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Single and Mitochondrial Gene Inheritance Disorder Prediction Using machine learning

Computers, Materials & Continua 2022年第10期73卷 953-963页

作者： Muhammad Umar Nasir Muhammad Adnan Khan Muhammad Zubair Taher MGhazal Raed A.Said Hussam Al Hamadi Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13120GyeonggidoKorea Faculty of Computing Riphah International UniversityIslamabad45000Pakistan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia Canadian University Dubai00000UAE Cyber-Physical Systems Khalifa UniversityAbu Dhabi127788UAE

One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic ***,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic *** is a challenging process since it must be completed effectively and *** research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical *** the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic *** predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be *** predict genetic disease,deep and machine learning approaches are *** most productive tool model provides more precise *** simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation *** approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives.

关键词： Genetic disorder machine learning deep learning single gene inheritance gene disorder mitochondrial gene inheritance disorder

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GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping

arXiv

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arXiv 2025年

作者： Jose, Allen Isaac Pan, Sicong Zaenker, Tobias Menon, Rohit Houben, Sebastian Bennewitz, Maren Bonn-Rhein-Sieg University of Applied Sciences Germany Humanoid Robots Lab University of Bonn Germany Fraunhofer Institute for Intelligent Analysis and Information Systems Germany Humanoid Robots Lab University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Germany

Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their complex structures, which often result in fruit occlusions. Existing view planning methods attempt to reduce occlusions but either struggle to achieve adequate coverage or incur high robot motion costs. We introduce a global optimization approach for view motion planning that aims to minimize robot motion costs while maximizing fruit coverage. To this end, we leverage coverage constraints derived from the set covering problem (SCP) within a shortest Hamiltonian path problem (SHPP) formulation. While both SCP and SHPP are well-established, their tailored integration enables a unified framework that computes a global view path with minimized motion while ensuring full coverage of selected targets. Given the NP-hard nature of the problem, we employ a region-prior-based selection of coverage targets and a sparse graph structure to achieve effective optimization outcomes within a limited time. Experiments in simulation demonstrate that our method detects more fruits, enhances surface coverage, and achieves higher volume accuracy than the motion-efficient baseline with a moderate increase in motion cost, while significantly reducing motion costs compared to the coverage-focused baseline. Real-world experiments further confirm the practical applicability of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Fruits

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A.I. and Data-Driven Mobility at Volkswagen Financial Services AG

arXiv

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arXiv 2022年

作者： Jawed, Shayan Arif, Mofassir Ul Islam Rashed, Ahmed Madhusudhanan, Kiran Elsayed, Shereen Jameel, Mohsan Schmidt-Thieme, Lars Volk, Alexei Hintsches, Andre Kornfeld, Marlies Lange, Katrin Information Systems and Machine Learning Lab University of Hildesheim Germany Data Analytics & A.I Unit Volkswagen Financial Services AG Germany

machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research. Volkswagen Financial Services (VWFS), as a market leader in vehicle leasing services, aims to leverage existing proprietary data and the latest research to enhance existing and derive new business processes. The collaboration between information systems and machine learning lab (ISMLL) and VWFS serves to realize this goal. In this paper, we propose methods in the fields of recommender systems, object detection, and forecasting that enable data-driven decisions for the vehicle life-cycle at VWFS. © 2022, CC BY-NC-ND.

关键词： machine learning

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On the Potential of Using ERP Business and System Data for Fraud Detection

On the Potential of Using ERP Business and System Data for F...

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IEEE International Conference on Big Data

作者： Jonas Schnepf Paula Vetter Tarik Temel Bernd Scheuermann Lars Schmidt-Thieme Faculty of Management Science and Engineering University of Applied Sciences Karlsruhe Karlsruhe Germany Information Systems and Machine Learning Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (纸本)9781665480468

Enterprise Resource Planning (ERP) systems are used to support and to control the business processes of a company or organization. Such systems integrate the data across the entire company into a complete system that is capable of enhancing the key operations in virtually any department. Commonly, running an ERP system helps companies operate more efficiently, however, this also leads to problems. Employees are able to enrich themselves through insider knowledge or by exploiting incomplete or incorrect permission settings. This is referred to as occupational fraud. Since ERP systems keep records of all executed business activities and log system events and permission checks, they provide a variety of different data sources that can be used to detect occupational fraud. This paper reviews existing literature in the area of fraud detection and fraud cases based on ERP data including business data and system data. Using the most widespread ERP system from SAP, the potential and the suitability of the various data sources with respect to fraud detection is examined.

