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International Conference on New Trends in Computing sciences (ICTCS)

作者： Katrina Sundus Ala'M. Al-Zoubi Bassam Hammo Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of CIS King Abdullah II School for Information Technology The University of Jordan Amman Jordan

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Breast cancer recurrence prediction is crucial for patient care, necessitating advanced methodologies to enhance precision and facilitate therapeutic decision-making. This paper evaluates how oversampling techniques affect breast cancer recurrence prediction on the base classifiers. Our study is based on the Jordan Breast Cancer dataset (JBRCA), which contains 21 features and 9723 instances. The JBRCA is a breast cancer dataset extracted from the King Hussein Cancer Center's (KHCC) registry database. By illustration, the study describes the imbalance problem facing most real datasets. This imbalance can lead to low model performance. Over-sampling techniques can help balance the data when there are fewer recurrence cases than non-recurrence. The proposed methodology includes data preparation and involves oversampling techniques for model development. We used base classification models such as logistic regression, decision tree, k-nearest neighbors, Gaussian Naive Bayes, and multilayer perceptron, combining them with oversampling techniques SMOTE, Random, ADASYN, SVMSMOTE, and Borderline-SMOTE to evaluate the compiled dataset. We examine the impact of the base classifiers along with oversampling techniques using accuracy, sensitivity, specificity, G-mean, and ROC-AUC to assess the model's efficacy.

关键词： Logistic regression Sensitivity Accuracy Medical services Nearest neighbor methods Multilayer perceptrons Market research Feature extraction Breast cancer data models

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

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Evaluating the Impact of dataset Size on Univariate Prediction Techniques for Moroccan Agriculture

Evaluating the Impact of Dataset Size on Univariate Predicti...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Ed-daoudi, Rachid Alaoui, Altaf Zerouaoui, Jad Ettaki, Badia Zerouaoui, Jamal Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

Learning models used for prediction are mostly developed without taking into account the size of datasets that can produce models of high accuracy and better performance. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size depends on the circumstances and context of prediction. This means that what makes a dataset to be considered as being big or small is controversial. In this paper, the ability of the predictive model to adapt to a particular size of data in training is examined. The study experiments on three different sizes of Moroccan agricultural data using a variety of statistical and Machine Learning techniques, to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model. The output of each model is measured using the Mean Absolute Error (MAE) and r-squared, and comparisons are made. The results of training the models through the three partitioned dataset show that, the models trained with the smallest and largest size of training data appear to be less accurate, while the models trained with a medium sized dataset delivers a much better results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Utilization of Machine Learning Algorithms to Monitor the Growth Path of Fishes in Marine Aquaculture 15th

Utilization of Machine Learning Algorithms to Monitor the Gr...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Ajibade, Samuel-Soma M. Essah, Richard Tapales, Catherine P. Antecristo, Charmaine P. Cababat, Feliciana G. Oyebode, Oluwadare Joshua Akintoye, Kayode A. Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer and Information Sciences Sunway University Selangor Malaysia Department of Computer Science and Engineering Chandigarh University Punjab India College of Arts and Sciences Cebu Technological University Cebu Moalboal Philippines Cebu Technological University Cebu Moalboal Philippines College of Education Cebu Technological University Cebu Moalboal Philippines Department of Civil and Environmental Engineering ABUAD Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria

ISBN: (纸本)9783031810855

Under a realistic cellular metabolic economic model of Nile tilapia, this work investigates Q-learning fish growth trajectory analysis. we offer two Q-learning data mining algorithms the best management policy using simulated data of optimum growth projections starting at the early growth stage and ending at appropriate market share. The early or primitive Q-learning technique learns optimum feed supplying regulatory regime for fish cultivation rate for the fished present the cages, whereas other one continuously adjusts appropriate nutrition controls the law for agricultural fish rate of increase in tanks together within ideal temperature pattern. The cumulative percentage weight of fishes through both vessels on land and afloat cages was tracked with relative dynamic model errors of 1.7% and 6.6%, respectively, using Q-learning control rules. Furthermore, contrasted to drifting cages where its water temp is controlled at 29.7 ℃, the eating and heat management policies minimise the proportional rate of feeding along with wastage of food in by 11%. data, algorithms, and performance of DL approaches used in adaptive aquaculture farms are also examined. In a nutshell, our goal is to give scholars and clinicians a greater grasp of the latest advancements of deep learning in aquaculture, which will help them develop smart aquacultural applications. With improved machine - learning technologies, a video trying to track biological early warning has made significant progress. Artificial intelligence has helped researchers better understand behavioural reactions to pharmacological and environmental stress. We discuss pioneer efforts in exact monitoring of a gathering of individuals in 2d/3d space in this paper, which introduces the fundamentals of activity recognition. Video tracking's professional and operational difficulties are discussed. The toxicological study based on fish behavioral data is then summarized. © The Author(s), under exclusive license to Springer Nature

