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Identification of Sounds from Construction Sites: A Machine Learning Approach 4th

Identification of Sounds from Construction Sites: A Machin...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Maji, Sayantan Mukherjee, Himadri Dhar, Ankita Marciano, Matteo Roy, Kaushik Department of Computer Science and Engineering Sister Nivedita University Kolkata India TISA Lab Department of Computer Science West Bengal State University Kolkata India Gazelien Records Lab Department of Arts and Humanities Music Program New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9789819757022

The rate at which the world is evolving is astonishing with cutting-edge technologies being introduced every day. There have been developments in every field ranging from constructing gigantic architectures to enhancing the ease and comfort with which we can commute daily. Nevertheless, this has given rise to different types of pollution, sound pollution being one of them. Although the impact of sound pollution is not instantaneously noticed, it has a distinct effect on humans and nature which is observed over a period of time. There are multiple sources that cause sound pollution. Sounds from ongoing construction sites are one of them. Such sounds disrupt the daily lifestyle of people, their health, and daily productivity. Mostly, these sounds start very early in the day thereby making it difficult for people to manually take precautions like shutting doors and windows or deploying mobile soundproofing solutions. Machine learning has advanced significantly over the years leading to the refinement of artificial intelligence. This has aided in the development of multifarious solutions to ease and simplify our daily lifestyle. This sets the stage for an autonomous system, that can detect sounds from construction sites very early to automatically deploy precautionary measures so that the effect of these sounds on close by residents can be minimized. In this paper, a system is presented that models audio clips using MFCC-based features for the identification of sounds from construction sites in the Urban environment. Experiments were performed on a dataset of over 6K clips composed of the varied construction sounds as well as ambient urban sounds and a maximum accuracy of 98.02% was achieved using random forest-based classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Impact of Multimedia Sensor Placement to Car Licence Number Detection 2022

Impact of Multimedia Sensor Placement to Car Licence Number ...

引用

11th International Conference on Software and computer Applications, ICSCA 2022

作者： Amelia, Afritha Zarlis, Muhammad Suherman, Suherman Efendi, Syahril Graduate Program of Computer Science Faculty of Computer Science and Information Technology University of Sumatera Utara Indonesia Computer Science Faculty of Computer Science and Information Technology University of Sumatera Utara Indonesia Electrical Engineering Faculty of Engineering University of Sumatera Utara Indonesia

ISBN: (纸本)9781450385770

A vehicle has a license plate and can be identified by the license number attached to the vehicle plate. For detection and recognition, multimedia sensors can be used so that it requires a vehicle tracking application that has a vehicle monitoring function and has implications for various fields. One of them is a multimedia sensor that requires an optimal design and method in the form of the number of multimedia sensors to be used, the placement of the multimedia sensor positions and solutions in the event of a system failure. To achieve multimedia sensor performance, research on multimedia sensors is carried out using a camera type with Phase Detection Auto Focus (PDAF) technology which is similar to a pair of right and left eyes to see an object. The objects detected were 2 cars, where the car passed the camera sensor at a speed of 10-15 km/hour 20 times. Changes in the distance of the vehicle to the camera affect the disparity value and the object's accuracy value, namely and the object's Accuracy value of the minimum disparity value of 6.20×106 of 16.88×109. © 2022 ACM.

关键词： Disparity Vehicle plate Phase Detection Auto Focus (PDAF) Tracking application Object accuracy

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Utilizing Semi-supervised Method in Predicting BRCA1 Pathogenicity Variants 8

Utilizing Semi-supervised Method in Predicting BRCA1 Pathoge...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Trinugroho, Joko Pebrianto Nirwantono, Rudi Sudigyo, Digdo Pardamean, Bens Department of Mathematics School of Computer Science Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Quantifying the effect of mutations in the BRCA1 gene is useful for understanding their clinical consequences on breast cancer. Machine learning models can be applied to predict the landscape of protein variant effects that might not be always accessible by experiments. In this work, we propose a simple semi-supervised learning method using a Gaussian mixture model to predict ~90% of the unlabeled missense variants of the BRCA1 gene collected from the ClinVar database. High-quality embeddings are used as a feature of the protein sequences, extracted using the latest pre-trained transformer-based language protein model. A statistical test show that the protein embeddings are effective and robust for predicting pathogenicity. Further, the lower representations of the features are then fed into the semi-supervised model. The prediction performance of the model only for the labeled testing data achieves an AUC score and an accuracy of 79.27% and 71.58%, respectively. Using our defined pathogenic probability score, we find that ~94% of variants in our unlabeled dataset are well-separated into either benign or pathogenic classes according to that scoring. Our scores obtain a moderate Spearman rank correlation with the results of established unsupervised variant effect models. Finally, our approach can potentially be developed for more accurate and biologically reliable predictions of the variant effects. © 2023 The Authors. Published by Elsevier B.V.

