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Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method

Procedia computer science 2023年 216卷 136-143页

作者： Muhammad Titan Hafizal Dian Pratama Putra Herry Wirianata Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Faculty of Forestry STIPER Institute of Agriculture Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

As one of the largest exporters in the world, cocoa (Theobroma cacao L.) production in Indonesia provides an important contribution to the plantation sector that can, directly and indirectly, attribute to the national economic development. However, recent data shows a decline in cocoa productivity in Indonesia. This is arguably caused by various factors, including poor agricultural management and practices in cocoa plantations, that may lead to lower fertility in the soil and an increased risk of diseases and pests. Potassium deficiency is a major contributing factor to the low soil fertility that affects cocoa yields. Therefore, in this work, we implement an application with an expert system utilizing the forward chaining method to detect potassium deficiency in cocoa plants and then give a fertilization-based recommendation based on the plants’ condition. The system employs a set of rules to identify symptoms related to the deficiency on the sample photo of a cocoa leaf according to the channels of red, green, and blue of the image. The sample images of cocoa leaves are submitted to the application with an easy-to-use interface that can show the scanning result and proceed to display the suggested quantity of fertilizers to prevent potassium deficiency. Implementing the system can contribute constructive impacts to improve current practices in the overall cropping system of cocoa plants.

关键词： agriculture cocoa expert system forward chaining recommendation system

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3D Geometric Shape Assembly via Efficient Point Cloud Matching

arXiv

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arXiv 2024年

作者： Lee, Nahyuk Min, Juhong Lee, Junha Kim, Seungwook Lee, Kanghee Park, Jaesik Cho, Minsu Department of Computer Science and Engineering POSTECH Pohang Korea Republic of Graduate School of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and computation. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Enhancing the Performance of E-Mode AlGaN/GaN HEMTs With Recessed Gates Through Low-Damage Neutral Beam Etching and Post-Metallization Annealing

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 150-155页

作者： Chen, Yi-Ho Ohori, Daisuke Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan Tohoku University Institute of Fluid Science Sendai980-8577 Japan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Pioneer Semiconductor Innovation Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan

This study investigated the electrical properties of AlGaN/GaN high-electron-mobility transistors (HEMTs) with varied recess depths under the gate electrode. We demonstrated a recess depth of approximately 6 nm, which was achieved through neutral beam etching (NBE) technique with a low etch rate of 1.8 nm/min, resulting in device enhancement-mode (E-mode) behavior with threshold voltage (Vth) of 0.49 V. The effects of post-metallization annealing (PMA) on the device performance were also examined. The results revealed that PMA treatment improves the DC characteristics of the devices, including maximum drain current (IDMAX), transconductance (gm), subthreshold swing (SS), on-off ratio, and off-state leakage current, with maximum enhancement percentage of 18.3% for IDMAX, 3758% for on-off ratio, and 54.3% for SS. Moreover, this study compared the recess depths of metal-insulator-semiconductor high-electron-mobility transistors (MIS-HEMTs) with the SiN dielectric layer. The results showed that MIS-HEMTs exhibit more negative Vth values, which can be attributed to the controlled surface states achieved through passivation. © 2020 IEEE.

关键词： Etching

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Developer-Centric Task Allocation: An Intelligent Approach for Enhanced Software Productivity

Developer-Centric Task Allocation: An Intelligent Approach f...

引用

International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Evandro L. R. Fensterseifer Filho Joaquim V. C. Assunção Lisandra M. Fontoura Graduate Program in Computer Science Federal University of Santa Maria - UFSM Santa Maria – RS Brazil Applied Computing Department Federal University of Santa Maria - UFSM Santa Maria – RS Brazil

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Software projects are affected by technical knowledge as well as the personality of the team. Such factors can reduce or increase the software quality and development speed. For successful task allocation, it is essential to consider the skills and profile of each developer, thus maximizing their productivity. In projects with large teams, task allocation can be challenging, and the help of tools can facilitate its execution. In this work, we propose an intelligent approach for allocating software development tasks suitable to the profile of developers. From the literature, we define the appropriate skills and technical profiles for a development team, and the assessment is based on the developer completing a questionnaire. We developed a recommendation system to suggest tasks to be allocated to developers, employing text processing techniques. For validation, 495 tasks were used from an actual project developing simulator software for military training. The recommended allocations were evaluated by the project developers and used to improve the system. Validations showed that the developed approach makes consistent and coherent task recommendations to developers according to the developer profile, as participants agree with the recommendation for 76% of tasks.

