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Journal articles on the topic 'Efficient Net'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Zamani, Pouya. "Statistical properties of proportional residual energy intake as a new measure of energetic efficiency." Journal of Dairy Research 84, no. 3 (2017): 248–53. http://dx.doi.org/10.1017/s0022029917000395.

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Traditional ratio measures of efficiency, including feed conversion ratio (FCR), gross milk efficiency (GME), gross energy efficiency (GEE) and net energy efficiency (NEE) may have some statistical problems including high correlations with milk yield. Residual energy intake (REI) or residual feed intake (RFI) is another criterion, proposed to overcome the problems attributed to the traditional ratio criteria, but it does not account for production or intake levels. For example, the same REI value could be considerable for low producing and negligible for high producing cows. The aim of this st

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Nahar, Surendra, and Sartaj Sahni. "Time and space efficient net extractor." Computer-Aided Design 20, no. 1 (1988): 17–26. http://dx.doi.org/10.1016/0010-4485(88)90137-6.

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Sabrin, F., M. A. Uddin, and A. K. F. Haque. "Applicability of Vaisburd and Evdokimov Model to Ionic Targets." Journal of Scientific Research 4, no. 2 (2012): 307. http://dx.doi.org/10.3329/jsr.v4i2.8704.

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The Vaisburd and Evdokimov proposed an empirical model to calculate the electron impact single ionization cross-sections of atoms and molecules. The model has been applied to some atoms and molecules. To examine the efficiency of the model, the present work applies the model to calculate cross-sections for Ne-isonuclear series Ne+, Ne2+, Ne3+, Ne4+, Ne5+, Ne6+, Ne7+, Ne8+, Ne9+ . The separate sets of values of the parameters of the model are determined by comparison with the available experimental data using a non-linear least-squares fitting computer code. Keywords: Electron impact ionization

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Xie, Liuyue, Tinglin Duan, and Kenji Shimada. "SAGA-net." ACM SIGAPP Applied Computing Review 22, no. 2 (2022): 21–33. http://dx.doi.org/10.1145/3558053.3558055.

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In this paper, we propose a real-time shape-assisted graph attention neural network to perform local pointcloud repairment. The orderless pointclouds require an effective shape encoder to distill local and global geometric feature descriptors. Previous work has attempted to convert pointcloud representation into a voxelized shape or perform grid-transformations. While these approaches can subsequently allow common convolution operations on the structured data, they either pose additional computational cost or disrupt the local geometric information. We present SAGA-Net, an efficient graph atte

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Kundur, N. C., and P. B. Mallikarjuna. "Deep Convolutional Neural Network Architecture for Plant Seedling Classification." Engineering, Technology & Applied Science Research 12, no. 6 (2022): 9464–70. http://dx.doi.org/10.48084/etasr.5282.

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Weed control is essential in agriculture since weeds reduce yields, increase production cost, impede harvesting, and degrade product quality. As a result, it is indeed critical to recognize weeds early in their vegetation cycle to evade negative impacts to crop growth. Earlier traditional methods used machine learning to determine crops along with weed species, but they had issues with weed detection efficiency at early growth stages. The current work proposes the implementation of a deep learning method that provides accurate results for precise weed recognition. Two different deep convolutio

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Deng, Yunjiao, Yulei Hou, Jiangtao Yan, and Daxing Zeng. "ELU-Net: An Efficient and Lightweight U-Net for Medical Image Segmentation." IEEE Access 10 (2022): 35932–41. http://dx.doi.org/10.1109/access.2022.3163711.

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Shu, Xin, Feng Chang, Xin Zhang, Changbin Shao, and Xibei Yang. "ECAU-Net: Efficient channel attention U-Net for fetal ultrasound cerebellum segmentation." Biomedical Signal Processing and Control 75 (May 2022): 103528. http://dx.doi.org/10.1016/j.bspc.2022.103528.

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Nayak, Dillip Ranjan, Neelamadhab Padhy, Pradeep Kumar Mallick, Mikhail Zymbler, and Sachin Kumar. "Brain Tumor Classification Using Dense Efficient-Net." Axioms 11, no. 1 (2022): 34. http://dx.doi.org/10.3390/axioms11010034.

