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2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Bubashait, Mohamed Hewahi, Nabil Big Data Science and Analytics Master Program College of Science University of Bahrain Sakhier Bahrain University of Bahrain Department of Computer Science College of IT Sakhier Bahrain

ISBN: (纸本)9781665440325

Like air pollution, sound pollution has grown to be a major concern for city residents, designers, and developers. Detecting and recognizing sound types and sources in cities and suburban areas or any environment have become a necessity for the quality of life as well as security. In recent years, researchers have explored many models using Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) Neural Network, and different combinations of these techniques, which produced promising results when combined with spectrogram images, or its different variations, to classify urban sounds. This research uses the DNN model performance as a baseline to compare the CNN and LSTM models' performance for classifying urban sound using Mel scale cepstral analysis (MEL) spectrum images using an open-source library called Librosa for sound processing. Models' performance was evaluated using the UrbanSound8k dataset. The CNN model underperformed with an accuracy rate of 87.15% and f1 score of 85.63% compared to both the DNN base model and the LSTM model. In contrast, comparing the LSTM model with CNN, LSTM shows better accuracy performance on test data with 90.15%, and f1 score of 90.15%. © 2021 IEEE.

关键词： Long short-term memory

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Lung Cancer Diagnosis Based on Object Detection Technology 14th

Lung Cancer Diagnosis Based on Object Detection Technology

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14th International Conference on Frontier Computing, FC 2024

作者： Xu, Jia-Lang Wang, Pei-Yun Huang, Tzu-Hsuan Hsu, Ying-Lin Huang, Kuo-Yang Chen, Mu-Yen Artificial Intelligence Development Center Changhua Christian Hospital Changhua Taiwan Doctoral Program in Data Science and Industrial Analytics National Chung Hsing University Taichung Taiwan Department of Applied Mathematics and Institute of Statistics National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Department of Engineering Science National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9789819623945

With the rise of artificial intelligence, many people nowadays use artificial intelligence to help solve some problems in life, and the medical field is also with the rise of artificial intelligence, many people are slowly applying artificial intelligence to medical images to assist in decision-making, this study proposes a Yolov8 algorithm for object tracking of CT images of lung cancer, which can quickly tell the doctor where the cancer is and what the type of cancer is. The results of this study show that the Box_Loss, Cls_Loss, and Dfl_loss of the Yolov8 algorithm in detecting the location of lung cancer are 1.3689, 0.4552, and 1.5569 in the validation data, respectively, and thus this study suggests that this method can help physicians know the location of the tumor and what kind of tumor it belongs to. Therefore, this study suggests that this method can help physicians know the location of the tumor and the type of tumor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Predicting Oil and Gas Pipe Wall Thickness Using Machine Learning

Predicting Oil and Gas Pipe Wall Thickness Using Machine Lea...

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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Mohammed, Abdulrahim Hewahi, Nabil College of Science University of Bahrain Big Data Science and Analytics Program Sakhir Bahrain College of Information Technology University of Bahrain Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

Pipeline networks are crucial for process industries transportation and business operation. These vital elements, however, are highly exposed to material degradation due to corrosion that seriously impedes operational profitability, reliability, and safety. Industries usually monitor pipeline corrosion rate by gauging pipe wall thickness. Predicting pipe wall thickness helps industries to identify locations with high corrosion rate at an early stage and take necessary actions. The aim of this research is to predict pipe wall thickness at different locations using different machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), fully connected Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM). The data processed and analyzed in this study included six different process variables as well as pipe wall thickness at three different locations in a downstream oil and gas industry. The study findings demonstrated that LSTM with test Root Mean Squared Errors (RMSEs) of 0.019 mm, 0.015 mm, and 0.017 mm for the three selected locations, outperformed the other methods applied and successfully identified the pipe wall thickness pattern. © 2021 IEEE.

关键词： Long short-term memory

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Descriptive analytics Analysis as Support for Ancestral Domain Preservation: Empirical Evidence Generation as support for Decision Making

Descriptive Analytics Analysis as Support for Ancestral Doma...