关键词： embeddings language model mutation effect protein sequence semi-supervised learning

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An IoT-Driven Fuzzy Decision Model for Determining Room Comfort

An IoT-Driven Fuzzy Decision Model for Determining Room Comf...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Ditdit Nugeraha Utama John Reigton Hartono Johan Muliadi Kerta Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Bandung Indonesia

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

The use of the Internet of Things (IoT) has been widely adopted for a lot of purposes. Business process automation is one type of implementation that has been carried out. In addition, the data successfully captured by the IoT ecosystem can be utilized for various computer modeling needs, including the decision model construction in assessing room quality. The decision model itself is a computer model supporting the decision maker in decision making process. This research, via four simple research stages, aims to develop an IoT-based decision model to evaluate room quality based on two parameters: temperature and humidity. The design of the IoT environment and the implementation of temperature and humidity data readings in a room are used as input data for a decision model to assess the comfort level of the room. In this study, the primary method used to design the model is object-oriented, while fuzzy logic is employed to build the model. The developed decision model can simulate data captured from the designed IoT ecosystem, successfully assessing the comfort level of a room. From the simulation results using 18 data points (from 9 days of data capturing) captured by temperature and humidity sensors, the average comfort level of the room was found to be 19.7873.

关键词： Temperature sensors Fuzzy logic Temperature distribution Object oriented modeling Computational modeling Simulation Ecosystems Humidity Data models Internet of Things

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The Use of Time and Frequency Features in Finger Movements Based on Electromyogram Recording

The Use of Time and Frequency Features in Finger Movements B...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Atmaji, Catur Lelono, Danang Harjoko, Agus Dharmawan, Andi Universitas Gadjah Mada Doctoral Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9781665425773

Human movement is a direct result of brain commands to muscles. The muscle activity can be analyzed with an electromyograph device and produce a recording in the form of an electromyogram or EMG signal. There are two recording methods, namely intramuscular EMG and surface EMG or sEMG which are easier and more convenient to implement but have lower resolution. Analysis of the EMG record data will be better if the right method is chosen, one of which is the selection of feature extraction methods. This study performs basic finger motion classification with a limited number of electrodes, namely 4 electrodes. The purpose of this research is to compare the variation of features in the time or frequency domain that will be used for classification using artificial neural networks (ANN) and long short-term memory (LSTM). Data acquisition was carried out with the Ganglion Board device on 6 subjects aged 20-22 years who conducted experiments with two kinds of movements, namely opening and holding all fingers alternately. The results of this study indicate that the use of time domain features for classification with ANN produces better accuracy than LSTM. This can happen because the duration of the movement in this study is quite short, which is only for two seconds. The results of the use of the frequency domain feature show that the use of LSTM produces better accuracy, especially on the mean power and median frequency characteristics. © 2022 IEEE.

关键词： Frequency domain analysis

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The Classification Model of Learners Based on Diagnostic Assessment Results in E-learning With Machine Learning 1

The Classification Model of Learners Based on Diagnostic Ass...

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1st International Conference on Software Engineering and Information Technology, ICoSEIT 2022

作者： Yel, Mesra Betty Zarlis, Muhammad Suwilo, Saib Efendi, Syahril Sihombing, Poltak Universitas Sumatera Utara Graduate Program of Computer Science Department of Computer Science Medan Indonesia Universitas Binus Department of Computer Science Jakarta Indonesia Universitas Sumatera Utara Department of Mathematics Medan Indonesia

ISBN: (纸本)9781665473033

The development of technology and artificial intelligence, especially in the era of Industrial Revolution 4.0 almost covers all fields, one of which is e-learning. In its development, it is very important to determine the factors to produce the quality of e-learning. Machine learning can help to find various solutions in the real world, especially to solve problems for which a specific algorithm has not been found. The implementation of e-learning is often ineffective and not on target which has an impact on learning achievement that is not achieved, this is due to several things namely the learning model is applied in general, the learning materials and weights are applied equally to all users. Seeing these problems, it is necessary to have a model to classify students according to their abilities, talents, and interests before starting learning to achieve their goals to be achieved. This paper aims to create a machine learning approach model to determine the level of ability and needs of students before starting learning which can reduce disparities between students and follow up learning materials. With the machine learning based on learning model, e-learning can provide teaching materials according to the abilities, and interests of students, sustainably and improve the quality of students. © 2022 IEEE.