关键词： Deep learning

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Assessing the Improvements Brought by Artificial Intelligence on the Prediction of Aerodynamic Coefficients

Assessing the Improvements Brought by Artificial Intelligenc...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Zerouaoui, Jad Alaoui, Altaf Ettaki, Badia Chakir, ElMahjoub Laboratory of Material and Subatomic Physics Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

The implementation of Artificial Intelligence in the aerospace field is fairly new and various domains of the aerospace discipline are to be explored. This paper provides a general review on the upbringing and improvements of Artificial Intelligence as a pilar of the next generation of scientific research. Applications and developments brought by Artificial Intelligence and Deep Learning to the aerospace industry generally, mainly the prediction of aerodynamic coefficients and design optimization using intricate neural networks replicating the process of the human brain. Scientific developments participated in improving major areas in the aerospace field. Due to the expensive and time-consuming process related to the complexity of the geometry and the precision of the results required, researchers integrated surrogate models based on Deep Learning in order to improve the calculation process of the aerodynamic coefficients. On one hand, this paper introduces and discusses the general architecture of several networks used to enhance the calculation ability of the machine, while presenting the process of each work along with recounting the different methods employed. On the other hand, a discussion regarding the challenges presented with utilizing Computational Fluid Dynamics while assessing and comparing the results brought by surrogate models based on efficiency and time consumption is also tackled. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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MatchNAS: Optimizing Edge AI in Sparse-Label data Contexts via Automating Deep Neural Network Porting for Mobile Deployment 24

MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts v...

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33rd ACM Web Conference, WWW 2024

作者： Huang, Hongtao Chang, Xiaojun Hu, Wen Yao, Lina School of Computer Science and Engineering University of New South Wales KensingtonNSW2052 Australia Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia CSIRO's Data61 University of New South Wales EveleighNSW2015 Australia

ISBN: (纸本)9798400701719

Recent years have seen the explosion of edge intelligence with powerful Deep Neural Networks (DNNs). One popular scheme is training DNNs on powerful cloud servers and subsequently porting them to mobile devices after being lightweight. Conventional approaches manually specialized DNNs for various edge platforms and retrain them with real-world data. However, as the number of platforms increases, these approaches become labour-intensive and computationally prohibitive. Additionally, real-world data tends to be sparse-label, further increasing the difficulty of lightweight models. In this paper, we propose MatchNAS, a novel scheme for porting DNNs to mobile devices. Specifically, we simultaneously optimise a large network family using both labelled and unlabelled data and then automatically search for tailored networks for different hardware platforms. MatchNAS acts as an intermediary that bridges the gap between cloud-based DNNs and edge-based DNNs. © 2024 ACM.

关键词： Network architecture

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Enhanced accuracy of deep learning method for fruit images classification

Enhanced accuracy of deep learning method for fruit images c...

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Information Technology (ACIT)

作者： Mohammad Arabiat Suhaila Abuowaida Nawaf Alshdaifat Ahmad Aburomman Esraa Al Henawi Mohmmad Dmour Ali Elrashidi Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Al al-Bayt University Mafraq Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

In the last few years, artificial intelligence has shown its importance and ability in computer vision due to its high level of accuracy and wide acceptance. The main goal of the research is to look into a better way to separate different types of fruits. Although deep learning is used, the standard classification methods for different types of fruit don't work very well. The main steps of the recommended better multiple fruit classification method are: 1) Making changes to the filter's size. Selecting the most suitable number of repeats is part of the process of improving the ResNet-101 block. 3) It is proposed that there be one more convolutional layer. Exists support for the suggested method after collecting and labeling 27,000 pictures as part of an actual study. The dataset with many fruits is split into two parts: 80% is used for training and 20% is used for testing. These pictures were used to train an application based on deep learning (DL) to help successfully determine multiple fruit objects in images. Then and then possible to compare the results of this method to the most current methods in the area of object division. The application being studied at does better than expected in terms of accuracy AP in a range of levels. The values always go farther than the equivalent measures of three widely used methods. Compared to the other methods, the suggested one takes less time to train. There are higher numbers for the thresholds than there are for the state of the art method's thresholds

关键词： Deep learning Training Image segmentation Accuracy Feature extraction Robustness Labeling Information technology Standards Testing

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A Trace-Selection Preprocessing Approach of Discovering Structurally Complete ICN-Process Models from Process Logs 3rd

A Trace-Selection Preprocessing Approach of Discovering Str...