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2023 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2023

作者： Castro, Eltimar T. Romano, Nephi P. Odchigue, Rev. F.R. Randy Jasper C. Young, Rev. F.R. John Christian U. Alfante, Jannina De Jesus Valencia, Rochelle A. Father Saturnino Urios University Data Science Analytics Center Butuan City Philippines Father Saturnino Urios University Office of VP Academic Affairs and Research Butuan City Philippines Father Saturnino Urios University Office of the President Dept. Butuan City Philippines Father Saturnino Urios University Arts and Science Program Butuan City Philippines

ISBN: (纸本)9798350321302

Ancestral domain refers to the lands, territories, and resources possessed and administered collectively by indigenous peoples, such as the indigenous communities of the Philippines. Indigenous peoples who have inhabited and cared for these regions for generations attach great cultural, spiritual, and economic significance to them. Indigenous Peoples Rights Act (IPRA) of 1997 acknowledged for the first time the idea of ancestral domain in the Philippines. However, the IPRA's implementation and the preservation of ancestral properties in the Philippines have encountered obstacles. There have been instances of unlawful logging, mining, and land appropriation in ancestral domains, typically with the participation of local government officials and influential persons. This has resulted in the uprooting of indigenous populations and the devastation of their land and resources. This research aimed to analyze the data in relation to tree planting activities initiated by several advocates, especially the Father Saturnino Urios University Foundation. Which aims, helping to preserve and saving Ancestral Land. This research employs data mining activity and develops insights according to generated patterns. Descriptive analytics is the branch of data analysis that involves summarizing and describing data. It typically involves using statistics, data visualization, and other techniques to identify patterns and trends in data. Descriptive analytics can be used to understand the characteristics of data and to communicate those characteristics to others. Further, the results show that tree planting activities conducted by some advocates resulted in good results, since planted trees near the watershed have had a survival rate of 92.28 (%) percent and around 43,270 seedlings planted in 88.05 hectares. On the other hand, abaca have been planted in the area for the livelihood of IP situated in the area, have a survival rate of 96 (%) percent. This result is the output of an ongoing reh

关键词： data visualization

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Characterization and Classification of Purity of Limestone in Madura Island for Industrial Application 1

Characterization and Classification of Purity of Limestone i...

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1st International Symposium on Physics and Applications, ISPA 2020

作者： Munawaroh, F. Muharrami, L.K. Triwikantoro Arifin, Z. Natural Science Education Study Program Universitas Trunojoyo Madura Bangkalan Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The characterization and classification of purity of limestone at Madura Island was investigated. Sampling was taken from nine quarries from different areas. The chemical analysis was carried out by X-ray fluorescence (XRF);the crystalline phase was characterized by X-ray diffraction (XRD). Analysis of purity classification based on XRF and XRD results according to British Geological Survey. Limestone has different purity, there are very high, medium, low and impure purity. The sample has a very high purity from Pamekasan (P.1) with CaO content of 99.06 wt% and 100% calcite. The samples are medium purity from Bangkalan (B.1, B.3), Pamekasan (P.2), Sumenep (S.1, and S.3) with CaO content of 91.61- 93.67 wt%. The samples are low purity from S.2 with CaO content 85.79 wt% and 100% dolomite. And the sample has impurities from P.3 and B.2 with CaO content 82.3 wt% and 84.7 wt%. Limestone with very high and medium purity which is processed into PCC can be applied in many industries as filler in rubber, paper making, plastic, paint, food, pharmaceutical, ceramic, adhesives, sealants, agriculture, and animal feed. © Published under licence by IOP Publishing Ltd.

关键词： Limestone

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An Enhanced BERTopic Framework and Algorithm for Improving Topic Coherence and Diversity

An Enhanced BERTopic Framework and Algorithm for Improving T...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Sahil Sawant Jinhong Yu Kirtikumar Pandya Chun-Kit Ngan Rolf Bardeli Data Science Program Worcester Polytechnic Institute Worcester MA USA Big Data & Consumer Analytics Vodafone GmbH Duesseldorf NRW Germany

In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even moderate dataset. More specifically, the contributions of this work are threefold: (1) integrate a dynamic and advanced optimizer into the existing BERTopic framework to learn the optimal number of dimensions of different document embeddings, (2) develop a k-means-based algorithm in the optimizer to support the dimension-embedding learning, and (3) conduct an extensive experimental study on three distinct types of datasets, including DBPedia, AG News, and Reuters, to evaluate the performance of our approach in terms of the topic quality (TQ) score computed by the topic coherence and the topic diversity. From the results, we can conclude that our enhanced, automated BERTopic framework with its dimension-embedding learning algorithm on documents outperforms the TQ score of the existing framework by 4.49% (before removing the stop words) and 16.52% (after removing the stop words) among all the four representable document-embedding approaches, including the BERTopic's Default Sentence Transformer, Google's Universal Sentence Encoder, OpenAI GPT-2, and our investigators' developed Context-aware Embedding Model, on all the three datasets.