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Brain tumors are most common in children and the elderly. It is a serious form of cancer caused by uncontrollable brain cell growth inside the skull. Tumor cells are notoriously difficult to classify due to their heterogeneity. Convolutional neural networks (CNNs) are the most widely used machine learning algorithm for visual learning and brain tumor recognition. This study proposed a CNN-based dense EfficientNet using min-max normalization to classify 3260 T1-weighted contrast-enhanced brain magnetic resonance images into four categories (glioma, meningioma, pituitary, and no tumor). The deve

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Felix, Jose Manuel, and Francisco Ortin. "Efficient Aspect Weaver for the .Net Platform." IEEE Latin America Transactions 13, no. 5 (2015): 1534–41. http://dx.doi.org/10.1109/tla.2015.7112012.

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Vishwa, Chandra Jajula, Maddala Akshay, Venkat Narayana Kotha Veera, Kumar Medabalimi Sasi, and Anurag P. Nancy. "Lung Cancer Detection using Efficient Net - B0." International Journal for Modern Trends in Science and Technology 11, no. 04 (2025): 373–76. https://doi.org/10.5281/zenodo.15168353.

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<em>Lung cancer remains a leading cause of cancer-related deaths worldwide, with early detection being crucial for improving survival rates. Traditional diagnostic approaches rely on manual assessment by radiologists, which can be time-consuming and prone to human error. This paper presents an AI-powered Lung Cancer Detection System utilizing deep learning models, specifically EfficientNet-B0, to classify lung CT scans into different stages. The system is designed with an interactive user interface built using Streamlit and performs model inference using PyTorch. Advanced image preprocessing t

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Turyahabwe, Remigio, Caroline Mulinya, Andrew Mulabbi, and Moses Olowo. "A Comparison of the Efficiency of Aquatic Macroinvertebrate Sampling Tools Used in Lotic Environmental Impact Assessment of Human Activities in A Tropical Mountain Stream in Eastern Uganda." Ghana Journal of Geography 14, no. 2 (2022): 1–23. http://dx.doi.org/10.4314/gjg.v14i2.1.

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The study was aimed at comparing the efficiency of three macroinvertebrate sampling tools used in lotic environmental impact assessment of River Sipi including Surber sampler, rock-filled basket and Kick net sampling tools. The efficiency of the sampling tools was based on the data collected by each sampling tool, which was in turn used to calculate the Relative variation (RV) (efficiency), diversity, richness, and relative abundance, time taken to sort macroinvertebrate per sample and taxa assemblage. Data was analysed using a two-way ANOVA that was performed under the R Development Core Team

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12

Cenger, Hatice. "Performance and Benchmarking: Automotive Industry Example." MANAS Sosyal Araştırmalar Dergisi 14, no. 3 (2025): 1047–56. https://doi.org/10.33206/mjss.1638816.

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This study aims to comparatively analyze the financial performances of firms in the automotive sector using Data Envelopment Analysis (DEA), distinguishing between firms with effective debt management and those without. It seeks to propose recommendations for less efficient firms to improve their efficiency by benchmarking them with more efficient counterparts. In this context, the relative financial performance of automotive sector companies traded in Istanbul Stock Exchange (ISE) in 2022 was measured with a mathematical programming-based Data Envelopment Analysis (DEA). As a result of the st

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Lu, Chuanhua, Hideaki Uchiyama, Diego Thomas, Atsushi Shimada, and Rin-ichiro Taniguchi. "Sparse Cost Volume for Efficient Stereo Matching." Remote Sensing 10, no. 11 (2018): 1844. http://dx.doi.org/10.3390/rs10111844.

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Stereo matching has been solved as a supervised learning task with convolutional neural network (CNN). However, CNN based approaches basically require huge memory use. In addition, it is still challenging to find correct correspondences between images at ill-posed dim and sensor noise regions. To solve these problems, we propose Sparse Cost Volume Net (SCV-Net) achieving high accuracy, low memory cost and fast computation. The idea of the cost volume for stereo matching was initially proposed in GC-Net. In our work, by making the cost volume compact and proposing an efficient similarity evalua

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Gargano, Luisa, Andrzej Pelc, Stéphane Pérennes, and Ugo Vaccaro. "Efficient communication in unknown networks." Networks 38, no. 1 (2001): 39–49. http://dx.doi.org/10.1002/net.1022.

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15

Hansen, Pierre, Jacques-Françols Thisse, and Richard E. Wendell. "Efficient points on a network." Networks 16, no. 4 (1986): 357–68. http://dx.doi.org/10.1002/net.3230160403.