关键词： Data analysis Soft sensors Process control Companies Big Data Enterprise resource planning Fraud

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Computational Approach for Respiratory Pressure Parameters in Neonatal Ventilation 24

Computational Approach for Respiratory Pressure Parameters i...

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24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Lipchak, Daria Chupov, Aleksey Dolganov, Anton Zhdanov, Aleksei Jt. Stock Co. Prod. Association Urals Optical and Mechanical Plant Named after Mr. E. S. Yalamov Medical Devices R and D Bureau Yekaterinburg Russia Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University Named after the First President of Russia B.N. Yeltsin Yekaterinburg Russia University of Erlangen-Nuremberg Machine Learning and Data Analytics Lab Erlangen Germany

ISBN: (纸本)9798350336870

In the context of mechanical ventilation, the accurate calculation of secondary pressure parameters is essential for clinicians to effectively assess a patient's clinical condition. In the case of neonatal resuscitation with the T-piece, both the inspiratory and expiratory triggers are manually operated. The device provides a constant flow at the initiation of inspiration and reduces the flow to limit output pressure according to the set peak inspiratory pressure. However, without an algorithmic classification of the inspiration and expiration pressure, data analysis may only be performed postfactum. To address the need for the separate trend analysis of PIP and PEEP in non-invasive mechanical ventilation with a T-piece, there is a requirement to develop and implement an accurate algorithm for respiratory pressure parameters calculation. In this study, we review the current state-of-the-art methods for the calculation of respiratory pressure parameters for non-invasive mechanical ventilation with a T-piece. We then present the development and implementation of an algorithm for respiratory pressure parameters calculation and discuss the results of the analysis. This algorithm is designed to provide accurate and reliable measurements of PIP and PEEP in neonatal resuscitation with the T-piece, allowing clinicians to more effectively monitor and assess a patient's clinical condition during mechanical ventilation. The results demonstrate the effectiveness of the algorithm in accurately calculating PIP and PEEP values, providing valuable insights into the patient's respiratory mechanics and helping to inform clinical decision-making. © 2023 IEEE.

关键词： Resuscitation

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Phantom embeddings: Using embedding space for model regularization in deep neural networks

Phantom embeddings: Using embedding space for model regulari...

引用

2020 Conference "learning, Knowledge, Data, Analytics", LWDA 2020

作者： Ul Islam Arif, Mofassir Jameel, Mohsan Grabocka, Josif Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany Department for Computer Science Albert-Ludwigs-University Freiburg Germany

The strength of machine learning models stems from their ability to learn complex function approximations from data;however, this strength also makes training deep neural networks challenging. Notably, the complex models tend to memorize the training data, which results in poor regularization performance on test data. The regularization techniques such as L1, L2, dropout, etc. are proposed to reduce the overfitting effect;however, they bring in additional hyperparameters tuning complexity. These methods also fall short when the inter-class similarity is high due to the underlying data distribution, leading to a less accurate model. In this paper, we present a novel approach to regularize the models by leveraging the information-rich latent embeddings and their high intra-class correlation. We create phantom embeddings from a subset of homogenous samples and use these phantom embeddings to decrease the inter-class similarity of instances in their latent embedding space. The resulting models generalize better as a combination of their embedding, regularizes them without requiring an expensive hyperparameter search. We evaluate our method on two popular and challenging image classification datasets (CIFAR and FashionMNIST) and show how our approach outperforms the standard baselines while displaying better training behavior. © 2020 by the paper's authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep neural networks

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Content Based Automated File Organization Using machine learning Approaches

引用

Computers, Materials & Continua 2022年第10期73卷 1927-1942页

作者： Syed Ali Raza Sagheer Abbas Taher M.Ghazal Muhammad Adnan Khan Munir Ahmad Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797UAE Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120GyeonggidoKorea Cyber-Physical Systems Khalifa UniversityAbu Dhabi127788UAE

In the world of big data,it’s quite a task to organize different files based on their *** with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer *** of file management related tasks will be solved if the files on any operating system are somehow categorized according to their ***,the browsing process can be performed quickly and *** research aims to design a system to automatically organize files based on their similarities in terms of *** proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer *** results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user *** results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their *** proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows.

