburg Germany Department of Computer Science University of Magdeburg Germany University Dermatology Clinic University Hospital Magdeburg Germany

ISBN: (纸本)9783038682165

We present a visual analytics system to support treatment decisions in late-stage Melanoma patients. With the aim of improving patient outcomes, personalized treatment decisions based on individual characteristics and medical histories are crucial. The research focuses on the design and development of a visual analytics system tailored specifically for tumor boards, where multidisciplinary teams collaborate to make informed decisions. By leveraging a comprehensive database containing treatment and tumor stage progression information from over 1100 patients, the system provides healthcare professionals with a holistic overview and facilitates the analysis of individual cases as well as comparisons between multiple patients. The distinction between tumor board preparation systems and systems used during discussions is emphasized to ensure user-centric design and usability. Through the use of visual analytics techniques, complex relationships between treatment outcomes, temporal features, and patient-specific factors are explored, enabling clinicians to identify patterns and trends that may impact treatment decisions. The findings of this research contribute to the growing field of visual analytics in healthcare and have the potential to enhance treatment decision-making and patient care in late-stage cancer scenarios. © 2023 The Authors.

关键词： Tumors

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Fog Obscurity Mitigation 11th

Fog Obscurity Mitigation

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Agarwal, Shivam Gupta, Abhishek Kumar Singh, Anand Tyagi, Shivangi Department of Computer Science and Information Technology KIET Group of Institutions Uttar Pradesh Ghaziabad India Department of Electronics and Communications KIET Group of Institutions Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819717231

Fog is a natural phenomenon that affects visibility majorly at high altitudes and low temperatures. The greater the distance to the viewer, the less the ability to see clearly. As per Federal Highway Administration, over 16,300 people get injured due to accidents during foggy environments. The proposed system can help in providing better visibility on foggy days using YOLOv4 through transmission maps. It can also help in issuing challan tickets during foggy days on highways as it is a tedious task for traffic authorities during foggy days due to restricted visibility and atmospheric turbulence. The proposed system can also be helpful in areas where the temperature is below freezing point and also on narrow mountain roads as a correct analysis of the route is essential for smooth movement on the road. It can also help in recognizing any license plate number in a parking lot or when exceeding the speed limit on a highway or country road so that the relevant authorities can be informed timely to prevent accidents. Experimental results showcase that our suggested system provides finer visibility than traditional systems further enhancing the ease of use and versatility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Visibility

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A review on analysis and prediction methodologies of crop yield pattern 5th

A review on analysis and prediction methodologies of crop yi...

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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Neha Ahlawat, Anil Chaudhary, Himanshu Department of Computer Science Engineering KIET Group of Institutions Delhi NCR Ghaziabad India Department of Electronics and Communication Engineering KIET Group of Institutions Delhi NCR Ghaziabad India

ISBN: (纸本)9781032642987

Making supportive decisions for crop production, such as crop name recommendations and forecasts for crop production, requires machine learning implementation. Numerous machine learning classifiers and algorithms are employed in this. Climatic factors (temperature, rainfall, precipitation, sun radiation, and humidity), edaphic factors (pH, kind, and proportions of nitrogen, phosphorus, and potassium in the soil), and physiographic factors (altitude, steepness of slope, exposure to light, and wind) are some of the variables or factors that influence crop production. However, the most common variables are temperature, soil pH, precipitation, and humidity, and the most common algorithms are Examples of artificial neural networks and deep learning algorithms include Random Forest, Support Vector Machine, Linear Regression, Long Short-Term Memory, Convolution Neural Network, and Deep Neural Network. As part of this study, we reviewed the literature to ascertain the techniques and influencing factors that have been used in agricultural output prediction. © 2024 The Author(s).

关键词： Support vector machines

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