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Shahejafar, Shaikh. "Performance Optimization Techniques in .NET Applications." International Journal of Advance and Applied Research S6, no. 23 (2025): 70–77. https://doi.org/10.5281/zenodo.15119205.

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<em>Performance optimization is a crucial aspect of .NET application development, ensuring efficient resource utilization and enhanced user experience. This paper explores various techniques to optimize .NET applications, including memory management strategies, garbage collection tuning, multithreading, asynchronous programming, and code efficiency improvements.</em> <em>Additionally, database and caching optimizations are discussed to enhance application responsiveness and scalability. By leveraging these best practices, developers can minimize performance bottlenecks and create high-performi

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Yin, Hao, Yi Wang, Jing Wen, et al. "DFBU-Net: Double-branch flat bottom U-Net for efficient medical image segmentation." Biomedical Signal Processing and Control 90 (April 2024): 105818. http://dx.doi.org/10.1016/j.bspc.2023.105818.

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Aly, Mohammed, and Abdullah Shawan Alotaibi. "EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net." Computers, Materials & Continua 77, no. 1 (2023): 557–82. http://dx.doi.org/10.32604/cmc.2023.042493.

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Manber, Udi, and Lawrence McVoy. "Efficient storage of nonadaptive routing tables." Networks 18, no. 4 (1988): 263–72. http://dx.doi.org/10.1002/net.3230180402.

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Gusfield, Dan. "Efficient algorithms for inferring evolutionary trees." Networks 21, no. 1 (1991): 19–28. http://dx.doi.org/10.1002/net.3230210104.

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Gusfield, Dan, and Dalit Naor. "Efficient algorithms for generalized cut-trees." Networks 21, no. 5 (1991): 505–20. http://dx.doi.org/10.1002/net.3230210503.

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Akbari, Nasrin, and Amirali Baniasadi. "EDGE-Net: Efficient Deep-Learning Gradients Extraction Network." International Journal of Artificial Intelligence & Applications 14, no. 2 (2023): 85–99. http://dx.doi.org/10.5121/ijaia.2023.14207.

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Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in edge detection tasks, but their large number of parameters often leads to high memory and energy costs for implementation on lightweight devices. In this paper, we propose a new architecture, called Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advantages of Depthwise Separable Convolutions and deformable convolutional networks (DeformableConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing network pruning techniques, our proposed

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Sallenave, Olivier, and Roland Ducournau. "Efficient Compilation of .NET Programs for Embedded Systems." Journal of Object Technology 11, no. 3 (2012): 5:1. http://dx.doi.org/10.5381/jot.2012.11.3.a5.

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Bütterling, Patrick, Bert Benders, and Lutz Eckstein. "Efficient 48-V Drivetrain and Power Net Architectures." MTZ worldwide 77, no. 9 (2016): 48–53. http://dx.doi.org/10.1007/s38313-016-0085-3.

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Chen, S., D. F. Sang, and C. S. Peng. "Virtual Net: An Efficient Simulation for Parallel Computation." International Journal of Modelling and Simulation 27, no. 2 (2007): 125–30. http://dx.doi.org/10.1080/02286203.2007.11442408.

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Prasanthi, N. R. S. L., G. V. Jyotsna, L. Lokeswar Rao, G. S. Varshini, B. Tejeswar, and D. K. S. Kartheek. "EFFICIENT U-NET FOR INSIGHTFUL LUNG CANCER DIAGNOSIS." Journal of Nonlinear Analysis and Optimization 15, no. 01 (2024): 1733–40. http://dx.doi.org/10.36893/jnao.2024.v15101.1733-1740.

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Lung cancer detection often relies on interpreting subtle nodules in CT scans, a task demanding precise segmentation tools beyond simple image classification models. While existing methods utilizing other architectures might achieve decent accuracy, they often struggle with limited CT scan datasets and scalability, hindering their real-world impact. Our paper addresses the imperative need for enhanced lung cancer detection by integrating the Efficient U-Net architecture, which is implemented to achieve better results on image classification tasks while using low computational resources, it mea

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Ms. Saranya K, Santhiya A, Sheebha Shanthini R, Sheshapriya N, and Varsha V. "Optimizing Wheat Rust Disease Detection with Efficient Net." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1846–50. https://doi.org/10.47392/irjaeh.2025.0267.

