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Computer System, Information Technology, and Electrical engineering (COSITE), International Conference on

作者： Riza Dianil Iman Roslidar Roslidar Kahlil Muchtar Amalia Amalia Mohammad Syaryadhi Alfatirta Mufti Novi Maulina Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Doctorate Student of Doctoral Program in Medical Sciences Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an Fl-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance.

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A Graphical Model for Fusing Diverse Microbiome Data

arXiv

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arXiv 2022年

作者： Aktukmak, Mehmet Zhu, Haonan Chevrette, Marc G. Nepper, Julia Magesh, Shruthi Handelsman, Jo Hero, Alfred Department of Electrical and Computer Engineering University of Michigan United States Department of Plant Pathology University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Microbiology Doctoral Training Program University of Wisconsin-Madison United States

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high dimensional features, in this case transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each correspond to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. The inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.1 © 2022, CC BY.

关键词： Covariance matrix

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Correlation between meteorological condition and criteria air pollutants in a tropical city, Padang - Indonesia

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AIP Conference Proceedings 2024年第1期2891卷

作者： Putri Nilam Sari Faculty of Public Health Andalas University Padang Indonesia 25164 Environmental Engineering Doctoral Program Bandung Institute of Technology Bandung Indonesia 40116

Criteria Air Pollutants cause significant health problems that require special investigation. The meteorological condition becomes essential in influencing the concentration of pollutants in the atmosphere. This study aims to determine the correlation of the meteorological parameters with criteria air pollutants to reduce the worsening impact in the future, especially in tropical areas. This study measured the correlation of meteorological indicators (temperature, humidity, maximum wind speed, wind direction, rainfall, and sunshine) with air pollutants (CO, NO2, O3, PM10, and SO2). Cross-correlation analysis was used to find the best predictor lag of daily meteorological variables. Almost all pollutants are significantly correlated at lag-1 with sunlight and minimum temperature. On the other hand, wind direction at maximum speed and rainfall are significantly negatively correlated at most at lag 0 for all pollutants. It is necessary to investigate further the interaction between meteorological parameters and surface elevation variations to determine the potential for inversion at this location.

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MMA-Net: Multi-Modal Attention Network for 2-D Object Detection in Autonomous Driving

MMA-Net: Multi-Modal Attention Network for 2-D Object Detect...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abhilash Gaur Shubh Goel Kanishk Goel Seshan Srirangarajan Po-Hsuan Tseng Kai-Ten Feng IIT Delhi-NYCU Taiwan Joint Doctoral Program Bharti School of Telecommunication Technology and Management Indian Institute of Technology Delhi (IIT Delhi) New Delhi India International College of Semiconductor Technology National Yang Ming Chiao Tung University (NYCU) Hsinchu Taiwan Department of Electrical Engineering IIT Delhi New Delhi India Department of Electrical Engineering Bharti School of Telecommunication Technology and Management IIT Delhi New Delhi India Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electronics and Electrical Engineering NYCU Hsinchu Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multimodal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator.

关键词： Laser radar Fuses Multimodal sensors Radar detection Object detection Signal processing Cameras Speech processing Autonomous vehicles Meteorology

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Development of Support Vector Machine Method to Forecast Dengue-Endemic Areas

Development of Support Vector Machine Method to Forecast Den...

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

作者： Hetty Meileni Ermatita Abdiansah Nyayu Latifah Husni Desmawati Doctoral Program in Engineering Science Universitas Sriwijaya (Politeknik Negeri Sriwijaya) Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Artificial Intelligence Research Development (AIRD) Universitas Sriwijaya Palembang Indonesia Department of Electrical Engineering Politeknik Negeri Sriwijaya Palembang Palembang Indonesia Faculty of Health Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

The cases of dengue hemorrhagic fever (DHF) in Indonesia have increased significantly since 2020. Data shown by the Central Statistics Agency, for example, in South Sumatra Province, there were 6,348 cases of DHF during that period. To suppress the case number of DHF, forecasting areas that are susceptible to this disease is urgently needed. This research aims to develop a machine learning method Support Vector Machine (SVM). The data used include area data, population density data, weather data, and dengue fever case data. The development of the SVM model is carried out using a machine learning method that combines regional data, population density data, weather data, and dengue case data. In the end, using the SVM method can be used to forecast dengue-endemic areas, so that the government and society can take further action to reduce the negative impact of DHF.