关键词： Training Productivity Databases Mathematical models Classification algorithms Resource management Data mining Recommender systems Text processing Software development management

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Q-Learning-Based Multi-Objective Global Path Planning for UGV Navigation*

Q-Learning-Based Multi-Objective Global Path Planning for UG...

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IEEE Latin American Robotics Symposium, LARS

作者： Hiago O. B. Batista Thayron M. Hudson Kevin B. de Carvalho Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa - MG Brazil

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

This paper presents an offline path planning strategy for unmanned ground vehicles (UGVs) using Q-learning. The proposed method addresses path optimization in warehouse-like environments, where tasks involve item pickup and delivery to specific locations. The Q-learning algorithm trains an agent to determine the most efficient routes, with validation conducted in an $8 \times 5$ meter workspace equipped with an Optitrack motion capture system. The workspace was discretized into a $16 \times 10$ grid, allowing the Q-learning to effectively navigate through complex obstacle-laden scenarios. Experimental results indicate that the Q-learning approach outperforms traditional methods such as Dijkstra, A-star, and Breadth-First Search in terms of path length, number of turns, planning time, and overall success rate; being up to 7 times faster to plan a path and reducing the number of bends by up to 41%. The Q-learning based paths feature more linear segments, which contribute to energy savings and improved navigational efficiency. Future work will explore applications in heterogeneous multi-agent systems and enhancements in training time and agent collaboration.

关键词： Training Meters Q-learning Navigation Path planning Motion capture Planning Robots Optimization Multi-agent systems

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Audio-Driven Facial Landmark Generation in Violin Performance using 3DCNN Network with Self Attention Model

Audio-Driven Facial Landmark Generation in Violin Performanc...

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

作者： Ting-Wei Lin Chao-Lin Liu Li Su Social Networks and Human-Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Chengchi University Taipei Taiwan

In a music scenario, both auditory and visual elements are essential to achieve an outstanding performance. Recent research has focused on the generation of body movements or fingering from audio in music performance. The audio-driven face generation technique in music performance is still deficient. In this paper, we compile a violin soundtrack and facial expression dataset (VSFE) for modeling facial expressions in violin performance. To our knowledge, this is the first dataset mapping the relationship between violin performance audio and musicians’ facial expressions. We then propose a 3DCNN network with self-attention and residual blocks for audio-driven facial expression generation. In the experiments, we compare our methods with three baselines on talking face generation. The codes and dataset are available on the Github (https://***/kevinlin91/icassp_music2face).

关键词： Visualization Codes Avatars Signal processing Acoustics Multiple signal classification Speech processing

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Unveiling Population Heterogeneity in Health Risks Posed by Environmental Hazards Using Regression-Guided Neural Network

arXiv

引用

arXiv 2024年

作者： Nam, Jong Woo Choi, Eun Young Ailshire, Jennifer A. Chiang, Yao-Yi Neuroscience Graduate Program University of Southern California United States Leonard Davis School of Gerontology University of Southern California United States Department of Computer Science and Engineering University of Minnesota United States

Environmental hazards place certain individuals at disproportionately higher risks. As these hazards increasingly endanger human health, precise identification of the most vulnerable population subgroups is critical for public health. Moderated multiple regression (MMR) offers a straightforward method for investigating this by adding interaction terms between the exposure to a hazard and other population characteristics to a linear regression model. However, when the vulnerabilities are hidden within a cross-section of many characteristics, MMR is often limited in its capabilities to find any meaningful discoveries. Here, we introduce a hybrid method, named regression-guided neural networks (ReGNN), which utilizes artificial neural networks (ANNs) to non-linearly combine predictors, generating a latent representation that interacts with a focal predictor (i.e. variable measuring exposure to an environmental hazard). We showcase the use of ReGNN for investigating the population heterogeneity in the health effects of exposure to air pollution (PM2.5) on cognitive functioning scores. We demonstrate that population heterogeneity that would otherwise be hidden using traditional MMR can be found using ReGNN by comparing its results to the fit results of the traditional MMR models. In essence, ReGNN is a novel tool that enhances traditional regression models by effectively summarizing and quantifying an individual’s susceptibility to health risks. © 2024, CC BY-NC-SA.