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Wheat rust is one of the most destructive crop diseases, significantly impacting global wheat production. Traditional methods of disease detection are often time-consuming, labor-intensive, and lack the precision required for early intervention. This paper proposes a deep learning-based approach for optimizing wheat rust disease detection using the EfficientNetV2 model. EfficientNetV2 is a powerful convolutional neural network architecture known for its improved accuracy, faster training times, and computational efficiency. The model is trained on a large dataset of wheat leaf images to learn

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Jothiaruna, N., Bandla Pavan Babu, Nagoor Basha Shaik, P. Arun Mozhi Devan, and Kishore Bingi. "Cardiovascular Classification Using Efficient Net on Electrocardiogram Images." Engineering Journal 28, no. 12 (2024): 67–78. https://doi.org/10.4186/ej.2024.28.12.67.

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P. Mohan Kumar, Ramavath Laxmi Bhargavi, Shaik Imran, and Mrs. J. Sowmya. "Rice Plant Disease Detection Using Efficient Net V2." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 01 (2025): 31–51. https://doi.org/10.47392/irjaeh.2025.0006.

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Rice is a staple crop feeding billions worldwide, yet its production is severely impacted by plant diseases, leading to significant economic losses and food insecurity. This project proposes an advanced Rice Plant Disease Detection System leveraging EfficientNetV2, a state-of the-art deep learning architecture, to achieve high accuracy in identifying and classifying rice diseases. The system incorporates geo-specific tagging during image acquisition, enabling location-based disease mapping and tailored crop recommendations. Key features include real-time disease detection, severity analysis, a

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Liu, Heng, Chuhua Jiang, Junhua Chen, Hao Li, and Yongqi Chen. "Research Advances in Marine Aquaculture Net-Cleaning Robots." Sensors 24, no. 23 (2024): 7555. http://dx.doi.org/10.3390/s24237555.

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In the realm of marine aquaculture, the netting of cages frequently accumulates marine fouling, which impedes water circulation and poses safety hazards. Traditional manual cleaning methods are marked by inefficiency, high labor demands, substantial costs, and considerable environmental degradation. This paper initially presents the current utilization of net-cleaning robots in the cleaning, underwater inspection, and monitoring of aquaculture cages, highlighting their benefits in enhancing operational efficiency and minimizing costs. Subsequently, it reviews key technologies such as underwate

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Buntin, G. David, and David J. Isenhour. "COMPARISON OF SWEEP-NET AND STEM-COUNT TECHNIQUES FOR SAMPLING PEA APHIDS IN ALFALFA." Journal of Entomological Science 24, no. 3 (1989): 344–47. http://dx.doi.org/10.18474/0749-8004-24.3.344.

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The accuracy, precision and efficiency of stem-count and sweep-net techniques were compared for sampling the pea aphid, Acyrthosiphon pisum (Harris), in alfalfa. Density estimates by both techniques were highly correlated (r = 0.87). Both techniques were similar in sample precision and efficiency, but stem counts provided more accurate density estimates than the sweep net technique. The stem count technique is an accurate and efficient alternative to the sweep net for sampling pea aphids in alfalfa.

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Zhang, Yan, Kefeng Li, Guangyuan Zhang, Zhenfang Zhu, and Peng Wang. "DFA-UNet: Efficient Railroad Image Segmentation." Applied Sciences 13, no. 1 (2023): 662. http://dx.doi.org/10.3390/app13010662.

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In computer vision technology, image segmentation is a significant technological advancement for the current problems of high-speed railroad image scene changes, low segmentation accuracy, and serious information loss. We propose a segmentation algorithm, DFA-UNet, based on an improved U-Net network architecture. The model uses the same encoder–decoder structure as U-Net. To be able to extract image features efficiently and further integrate the weights of each channel feature, we propose to embed the DFA attention module in the encoder part of the model for the adaptive adjustment of feature

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Iklima, Zendi, Trie Maya Kadarina, Ketty Siti Salamah, and Arrival Dwi Sentosa. "Real-time dental caries segmentation with an efficient Deformable U-Net (DU-Net) for teledentistry system." SINERGI 29, no. 2 (2025): 447. https://doi.org/10.22441/sinergi.2025.2.015.

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Digital technology has greatly improved teledentistry by facilitating telediagnostics and teleconsultations, particularly benefiting those in remote areas. Additionally, AI advancements enhance diagnostic accuracy and streamline clinical decision-making, reducing costs and resource disparities in dental care. This study presents an improved U-Net architecture, Deformable U-Net (DU-Net), for semantic dental caries segmentation, leveraging deformable convolutions to dynamically adjust sampling points for improved feature extraction and reduced computational redundancy. By connecting encoder-deco

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Chen, Lin, and Yaacov Yesha. "Efficient parallel algorithms for bipartite permutation graphs." Networks 23, no. 1 (1993): 29–39. http://dx.doi.org/10.1002/net.3230230105.