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Modifying the Revised NIOSH Lifting Equation using Anthropometric Variables to Calculate Horizontal and Vertical Multipliers

Modifying the Revised NIOSH Lifting Equation using Anthropom...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Moehamad Adi Rochmat Sarifuddin Madenda Tri Handhika Ernastuti Ernastuti Department of Industrial Engineering Gunadarma University Depok Indonesia Doctoral Program in Information Technology Gunadarma University Depok Indonesia Centre for Computational Mathematics Studies Gunadarma University Depok Indonesia Faculty of Industrial Technology Gunadarma University Depok Indonesia

ISBN: (纸本)9781665453967

The Revised NIOSH Lifting Equation is a work system assessment tool which often used in industrial world. The calculation result in the form of Recommended Weight Limit (RWL) and the Lifting Index (LI) become the basis for improving the work system carried out. RNLE is an application developed by NIOSH and uses American anthropometry as a constant in its calculations. Some researchers, especially in Asia, consider that the application is less suitable for Asia people who have anthropometric differences with Americans. The assessment carried out by the RNLE application is based on work activities carried out without considering the worker's body posture. In particular, the calculate of HM and VM in this application uses a constant related to anthropometry. RNLE uses the constants based on the average anthropometry of workers in calculation. The constant anthropometry used in the equation led to the results of the risk calculation that does not correspond to physical aspects of workers. It takes an equation that consider anthropometry workers to get the calculation of the value of the appropriate work risk. The study aims to propose equations that consider anthropometry factors to calculate the risk factor. The development of the equation with Python programming language allows further development in the field of information and communication technology. The results of the study shows that the average results of the RWL and LI calculation uses anthropometry data not equal to the RNLE, thus required new equation in order to resulting more objective risk value.

关键词： Asia Anthropometry Mathematical models Information and communication technology Indexes Intelligent systems Cybernetics

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A Novel Hybrid Framework for Energy-Efficient Clustering and Routing in Heterogeneous IoT-Driven Wireless Sensor Networks

A Novel Hybrid Framework for Energy-Efficient Clustering and...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Gopal Kumar Gupta Arpit Jain Khemraj Sharma P. M. Ashok Kumar Shubhi Gupta Sheenu Agarwal Symbiosis Institute Of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Business Administration Doctoral Program in Management Sciences NANHUA UNIVERSITY Dalin Chiayi R.O.C Department of Computer Science and Engineering Amity University Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering Uttarakhand Technical University Dehradun U.K. India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Smart computing has been particularly notable in the development of wireless sensor networks (WSNs), which have many applications. Battery-powered, self-configuring sensor nodes are the basis of these networks. Energy and resources are limited when it comes to sensors. Unbalanced nodes in the network consume more power, which adversely affects the network’s lifespan. An improved energy-efficient cluster-based routing protocol for heterogeneous wireless sensor networks (WSNs) based on the Internet of Things is proposed in this study. Several performance metrics were used to evaluate the proposed model’s effectiveness, including energy efficiency, alive nodes, dead nodes, network lifetime, and residual energy. The results of the comparison were compared with existing methodologies, such as LEACH, PSO, and hybrid PSO. The proposed approach outperforms the existing model by a substantial margin in networks with 50 and 100 nodes, according to the simulation results.

关键词： Wireless communication Measurement Wireless sensor networks Simulation Heuristic algorithms Routing Energy efficiency Routing protocols Internet of Things Informatics

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Identification of climate change impact at Raknamo Dam, Kupang, Indonesia 5

Identification of climate change impact at Raknamo Dam, Kupa...