关键词： Heuristic algorithms Pipelines Coherence Transformers Internet Context modeling

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Statistical Modeling: A New Regression Curve Approximation using Mixed Estimators Truncated Spline and Epanechnikov Kernel

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Engineering Letters 2023年第4期31卷 1649-1655页

作者： Sifriyani Dani, Andrea Tri Rian Fauziyah, Meirinda Budiantara, I. Nyoman Statistics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia Statistics Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

In the era of computation, researchers have paid significant attention to the nonparametric regression method. Nonparametric regression has the benefit of a high degree of modeling flexibility. Developing a mixed estimator truncated spline-Epanechnikov kernel is the most recent innovation in this study. The truncated spline estimator excels at handling data whose behavior varies at predetermined intervals. In contrast, the Epanechnikov kernel estimator has a more flexible structure and excels at modeling data that does not adhere to a particular pattern. Maximum Likelihood Estimation is utilized to estimate parameters. The concluding section of this study will discuss the estimator properties of the mixed estimators truncated spline and Epanechnikov kernel models. The proposed can be utilized for additional analysis in the field of nonparametric regression. © 2023, International Association of Engineers. All rights reserved.

关键词： Maximum likelihood estimation

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Pressure-dependent electronic properties in the rocksalt structure of lithium fluoride: A DFT Study 5

Pressure-dependent electronic properties in the rocksalt str...

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5th Padjadjaran International Physics Symposium, PIPS 2022

作者： Gunawan, A.F. Astuti, F. Physics Study Program Republic of Indonesia Defense University Kawasan Indonesia Peace and Security Center Sentul Bogor16810 Indonesia Department of Physics Faculty of Science Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia

Density functional theory (DFT) calculation are carried out for the cubic structure of lithium fluoride (LiF). Variation of applied pressure is considered, ranging from 0 to 120 GPa with an increment of 30 GPa based on PBE and SCAN functional scheme. In zero pressure and energy-cut off 100 Ry, best result on lattice parameter is obtained by using PBE functional with the value of 4.06 Å, while SCAN functional overestimates the lattice parameter at 4.15 Å. The insulating gap of both results is significantly underestimated with 8.82 eV and 9.26 eV for PBE and SCAN functionals respectively. With the applied hydrostatic pressure, the lattice parameter are reduced exponentially, while still retaining the insulating behaviour with an increased value of gap energy. © Published under licence by IOP Publishing Ltd.

关键词： Lattice constants

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Investigation of the hexagonal boron nitride (h-BN) electronic and structural parameters and its dependency on the known semi-local functionals 5

Investigation of the hexagonal boron nitride (h-BN) electron...

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5th Padjadjaran International Physics Symposium, PIPS 2022

作者： Tiana, W.R. Ramadhan, M.R. Astuti, F. Physics Study Program Republic of Indonesia Defense University Kawasan Indonesia Peace and Security Center Sentul Bogor16810 Indonesia Department of Physics Faculty of Science Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia

Known as the next wonder material after graphene, hexagonal form of Boron-Nitride (hBN) attracts a lot of interest due to its capabilities to be utilized in wide range of applications. Here we investigate bilayer structure of hBN using density functional theory (DFT) techniques. In pristine case, we observed that the SCAN functional gives a significantly better estimation of inter-layer distance compared to the PBE functional. Furthermore, we also modelled the hBN structure with a single oxygen molecule sandwiched in between of B-N layer, and conclude that the most probable sites for oxygen molecule to be trapped is at the exact center of the bilayer hBN structure. © Published under licence by IOP Publishing Ltd.

关键词： Boron nitride

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Exploring the Relation between Blood Tests and Covid-19 Using Machine Learning

Exploring the Relation between Blood Tests and Covid-19 Usin...

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2020 International Conference on data analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Almansoor, Mohamed Hewahi, Nabil M. University of Bahrain Big Data Science Analytics Program Bahrain University of Bahrain Department of Computer Science Bahrain

ISBN: (纸本)9781728196756

COVID-19 is a global pandemic that hit the world in 2019-2020 and caused massive losses. Every day, hundreds of thousands of tests are being done on possible infected cases. It usually takes several hours to get the results of virus test in advanced countries, whereas in other countries might take days. The aim of this study is to investigate whether normal blood medical tests help in detecting covid-19 using various machine learning approaches. If true, this would give an indication to people who should undergo the virus test. In this paper we independently use machine learning algorithms including support vector machines, adaptive boosting, random forest and k-nearest neighbors. These algorithms are then merged to form ensemble learning which leads to the classification. The results show that the ensemble learning is having the highest true positive rate of 30%. The obtained results show that normal blood tests do not help much in giving right indications about detecting COVID-19. © 2020 IEEE.

关键词： COVID-19

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