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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Pham, Dinh-Lam Batzaya, Tsagaantsooj Kim, Kyoung-Sook Chang, Jeong-Hyun Kim, Kwanghoon Pio CCSRI Kyonggi University Gyeonggi Suwon16227 Korea Republic of Department of Public Safety Bigdata Graduate School of Kyonggi University Gyeonggi Suwon16227 Korea Republic of Data Process Engineering Research Laboratory Faculty of AI Computer Science and Engineering Kyonggi University Gyeonggi Suwon16227 Korea Republic of

ISBN: (纸本)9783031809422

In business process life-cycle management and reengineering through process mining, it is crucial for the process mining system to discover structurally safe and complete business process models from process logs. However, most process mining systems typically suffer from discovering inaccurate and unstructured business process models due to various types of anomalous traces hidden in process logs. In this paper, we therefore propose a process log preprocessing approach that purifies a corresponding process log dataset by selecting a group of reasonable process log traces and applying to discover an essential business process model satisfying the structural completeness requirement of matched-pairing and proper-nesting properties. The theoretical basis of the proposed approach is the mathematical graph model of information control net model that is the typical representation of business process models. Finally, we practically demonstrate the conceptual excellence and functional correctness of the proposed preprocessing approach through experimental verifications using real business process enactment log datasets available in the 4TU Center for Research data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reengineering

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Classification of Acute Intracerebral Hemorrhage Using Radiomics on Brain Computed Tomography Images

Classification of Acute Intracerebral Hemorrhage Using Radio...

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International Conference on Information Technology and Electrical Engineering (ICITEE)

作者： Phattanun Thabarsa Salita Angkurawaranon Chakri Madla Withawat Vuthiwong Kittisak Unsrisong Papangkorn Inkeaw Master's Degree Program in Data Science Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Radiology Faculty of Medicine Chiang Mai University Chiang Mai Thailand Department of Computer Science Data Science Research Center Faculty of Science Chiang Mai University Chiang Mai Thailand

Acute intracerebral hemorrhage (ICH) entity accounts for 10 to 15% of all strokes and is associated with a higher mortality rate ischemic stroke or subarachnoid hemorrhage. Causes of ICH are divided into primary, and secondary, including vascular malformation and tumorous. Primary ICH accounts for approximately 80% of all ICH cases. Vascular anomalies rank as the second most common cause of spontaneous ICH overall. Furthermore, hemorrhage resulting from brain tumors can occur in up to 10% of all primary or metastatic tumors. Early recognizing of these three causes of bleeding is critical for clinicians in precise diagnosis, effective treatment management, and helps avoid delayed diagnosis. We proposed a radiomics approach for classifying multiple causes of acute ICH as vascular malformation, tumorous, and primary-related hematoma. Non-contrast brain computed tomography with clinical features was used as input. The regions of both hematoma and perihematomal edema were delineated by using manual segmentation approach. Four feature selection methods were adopted. Also, three classification models were investigated in this study. The results showed that using the features selected by F-value applied with SVM classifier outperformed the other models, achieving weighted average accuracy (± SD) of 0.84 (± 0.07). Additionally, the model demonstrated average sensitivity and positive predictive value of 0.84 (± 0.06) and 0.86 (± 0.05), respectively. We also evaluate the overall performance of discriminating each class from the rest using AUC. The result suggested that our proposed model achieved the weighted average AUC of 0.90. Our proposed method highlights the potential in identifying multiple causes of acute and nontraumatic ICH, which has not been previously explored.

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arXiv

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arXiv 2023年

作者： Su, Jian-Nan Gan, Min Chen, Guang-Yong Guo, Wenzhong Philip Chen, C.L. The College of Computer and Data Science Fuzhou University Fuzhou350108 China The School of Computer Science and Engineering South China University of Technology Guangzhou510641 China The Faculty of Science and Technology University of Macau China

Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually used the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to the NLA with randomly selected regions stimulated our interest to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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