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International Conference on Knowledge and Systems engineering (KSE)

作者： Van Hoan Do Stefan Canzar Center for Applied Mathematics and Informatics Le Quy Don Technical University Hanoi Vietnam Department of Computer Science and Engineering Huck Institutes of the Life Sciences The Pennsylvania State University University Park PA United States

Emerging single-cell technologies profile different modalities of data in the same cell, providing opportunities to study cellular population and cell development at a res-olution that was previously inaccessible. The first and most fundamental step in analyzing single-cell multimodal data is the identification of the cell types in the data using clustering analysis and classification. However, combining different data modalities for the classification task in multimodal data remains a computational challenge. We propose an approach for identifying cell types in multimodal omics data via joint dimensionality reduction. We first introduce a general framework that extends loss based dimensionality reduction methods such as nonnegative matrix factorization and UMAP to multimodal omics data. Our approach can learn the relative contribution of each modality to a concise representation of cellular identity that enhances discriminative features and decreases the effect of noisy features. The precise representation of the multimodal data in a low dimensional space improves the predictivity of classification methods. In our experiments using both synthetic and real data, we show that our framework produces unified embeddings that agree with known cell types and allows the predictive algorithms to annotate the cell types more accurately than state-of-the-art classification methods.

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Implicit Neural Representation in Medical Imaging: A Comparative Survey

Implicit Neural Representation in Medical Imaging: A Compara...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof School of Computer Engineering Iran University of Science and Technology Iran School of Electrical Engineering Iran University of Science and Technology Iran Department of Electrical Engineering and Computer Science South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), International Conference on

作者： Abhivyakti Yadav Divya S I R Oviya Kalpana Raja Department of Computer Science and Engineering (Artificial Intelligence) Amrita School of Computing Amrita Vishwa Vidyapetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization.

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Human Factors Analysis for Interactive Communication Systems Design in Higher Learning Institutions

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SSRN 2022年

作者： Mwombeki, Anthony Shidende, Nima Mselle, Leornard Department of Information Systems and Technology College of Informatics and Virtual Education University of Dodoma UDOM Dodoma Tanzania United Republic of Department of Computer Science and Engineering College of Informatics and Virtual Education University of Dodoma UDOM Dodoma Tanzania United Republic of

Over recent years, designers have encountered several challenges on how to capture and analyze human factors needed for effective design of interactive systems. In most cases, much effort is kept on functional requirements fulfilment with human factors aspects being given limited consideration. This study noticed persisting communication challenges in Tanzania's higher learning institutions. Lack of suited human factors frameworks was found to complicate the effective design of interactive communication systems. Authors qualitatively analyzed human factors needed in interactive communication systems design process. Based on that analysis, and following a thorough user centred design process, a four phase design science research methodology was employed to develop a human factors framework for interactive communication systems’ design in higher learning institutions. © 2022, The Authors. All rights reserved.

关键词： Human computer interaction

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Implementation of Knowledge based Collaborative Filtering and Machine Learning for E-Commerce Recommendation System 3

Implementation of Knowledge based Collaborative Filtering an...

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3rd International Conference on Computational and Experimental Methods in Mechanical engineering, ICCEMME 2021

作者： Singh, Mahesh Kumar Rishi, Om Prakash Singh, Akhilesh Kumar Singh, Pushpendra Choudhary, Pushpa Department of Information Technology G L Bajaj Institute of Technology and Management UP Greater Noida India Department of Computer Science and Informatics University of Kota Rajasthan Kota India Department of Information Technology Indraprastha Engineering College UP Ghaziabad India

This is the era of I-way. The development of high-speed computing and huge storage devices change the working culture of human. It affects the traditional business processes and shifted towards online business. It creates huge problems like overload and irrelevant information which are the causes of confusion both customers as well as enterprise. Recommendation system solves these problems. Design and development of efficient system is one of the key areas of the recent researchers. Collaborative filtering (CF) and content-based filtering algorithms are widely used in the implementation of such system. Collaborative used user's features while content-based used item's features. Most of the CFS are rating or review based processed homogeneous information. In this paper we proposed knowledge-based collaborative filtering algorithm for large data set that uses various activities done by users during interaction of item through E-commerce web site like clicks, select and purchase. The performance of the system is compared with the base models using real time Amazon E-commerce dataset using precession, recall and NDCG evaluation parameters in various combinations of activities performed by users on items. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Collaborative filtering

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Analysis of the effectiveness of the optimization processes of trans active power system management with distributed multi-purpose energy resources

Analysis of the effectiveness of the optimization processes ...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Volodymyr Khilenko Halyna Bielokha Serhii Denysiuk Oleksii Stepanov Ivan Kotuliak Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia Power Supply Department National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Information Technologies National University of Life and Environmental Sciences of Ukr Article ACDSA Khilenko ID 414 09-02-2024aine Kyiv Ukraine

The article the efficiency of the Microgrid network when transitioning to a transactive power system that uses control algorithms called to optimize the distribution of power between sources of distributed generation and to minimize the time of decision-making about mode switching and at the corresponding change of the power system architecture. Tables and graphs of the conducted modeling are presented and the efficiency of this control-optimized system is calculated in comparison with the power system where uniform distribution of load power is used.

