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Determining the stock status of snapper (Lutjanus sp.) using surplus production model: a case study in Banyuasin coastal waters, South Sumatra, Indonesia

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IOP Conference Series: Earth and Environmental science 2020年第1期404卷

作者： Fauziyah A I S Purwiyanto F Agustriani W A E Putri Ermatita A Putra Marine Science Study Program Faculty of Mathematics and Natural Sciences Sriwijaya University Indonesia Information System Department Faculty of Computer Science Sriwijaya University Indonesia

Snapper (Lutjanus sp.) is an economically important fish for local fishermen in Banyuasin coastal water of South Sumatra. However, the current and historical stock of this species is still unknown. This study was aimed to estimate the stock status of Lutjanus sp. in the Banyuasin coastal waters. The annual catch and effort data were analyzed from 2008 to 2016. The different surplus production models were tested to obtain the best-fitted model based on the sign suitability test, model performance test, and multiple criteria analysis. The results indicated that the best-fitted model for Lutjanus sp. was the Fox model. The model had the best value for the determination coefficient (R2 = 97.2%), Nash-Sutcliffe Efficiency (-0.277), Mean Absolute Deviation (29.198), Mean Square Error (1,190.522), Root Mean Square Error (34.504), and RMSE-observations Standard Deviation Ratio (1.13), whereas the value of Mean Absolute Percentage Error (0.05) was the second-best value. The optimum effort (Eopt), maximum sustainable catch (CMSY), and total allowable catch were 22.236 trips/year, 623 ton and 498 ton/year, respectively. Based on plotting the effort and exploitation level (141%; 102%) in 2016, the stock status of Lutjanus sp. indicated depleting stock, the high fishing pressure and could encourage overfishing stock in the future.

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Fuzzy logic and A* algorithm implementation on goat foraging games

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IOP Conference Series: Materials science and Engineering 2018年第1期332卷

作者： P Harsani I Mulyana D Zakaria Computer Science Study Program Pakuan University Jl. Pakuan PO Box 452 Bogor 16143 Jawa Barat Indonesia

Goat foraging is one of the games that apply the search techniques within the scope of artificial intelligence. This game involves several actors including players and enemies. The method used in this research is fuzzy logic and Algorithm A*. Fuzzy logic is used to determine enemy behaviour. The A* algorithm is used to search for the shortest path. There are two input variables: the distance between the player and the enemy and the anger level of the goat. The output variable that has been defined is the enemy behaviour. The A* algorithm is used to determine the closest path between the player and the enemy and define the enemy's escape path to avoid the player. There are 4 types of enemies namely farmers, planters, farmers and sellers of plants. Players are goats that aims to find a meal that is a plant. In this game goats aim to spend grass in the garden in the form of a maze while avoiding the enemy. The game provides an application of artificial intelligence and is made in four difficulty levels.

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Classification of apples based on color feature extraction and Euclidean distance search

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AIP Conference Proceedings 2024年第1期3048卷

作者： Dian Nursantika Erna Piantari Information System Study Program Department of Engineering Universitas Terbuka Tangerang Indonesia Department of Computer Science Universitas Pendidikan Indonesia Bandung Indonesia

Apple is a popular fruit in the world, until it became an icon by one of the leading software and hardware developers. The colors of apples that are often found in Indonesia are red and green, humans are recognize the color of the apple through sight, based on previous knowledge and experience. How about the computer, is the computer able to recognize the color of the apple? Through this research, it is expected to solve these challenges, so the research results can be applied in various devices, for example for grade classification of apples, identification of types of apples, and educational games. The research consists of two parts, namely training and testing. Training is conducted to train images by means of preprocessing, feature extraction, and feature collection. Testing is carried out to determine the ability of computers to recognize apples with the stages of preprocessing, feature extraction, and feature matching using the Euclidean distance algorithm. The images used in this study were 40 training images and 65 testing images. The results of testing on the training image have an accuracy rate of 100%, while the accuracy level of the new image is 84% recognized as true and 16% recognized as false.

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Performance LoRa Technology for Autonomous Vehicles

Performance LoRa Technology for Autonomous Vehicles

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Puput Dani Prasetyo Adi Iwan Purnama Arfanda Anugrah Siregar Angga Putra Juledi Firman Edi Abdul Karim Yuyu Wahyu Fairuz Iqbal Maulana Suhardi Atmoko Budi Susilo Toni Ilham Maliki Harahap Lintang Patria National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Science and Technology Labuhanbatu University Labuhanbatu North Sumatra Indonesia Telecommunication Engineering Study Program Politeknik Negeri Medan Medan City North Sumatra Indonesia Department of Engineering Management Faculty of Industrial Technology Batam Institute of Technology Batam City Riau Islands Indonesia Department of Information Technology Budi Darma University Medan North Sumatra Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Teacher Training and Education Labuhanbatu University Labuhanbatu North Sumatra Indonesia Faculty of Engineering Universitas Terbuka Jakarta Indonesia

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss.