关键词： File organization natural language processing machine learning

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Offline Handwritten Amharic Character Recognition Using Few-shot learning

arXiv

引用

arXiv 2022年

作者： Samuel, Mesay Schmidt-Thieme, Lars Sharma, D.P. Sinamo, Abiot Bruck, Abey Arba Minch University Faculty of Computing and Software Engineering Ethiopia Information Systems and Machine Learning Lab Hildesheim31141 Germany Amuit Moefdre under Undp Germany Ministry of Innovation and Technology Ethiopia

Few-shot learning is an important, but challenging problem of machine learning aimed at learning from only fewer labeled training examples. It has become an active area of research due to deep learning requiring huge amounts of labeled dataset, which is not feasible in the real world. learning from a few examples is also an important attempt towards learning like humans. Few-shot learning has proven a very good promise in different areas of machine learning applications, particularly in image classification. As it is a recent technique, most researchers focus on understanding and solving the issues related to its concept by focusing only on common image datasets like Mini-ImageNet and Omniglot. Few-shot learning also opens an opportunity to address low resource languages like Amharic. In this study, offline handwritten Amharic character recognition using few-shot learning is addressed. Particularly, prototypical networks, the popular and simpler type of few-shot learning, is implemented as a baseline. Using the opportunities explored in the nature of Amharic alphabet having row-wise and column-wise similarities, a novel way of augmenting the training episodes is proposed. The experimental results show that the proposed method outperformed the baseline method. This study has implemented few-shot learning for Amharic characters for the first time. More importantly, the findings of the study open new ways of examining the influence of training episodes in few-shot learning, which is one of the important issues that needs exploration. The datasets used for this study are collected from native Amharic language writers using an Android App developed as a part of this study. © 2022, CC BY.

关键词： Character recognition

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Multi-script handwritten digit recognition using multi-task learning

arXiv

引用

arXiv 2021年

作者： Gondere, Mesay Samuel Schmidt-Thieme, Lars Sharma, Durga Prasad Scholz, Randolf Arba Minch University Faculty of Computing and Software Engineering Ethiopia Information Systems and Machine Learning Lab Hildesheim31141 Germany

Handwritten digit recognition is one of the extensively studied area in machine learning. Apart from the wider research on handwritten digit recognition on MNIST dataset, there are many other research works on various script recognition. However, it is not very common for multi-script digit recognition which encourage the development of robust and multipurpose systems. Additionally working on multi-script digit recognition enables multi-task learning, considering the script classification as a related task for instance. It is evident that multi-task learning improves model performance through inductive transfer using the information contained in related tasks. Therefore, in this study multi-script handwritten digit recognition using multi-task learning will be investigated. As a specific case of demonstrating the solution to the problem, Amharic handwritten character recognition will also be experimented. The handwritten digits of three scripts including Latin, Arabic and Kannada are studied to show that multi-task models with reformulation of the individual tasks have shown promising results. In this study a novel way of using the individual tasks predictions was proposed to help classification performance and regularize the different loss for the purpose of the main task. This finding has outperformed the baseline and the conventional multi-task learning models. More importantly, it avoided the need for weighting the different losses of the tasks, which is one of the challenges in multi-task learning. © 2021, CC BY.

关键词： learning systems

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Atribute-aware non-linear co-embeddings of graph features 13

Atribute-aware non-linear co-embeddings of graph features

引用

13th ACM Conference on Recommender systems, RecSys 2019

作者： Rashed, Ahmed Grabocka, Josif Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany

ISBN: (纸本)9781450362436

In very sparse recommender data sets, attributes of users such as age, gender and home location and attributes of items such as, in the case of movies, genre, release year, and director can improve the recommendation accuracy, especially for users and items that have few ratings. While most recommendation models can be extended to take attributes of users and items into account, their architectures usually become more complicated. While attributes for items are often easy to be provided, attributes for users are often scarce for reasons of privacy or simply because they are not relevant to the operational process at hand. In this paper, we address these two problems for attribute-aware recommender systems by proposing a simple model that co-embeds users and items into a joint latent space in a similar way as a vanilla matrix factorization, but with non-linear latent features construction that seamlessly can ingest user or item attributes or both (GraphRec). To address the second problem, scarce attributes, the proposed model treats the user-item relation as a bipartite graph and constructs generic user and item attributes via the Laplacian of the user-item co-occurrence graph that requires no further external side information but the mere rating matrix. In experiments on three recommender datasets, we show that GraphRec signifcantly outperforms existing state-of-the-art attribute-aware and content-aware recommender systems even without using any side information. © 2019 Copyright held by the owner/author(s).

关键词： Recommender systems

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