关键词： Students

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Supporting the Automated Generation of Acceptance Tests of Process-Aware Information Systems 19

Supporting the Automated Generation of Acceptance Tests of P...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Paiva, Tales M. Oliveira, Toacy C. Pillat, Raquel M. Alencar, Paulo S.C. Computer and Systems Engineering Program Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Software Engineering Program Universidade Federal do Pampa Alegrete Brazil Cheriton School of Computer Science University of Waterloo Waterloo Canada

ISBN: (纸本)9789897586729

Software quality assurance is a crucial process that ensures software products meet specified requirements and quality standards. Achieving an exhaustive test coverage is essential for quality assurance, particularly in complex and dynamic Process-Aware Information Systems (PAIS) built upon the Business Process Model and Notation (BPMN). Manual testing in such systems is challenging due to many execution paths, dependencies, and external interfaces. This paper proposes a model-based testing strategy that uses BPMN models and build specifications as input to generate a Robotic Process Automation (RPA) script that automates a comprehensive User Acceptance Test procedure. Leveraging on Robotic Process Automation (RPA) to automate user interactions allows for reducing the need for testers to manually input PAIS-related information when handling user forms. We also present a Case Study to demonstrate the feasibility of our approach. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Acceptance tests

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Detecting Bipolar Disorder based on Crowdsourced Symptoms using Machine Learning with Shapley Additive Explanations 9th

Detecting Bipolar Disorder based on Crowdsourced Symptoms us...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Martawidjaja, Marvel Lefrandt, Matthew Paramita, Ni Luh Putu Satyaning Pradnya Zhan, Rongfang Komalasari, Renata Kurniadi, Felix Indra Sihotang, Erna Fransisca Angela Anggreainy, Maria Susan Cahyono, Dwy Bagus Jati, Putu Hadi Purnama Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Department of Rehabilitation and Health Services University of North Texas Denton76203 United States College of Nursing Pennsylvania State University State College16802 United States Statistics Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11530 Indonesia Meta Lab Jakarta10310 Indonesia

Bipolar disorder is a mental health condition characterized by extreme mental states ranging from manic highs to depressive lows. Early intervention is crucial to prevent progression and complications of bipolar disorder, which can lead to significant loss. This study proposes a framework to detect bipolar disorder based on crowdsourced symptoms in the form of free texts. We extract the features by transforming these free-text symptoms into binary features using a set of natural language processing techniques. We build support vector machine models with different kernel functions and penalized logistic regression models with different penalty functions, where the best models have a precision of 0.7, recall of 0.78, F1 score of 0.74, and accuracy of 0.88. Moreover, the models are explainable since we incorporate a model-agnostic explanation method called Shapley additive explanations to understand the symptoms that mostly contributed to the prediction of bipolar disorder. The models presented in this study can be implemented in the initial screening process of bipolar disorder for further examination. © 2024 The Authors.

关键词： Adversarial machine learning

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Effect of Using Grid Search Method on The Performance of Machine Learning Models for Heart Disease Classification

Effect of Using Grid Search Method on The Performance of Mac...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Maria Susan Anggreainy Ayuliana Computer Science Department BINUS Graduiate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

To get accurate data prediction results, it requires not only the right machine learning model, but also hyperparameters. Hyperparameters play an important role in shaping and optimizing the performance of machine learning models. Determining the right combination of model and hyperparameters is often a challenge. This research will use a hyperparameter search method, namely grid search. The aim of this research is to find out optimization best results from hyperparameters for several machine learning models using the Grid Search method and see the performance impact of using the grid search method for heart disease classification. Heart disease is a disease that has the potential to be fatal for humans if not treated quickly. Early detection can be done by utilizing machine learning models that are able to analyze data patterns from studied data sets. By training machine learning to study data sets related to heart disease, it is hoped that it will be able to carry out early detection of heart disease.

关键词： Heart Training Search methods Computational modeling Machine learning Predictive models Data models Optimization Tuning Diseases

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Tracing Semantic Variation in Slang

Tracing Semantic Variation in Slang

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Sun, Zhewei Xu, Yang Department of Computer Science University of Toronto Toronto Canada Cognitive Science Program University of Toronto Toronto Canada

The meaning of a slang term can vary in different communities. However, slang semantic variation is not well understood and under-explored in the natural language processing of slang. One existing view argues that slang semantic variation is driven by culture-dependent communicative needs. An alternative view focuses on slang's social functions suggesting that the desire to foster semantic distinction may have led to the historical emergence of community-specific slang senses. We explore these theories using computational models and test them against historical slang dictionary entries, with a focus on characterizing regularity in the geographical variation of slang usages attested in the US and the UK over the past two centuries. We show that our models are able to predict the regional identity of emerging slang word meanings from historical slang records. We offer empirical evidence that both communicative need and semantic distinction play a role in the variation of slang meaning yet their relative importance fluctuates over the course of history. Our work offers an opportunity for incorporating historical cultural elements into the natural language processing of slang. © 2022 Association for Computational Linguistics.

关键词： Semantics

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