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Goldschmidt, Olivier, and Alexan Takvorian. "An efficient graph planarization two-phase heuristic." Networks 24, no. 2 (1994): 69–73. http://dx.doi.org/10.1002/net.3230240203.

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Katoh, Naoki, and Kazuo Iwano. "Efficient algorithms for minimum range cut problems." Networks 24, no. 7 (1994): 395–407. http://dx.doi.org/10.1002/net.3230240705.

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Banik, Debapriya, Kaushiki Roy, and Debotosh Bhattacharjee. "EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames." Nordic Machine Intelligence 1, no. 1 (2021): 14–16. http://dx.doi.org/10.5617/nmi.9122.

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This paper addresses the Instrument Segmentation Task, a subtask for the “MedAI: Transparency in Medical Image Segmentation” challenge. To accomplish the subtask, our team “Med_Seg_JU” has proposed a deep learning-based framework, namely “EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames”. The proposed framework is inspired by the M-Net architecture. In this architecture, we have incorporated the EfficientNet B3 module with U-Net as the backbone. Our proposed method obtained a JC of 0.8205, DSC of 0.8632, PRE of 0.8464, REC of 0.9005, F1 of 0.8632, and A

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Sun, Feng, Ajith Kumar V, Guanci Yang, Ansi Zhang, and Yiyun Zhang. "Circle-U-Net: An Efficient Architecture for Semantic Segmentation." Algorithms 14, no. 6 (2021): 159. http://dx.doi.org/10.3390/a14060159.

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State-of-the-art semantic segmentation methods rely too much on complicated deep networks and thus cannot train efficiently. This paper introduces a novel Circle-U-Net architecture that exceeds the original U-Net on several standards. The proposed model includes circle connect layers, which is the backbone of ResUNet-a architecture. The model possesses a contracting part with residual bottleneck and circle connect layers that capture context and expanding paths, with sampling layers and merging layers for a pixel-wise localization. The results of the experiment show that the proposed Circle-U-

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Dina, Mushlihatud, and Vima Tista Putriana. "Effectiveness and Efficiency Of Baznas With ACR and ISZM Method." Al-Kharaj : Jurnal Ekonomi, Keuangan & Bisnis Syariah 6, no. 3 (2023): 3399–412. http://dx.doi.org/10.47467/alkharaj.v6i3.5297.

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This study aims to compare the analysis of effectiveness and efficiency of BAZNAS Solok Regency and BAZNAS Solok City for the period 2018 - 2021 using the Allocation to Collection Ratio (ACR) and the International Standard of Zakat Management (ISZM) method. The analysis technique is carried out using ACR ratio analysis which consists of Gross Allocation to Collection Ratio, Gross Allocation to Collection Ratio Non-Amil, Net Allocation Ratio, and Net Allocation Ratio Non-Amil while ISZM ratio consists of program expense ratio, operational expense ratio, collection expense ratio, and collection

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Li, Jiangqi, and Xiang Li. "MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation." Applied Sciences 13, no. 8 (2023): 4842. http://dx.doi.org/10.3390/app13084842.

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In the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell nuclei analysis represent an important measure for cancer diagnosis. The most valuable aspect of the nuclei analysis is the segmentation of the different nuclei morphologies of different organs and subsequent diagnosis of the type and severity of the disease based on pathology. In recent years, deep learning techniques have been widely used in digital histopathology analysis. Automated nuclear segmentation technology enables the rapid and efficient segmentation of tens of thousands of complex and variab

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Turk, Fuat. "RNGU-NET: a novel efficient approach in Segmenting Tuberculosis using chest X-Ray images." PeerJ Computer Science 10 (February 5, 2024): e1780. http://dx.doi.org/10.7717/peerj-cs.1780.