关键词： Multiple linear regression

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Picosecond optical-to-x-ray (Cd,Mg)Te radiation detectors

Picosecond optical-to-x-ray (Cd,Mg)Te radiation detectors

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Cheng, J. Komissarov, I. Chen, G. Chakraborty, D. Kutcher, S. Chen, H. Trivedi, S. Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States Brimrose Technology Corp SparksMD21152 United States

We developed a dual optical/x-ray ultrafast photodetector based on in-house grown Cdo * Mg0.03Te single crystals. The detector is characterized by ~200 ps full-width-at-half-maximum, readout-electronics limited photor... 详细信息

ISBN: (纸本)9781957171258

关键词： X ray detectors

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Building A Face Database Of Arab Faces Toward Evaluating Bias In Facial Analysis Systems 17

Building A Face Database Of Arab Faces Toward Evaluating Bia...

引用

17th International Conference on Interfaces and Human computer Interaction 2023, IHCI 2023, the 17th International Conference on computer Graphics, Visualization, computer Vision and Image Processing 2023, CGVCVIP 2023 and 16th International Conference on Game and Entertainment Technologies 2023, GET 2023

作者： Khalil, Ashraf Glal, Suha Ahmed, Khider Khan, Sana Zeb Abdulgani, Aysha College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Mechatronics Engineering Graduate Program American university of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American university of Sharjah Sharjah United Arab Emirates United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9789898704498

Machine learning algorithms are fundamentally driven by the data provided by humans;consequently, the decisions made by those algorithms are not free from human bias. This is particularly evident in the case of facial analysis systems that employ machine learning algorithms. Recent studies have shown that the decisions made by many of the commercially available facial analysis systems are prejudiced against certain groups of race, ethnicity, age, gender and culture. Further studies have identified that the underlying reason for such biased decisions is that the open source material available for facial image databases which are used in commerce and academia to train the algorithms has meager diversity in these categories. To compound this issue, facial analysis technology is promoted by influential companies and artificial intelligence service providers without affirming the fairness and accuracy of the decisions given by these systems. To minimize bias and ensure representation of the Middle Eastern population in the imminent growth of this technology, we propose the development of two Arab face databases along with an algorithmic audit involving seven commercially available facial analysis systems. Of the databases, the first, Arab-LEANA, will include 300 Arab subjects' face images with variation in lighting, expression, accessory, nationality and age (LEANA). The second, Arab Public Figures Faces (APFF), will contain images and videos of 300 Arab public figures captured "in the wild". Faces for APFF will be selected manually from the internet since manual selection of faces will result in a high degree of variability in scale, pose, expression, illumination, age, occlusion and make-up. These databases will provide the worldwide community of face recognition researchers with a large-scale, diverse collection of Arab face images for training and evaluating algorithms toward developing a more representative, and therefore more robust, capacity for facial analysis. T

关键词： Machine learning

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Uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships 26th

Uncertainty Quantification Using Subsampling for Assembly-F...

引用

26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Rachtman, Eleonora Sarmashghi, Shahab Bafna, Vineet Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego La JollaCA United States Department of Electrical and Computer Engineering UC San Diego La JollaCA United States Department of Computer Science and Engineering UC San Diego La JollaCA United States

Computing the distance between two genomes, often without using alignments or even access to assembled sequences, is fundamental to many downstream analyses. © 2022, The Author(s), under exclusive license to Spri... 详细信息

ISBN: (纸本)9783031047480

关键词： Uncertainty analysis

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