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A Guide to Tracking Phylogenies in Parallel and Distributed Agent-based Evolution Models

arXiv

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arXiv 2024年

作者： Moreno, Matthew Andres Ranjan, Anika Dolson, Emily Zaman, Luis Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, in silico work with agent-based models provides an opportunity to collect high-quality records of ancestry relationships among simulated agents. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Existing work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, direct tracking can be inefficient for large-scale, many-processor evolutionary simulations. An alternate approach to extracting phylogenetic information from simulation that scales more favorably is post hoc estimation, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recently introduced "hereditary stratigraphy" algorithms provide means for efficient inference of phylogenetic history from non-coding annotations on simulated organisms' genomes. A number of options exist in configuring hereditary stratigraphy methodology, but no work has yet tested how they impact reconstruction quality. To address this question, we surveyed reconstruction accuracy under alternate configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. We synthesize results from these experiments to suggest a prescriptive system of best practices for work with hereditary stratigraphy, ultimately guiding researchers in choosing appropriate instrumentation for large-scale simulation studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Biotic

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Phenotype Search Trajectory Networks for Linear Genetic programming

arXiv

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arXiv 2022年

作者： Hu, Ting Ochoa, Gabriela Banzhaf, Wolfgang School of Computing Queen's University KingstonONK7L 2N8 Canada University of Stirling StirlingFK9 4LA United Kingdom Department of Computer Science and Engineering BEACON Center for the Study of Evolution in Action and Ecology Evolution and Behavior Program Michigan State University East LansingMI48864 United States

Genotype-to-phenotype mappings translate genotypic variations such as mutations into phenotypic changes. Neutrality is the observation that some mutations do not lead to phenotypic changes. studying the search trajectories in genotypic and phenotypic spaces, especially through neutral mutations, helps us to better understand the progression of evolution and its algorithmic behaviour. In this study, we visualise the search trajectories of a genetic programming system as graph-based models, where nodes are genotypes/phenotypes and edges represent their mutational transitions. We also quantitatively measure the characteristics of phenotypes including their genotypic abundance (the requirement for neutrality) and Kolmogorov complexity. We connect these quantified metrics with search trajectory visualisations, and find that more complex phenotypes are under-represented by fewer genotypes and are harder for evolution to discover. Less complex phenotypes, on the other hand, are over-represented by genotypes, are easier to find, and frequently serve as stepping-stones for evolution. © 2022, CC BY.

关键词： Complex networks

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Villages Status Classification Analysis Involving K-Means Algorithm To Support Kementerian Desa Pembangunan Daerah Tertinggal dan Transmigrasi Work programs

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Journal of Physics: Conference Series 2020年第1期1641卷

作者： Paska Marto Hasugian Harvei Desmon Hutahaean Bosker Sinaga Sriadhi Saranom Silaban Informatics Engineering Study Program STMIK Pelita Nusantara Medan Indonesia Computer Science and Informatics Education Study Program Faculty of Engineering Universitas Negeri Medan Medan 20221 Indonesia Departement of chemistry Universitas Negeri Medan Medan 20221 Indonesia

Data mining is a technique of extracting information that has not been known before in a collection of data in the database. Data mining has been applied in various fields that require extracting information, some of the work that can be generated with data mining is classification, prediction, and data grouping. In this study, an analysis of village data collection was carried out to explore the potential or knowledge of the data that has been presented with the main objective of producing a grouping of village status. To support clustering or grouping activities the K-means algorithm used with the general process is to carry out the modeling process without supervision and is one of the methods for grouping data with a system partition, with the principle of allocating each data to the centroid or the closest average, work steps conducted in support of this research is to collect data related to the analysis of data grouping and proceed with the calculation process in accordance with the work steps of the K-means algorithm, the amount of data used as a test of 303 villages scattered in the old Padang regency. The results of calculations by displaying a new group of Cluster 0 is occupied by 120 villages, cluster 1 with a total data of 123 villages, Cluster 2 with a total of 6 villages, cluster 3 with a total of 33 villages while cluster 4 with a total of 21.

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Review the Utilization of Big Data and K-Means Algorithm in Supporting The Determination of Village Status As Support To The Ministry of Village PDTT

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Journal of Physics: Conference Series 2021年第1期1811卷

作者： Paska Marto Hasugian Harvei Desmon Hutahaean Bosker Sinaga Sriadhi Saronom Silaban Informatics Engineering Study Program STMIK Pelita Nusantara Medan Indonesia Computer Science and Informatics Education Study Program Faculty of Engineering Universitas Negeri Medan Medan 20221 Indonesia Departement of chemistry Universitas Negeri Medan Medan 20221 Indonesia

Big data refers to big data, fast data processing, diversity of data structures, and data values so that it is not possible to be processed with outdated methods. Big data technology is used in various industrial sectors. Big data technology is the whole technology that can handle Big Data. Some of the uses of big data are based on the largest significant data traffic sources such as social media, financial transactions, public data, sensor data, and corporate data. The same is the case with the status of so many villages in Indonesia, so it is better to use big data for the classification of village status based on the village index build by involving an algorithm process. This research aims to produce a description of the role of big data in supporting activities in a grouping. The method used is a qualitative approach related to data collection based on scientific work with the source of data information needed to study literature techniques from various studies that have been published in national and international journals. A decision is made that big data has been widely used in different circles to facilitate performance and speed up the decision-making process.

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Design of big data technology prototype for classification of village status based on village development index involves k-means algorithm to support village ministry Pdtt work programs

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Journal of Physics: Conference Series 2021年第1期1811卷

Big data technology is the overall technology that can handle the processing associated with analyzing the data to explore the potential that is in it. Some of the uses of big data are based on the biggest data traffic sources, such as social media, financial transactions, public data, censorship data, and company data. The problem found in the accumulation of village status data that has not been utilized in the decision-making process to determine the status of the next village and the absence of an application to process the data stored in the data database so that a tool is developed to analyze the buildup. The results of this study are to produce a prototype design that is Unified Modeling Language (UML) design, Form Design, and Database Design that will be used to develop Big Data Technology in determining the classification of village status based on the Village Build index.

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