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5th International Conference on Climate Change 2020, ICCC 2020

作者： Nait, C. Bisri, M. Soetopo, W. Prayogo, T. Limantara, L.M. Krisnayanti, D.S. Doctoral Program in Water Resources Engineering Department University of Brawijaya M.T. Haryono Street No. 167 Malang65145 Indonesia Department of Water Resources Engineering University of Brawijaya M.T. Haryono Street No. 167 Malang65145 Indonesia Departement of Civil Engineering Nusa Cendana University Adisucipto Street Penfui PO. BOX 104 Kupang Indonesia

Impacts of climate change have received extensive attention, especially for the semi-arid regions. Raknamo reservoir is located at Kupang Regency, a semi-arid area with significant differences in rainy duration (3-4 months) and dry seasons (8-9 months). This preliminary study aimed to identify the recent factors that caused the delays in initial filling of Raknamo reservoir, which may be affected by climate change. There are three rainfall stations observed surrounding Raknamo watershed, namely Camplong, Naibonat and Raknamo. Direct comparisons were made of rainfall data for 33 years (1986-2019) daily. The climatology station Lasiana was used for climatological data with a data length of 33 years (1986-2019). This research used a qualitative approach, runoff was analyzed using regression analysis. The results showed that factors such as temperature (T), potential evapotranspiration (ETo), and rainfall (R) affected the availability of initial filling in the Raknamo reservoir. The amount of rainfall intensity that is not achieved in one period to fill the reservoir has resulted in the Raknamo reservoir utilization not being optimized. © Published under licence by IOP Publishing Ltd.

关键词： Reservoirs (water)

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Container Detection System Using CNN Based Object Detectors 7

Container Detection System Using CNN Based Object Detectors

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7th International Conference on Instrumentation, Control, and Automation, ICA 2021

作者： Bandong, Steven Nazaruddin, Yul Yunazwin Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung Indonesia National Center for Sustainable Transportation Technology CRCS Instrumentation and Control Research Group Department of Engineering Physics Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision NLP and Big Data Analytics Institut Teknologi Bandung Instrumentation and Control Research Group Department of Engineering Physics Bandung Indonesia

ISBN: (纸本)9781665432955

The increase in free trade will also amplify the exchange of goods between countries and islands, especially in the seaports. The manual operation of the gantry-crane at the seaports has a risk due to human negligence. For that reason, automation is strongly needed in the loading and unloading of the container. Camera-based object detection is implemented to achieve this goal. In the recent years, CNN gives better accuracy in term of image classification, recognition and detection compared to the traditional method that use handcrafted features. In this paper, several advanced detection methods using CNN-based object detection, namely MobileNet, ResNet, and Faster RCNN are compared to detect and track the movement of containers. The results of the experiment show that the SSD ResNet and Faster RCNN methods are superior in terms of detection accuracy based on the Precision, Recall, Average Precision, and IoU values. MobileNet v3 excels in speed detection compared to the other methods. © 2021 IEEE.

关键词： Containers

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Utilization of Linguistic Data for Learner Assessment on e-Learning: Instrument and Processing

Utilization of Linguistic Data for Learner Assessment on e-L...

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International Conference on Informatics and Computing (ICIC)

作者： Wenty Dwi Yuniarti Sri Hartati Sigit Priyanta Herman Dwi Surjono Doctoral Program of Computer Science Universitas Gadjah Mada Yogyakarta Indonesia Universitas Islam Negeri Walisongo Semarang Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Informatics Engineering Education Faculty of Engineering Universitas Negeri Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9798350345728

The increasingly massive use of e-Learning illustrates the speed and need for innovation in learning. According to the National Higher Education Standards (SN-Dikti), constructive alignment is required between learning outcomes, processes, and assessments to properly implement learning in e-Learning. Assessment is an essential component of e-Learning. Unfortunately, the problem of assessment in e-Learning is still found. One of them is the limitations of e-Learning in accommodating and processing various assessment data, primarily linguistic. Even though the variety of assessment data, both numerical, linguistic, and a combination of the two, supports a comprehensive assessment. On the other hand, the accommodation of linguistic data raises problems regarding how to process of unifying linguistic data is carried out. Research related to linguistic data using computing with words has been carried out, but it still needs more precise results from the unification of the linguistic data. Therefore, this study proposes providing an assessment instrument to accommodate linguistic data in e-Learning, as well as showing how to process of unifying linguistic data is carried out using 2-Tuple Fuzzy Linguistic. This approach can avoid the loss of assessment information by presenting more informative and precise results in a 2-tuple (s, α) where $s$ indicates the ability level, and α shows a comparison of abilities with other learners and the potential of the learner to achieve higher abilities. This proposal has the potential to be applied in a learner assessment system for higher education e-Learning.

关键词： Technological innovation Electronic learning Instruments Education Linguistics Proposals Informatics

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