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Negatively-Associated Maximal Frequent Geneset Mining on DNA Methylation Profile

Negatively-Associated Maximal Frequent Geneset Mining on DNA...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Mallik, Saurav Rakshit, Souvik Maulik, Ujjwal Zhao, Zhongming The University of Texas Health Science Center at Houston Center for Precision Health School of Biomedical Informatics HoustonTX77030 United States Jadavpur University Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9781665401265

Association rule mining has been an important approach for feature and biomarker discovery in various omics data. One main challenge is that it generates a large number of itemsets. The effect of this shortcoming increases substantially in the case of negative association rule mining that is useful for detecting significant relationships between genes (items/features) in the form of either presence or absence in disease characterization. In this article, we propose a new algorithm, NegaMax (negatively-associated maximal frequent itemsets) for negative association itemset mining. Our method follows depth-first search rather than breadth-first search used in the other methods. It identifies a much fewer number of non-redundant itemsets than that by the existing methods. Thus, it saves elapsing time for itemset generation which potentially remove false positive intermediate results. We demonstrated NegaMax in a real-world DNA methylation dataset. The proposed method is highly beneficial from a medical perspective. © 2021 IEEE.

关键词： Methylation

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IoT-based fertilizer recommendation system

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AIP Conference Proceedings 2024年第1期2926卷

作者： Isbat Uzzin Nadhori Ahmad Syauqi Ahsan Arif Basofi Dwi Febriyanti Informatics and Computer Engineering Department Politeknik Elektronika Negri Surabaya Surabaya Indonesia

The main factor for plant growth is fertilization. Fertilization is one of the important processes in plant growth that aims to add nutrients that are completely absorbed by plants. But excessive fertilization can damage the soil structure as well as plants become vulnerable to pests and wasting costs. However, until now, many farmers still apply fertilizer without knowing for sure the nutrient content of the soil; Meanwhile, checking the nutritional content of plants uses chemical solutions and sensors which are relatively expensive, making it difficult for farmers to implement. Based on this problem, this research aims to propose a new approach in the accuracy of fertilization based on soil pH sensors., by utilizing Internet of Things technology, this research focuses on providing recommendations for IoT-based fertilizers using soil pH sensors, the results of the sensors will be correlated with nutrients contained in the soil. Prediction of soil nutrient content is obtained from the correlation between plant environmental conditions. With this system, farmers can monitor the soil density around the plant and get fertilization recommendations through smartphones, and will get notifications of dosage recommendations and types of fertilizers if the pH condition is beyond the normal limit that has been determined. Based on the trials that have been carried out, the system can run well.

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Comparison of Google Analytics with Similar Web for Statistical Analysis of Website Traffic 1st

Comparison of Google Analytics with Similar Web for Statisti...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Garg, Bindu Kasar, Manisha Kotecha, Ketan Rahmani, Mohammad Khalid Imam University College of Engineering Pune India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031860713

This study addresses the burgeoning demand for website data collection and analysis in business operations, emphasizing the pivotal role of web analytics in providing crucial insights into customer behaviour. Despite the prevalence of various website traffic analysing tools, there exists a notable lack of comprehensive testing on the precision and reliability of these services. The research compares four key analytics measures from Google Analytics (GA) with SimilarWeb (SW) using average monthly statistics over a one-year period for 50+ websites across different nations and business verticals. Employing various statistical tests, including paired t-tests, Shapiro-Wilk tests, and correlation analysis, the study reveals statistically significant variations in key metrics between GA and SW. Notably, SimilarWeb tends to provide lower estimates for the number of visits and unique visitors compared to Google Analytics. However, the findings underscore the potential complementarity of using both tools in cases where direct access to a site’s analytics is not feasible, particularly for competitive intelligence and benchmarking purposes. The study’s extensive statistical analysis covers unique visitors, total visits, bounce rates, and average session duration, offering valuable insights into the nuanced variations and potential sources of error. There are statistically significant differences between the two web traffic tools in terms of unique visitors, total visits, average session time & bounce rates. In comparison to Google Analytics, SimilarWeb figures were on average, 40% less for unique visitors, 50% greater for average session time, 25% greater for bounce rate, and 20% lesser for total visits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Error statistics

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Tensor-Based Multi-Modal Multi-Target Regression for Alzheimer’s Disease Prediction

Tensor-Based Multi-Modal Multi-Target Regression for Alzheim...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jun Yu Benjamin Zalatan Yong Chen Li Shen Lifang He Department of Computer Science and Engineering Lehigh University Bethlehem PA U.S.A. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia PA U.S.A.

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The assessment of Alzheimer’s Disease (AD) progression via the analysis of physical changes within the brain has attracted great interest from the fields of healthcare, computational medicine, and machine learning alike. Recent studies have demonstrated that using both multi-modal data and multiple AD assessment scores in a predictive model can better reflect pathological characteristics and enhance prediction performance. However, using such high-dimensional structure information to model inter-correlation between multiple targets remains a challenging task. In this paper, we propose a Tensor-based Multi-modal Multi-Target Regression (TMMTR) method for AD detection and prediction, which enables simultaneously modeling multilinear structure information as well as intrinsic inter-target correlations in a general learning framework. We also investigate the tensor-structured sparsity that supports the interpretability of our prediction. Experiments conducted on the ADNI dataset validate the superior performance of our method when compared to other state-of-the-art methods.

关键词： Pathology Correlation Tensors Computational modeling Medical services Machine learning Predictive models

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