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Tuberculosis affects various tissues, including the lungs, kidneys, and brain. According to the medical report published by the World Health Organization (WHO) in 2020, approximately ten million people have been infected with tuberculosis. U-NET, a preferred method for detecting tuberculosis-like cases, is a convolutional neural network developed for segmentation in biomedical image processing. The proposed RNGU-NET architecture is a new segmentation technique combining the ResNet, Non-Local Block, and Gate Attention Block architectures. In the RNGU-NET design, the encoder phase is strengthene

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Ahady, Shambalid, Nirendra Dev, and Anubha Mandal. "Toward Zero Energy: Active and passive design strategies to achieve net zero Energy Building." International Journal of Advance Research and Innovation 7, no. 1 (2019): 49–61. http://dx.doi.org/10.51976/ijari.711908.

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Buildingsare found to be an essential part of the needed transition towards energy sustainability. In the past few years, there have been growing interests in net zero energy buildings (NZEB) adapted worldwide. The minimized energy demand and airtightness of a passive house and the low energy buildings have provided in the past a step forward to the energy efficiency goal and the net-zero energy building. Implementation of proven energy efficiency technologies offers the world the fastest, most economical, and most environmentally benign way to alleviate threats. This paper will discuss Net Ze

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Afsana, Asharaf, Anirudh Anush, S. Kartha Gayathri, S. Ansia, and T. Ananthan. "Rice Leaf Disease Detection Using Efficient Net B5 Model." Recent Trends in Computer Graphics and Multimedia Technology 6, no. 1 (2023): 25–31. https://doi.org/10.5281/zenodo.10400602.

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<em>Plant diseases, particularly those impacting rice leaves, pose a critical challenge for farmers, impeding their ability to meet the escalating food demands of a growing population. The detrimental effects of rice diseases result in significant production and economic losses, profoundly affecting farmers' livelihoods. This distressing situation has unfortunately contributed to an alarming rise in farmer suicides, emphasizing the pressing need for targeted control strategies. To tackle this issue, a novel approach is proposed, leveraging an EfficientNet B5 model, a state-of-the-art convoluti

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Natschlger, T., W. Maass, and A. Zador. "Efficient temporal processing with biologically realistic dynamic synapses." Network: Computation in Neural Systems 12, no. 1 (2001): 75–87. http://dx.doi.org/10.1080/net.12.1.75.87.

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Even, Shimon. "Area efficient layouts of the Batcher sorting networks." Networks 38, no. 4 (2001): 199–208. http://dx.doi.org/10.1002/net.10003.

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Nakano, Shin-ichi, Ryuhei Uehara, and Takeaki Uno. "Efficient algorithms for a simple network design problem." Networks 62, no. 2 (2013): 95–104. http://dx.doi.org/10.1002/net.21500.

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Bauguion, Pierre-Olivier, Walid Ben-Ameur, and Eric Gourdin. "Efficient algorithms for the maximum concurrent flow problem." Networks 65, no. 1 (2014): 56–67. http://dx.doi.org/10.1002/net.21572.

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Maulina, Fera. "ANALISIS TINGKAT EFISIENSI PENGGUNAAN MODAL MELALUI PENDEKATAN DU PONT SYSTEM PADA PT SIANTAR TOP TBK." Jurnal Ekonomi Integra 11, no. 2 (2021): 137. http://dx.doi.org/10.51195/iga.v11i2.165.

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This study aims to describe and analyze the efficiency of the use of capital through the Du Pont approach. The data analysis that will be used is quantitative with the data source, namely secondary data obtained from the 2016-2019 financial statements of PT Siantar Top Tbk. The research variables used are: (1) Asset Turn Over, to show the company's ability to manage all assets to generate sales; (2) Net Profit Margin, to show how much net profit the company gets; (3) Return on Assets, to measure the rate of return of all existing assets; (4) Equity Multiplier, to describe how much equity is co

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Deng, Liang, Wenchun Bao, Yueqing Wang, et al. "Vortex-U-Net: An efficient and effective vortex detection approach based on U-Net structure." Applied Soft Computing 115 (January 2022): 108229. http://dx.doi.org/10.1016/j.asoc.2021.108229.

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Javed, Aymin, Nadeem Javaid, Nabil Alrajeh, and Muhammad Aslam. "DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction." Applied Sciences 14, no. 22 (2024): 10516. http://dx.doi.org/10.3390/app142210516.

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Abstract:

Cardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the health care industry is likely to transform significantly. To predict CVD, we constructed two models: Dense Belief Network (DB-Net) and Deep Vanilla Recurrent Network (DVR-Net). Proximity Weighted Random Affine Shadow sampling balancing technique is used for balancing the highly imbalanced Heart Disease Health Indicator dataset. SHapley Additive exPlan

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