An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation

Mission planning of air strike operations is hard because it has to give instructions to a large number of units during a relatively long period of time in an uncertain environment. If some instruction parameters can be calculated by an intelligent agent, better strategies can be found more quickly. In a specific combat scenario of air strike operations against islands, an intelligent model is proposed to improve the performance and flexibility of mission planning. The proposed intelligent mission planning model is based on rule-based decision and uses a fully connected recurrent neural network to calculate some of the decision parameters. The proposed intelligent mission planning model shows better results as compared to rule-based decision making with randomized parameters, and it performs as good as experts in the test set of the specific combat scenario.

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Comparison of rule-based and neural network models for negation detection in radiology reports

Natural Language Engineering ◽

10.1017/s1351324920000509 ◽

2020 ◽

pp. 1-22 ◽

Cited By ~ 2

Author(s):

D. Sykes ◽

A. Grivas ◽

C. Grover ◽

R. Tobin ◽

C. Sudlow ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Language Processing ◽

Network Models ◽

Neural Network Models ◽

Test Set ◽

Rule Based ◽

Radiology Reports ◽

The Neural Network ◽

Negation Detection

Abstract Using natural language processing, it is possible to extract structured information from raw text in the electronic health record (EHR) at reasonably high accuracy. However, the accurate distinction between negated and non-negated mentions of clinical terms remains a challenge. EHR text includes cases where diseases are stated not to be present or only hypothesised, meaning a disease can be mentioned in a report when it is not being reported as present. This makes tasks such as document classification and summarisation more difficult. We have developed the rule-based EdIE-R-Neg, part of an existing text mining pipeline called EdIE-R (Edinburgh Information Extraction for Radiology reports), developed to process brain imaging reports, (https://www.ltg.ed.ac.uk/software/edie-r/) and two machine learning approaches; one using a bidirectional long short-term memory network and another using a feedforward neural network. These were developed on data from the Edinburgh Stroke Study (ESS) and tested on data from routine reports from NHS Tayside (Tayside). Both datasets consist of written reports from medical scans. These models are compared with two existing rule-based models: pyConText (Harkema et al. 2009. Journal of Biomedical Informatics42(5), 839–851), a python implementation of a generalisation of NegEx, and NegBio (Peng et al. 2017. NegBio: A high-performance tool for negation and uncertainty detection in radiology reports. arXiv e-prints, p. arXiv:1712.05898), which identifies negation scopes through patterns applied to a syntactic representation of the sentence. On both the test set of the dataset from which our models were developed, as well as the largely similar Tayside test set, the neural network models and our custom-built rule-based system outperformed the existing methods. EdIE-R-Neg scored highest on F1 score, particularly on the test set of the Tayside dataset, from which no development data were used in these experiments, showing the power of custom-built rule-based systems for negation detection on datasets of this size. The performance gap of the machine learning models to EdIE-R-Neg on the Tayside test set was reduced through adding development Tayside data into the ESS training set, demonstrating the adaptability of the neural network models.

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Actor–critic-based decision-making method for the artificial intelligence commander in tactical wargames

The Journal of Defense Modeling and Simulation Applications Methodology Technology ◽

10.1177/1548512920954542 ◽

2020 ◽

pp. 154851292095454

Author(s):

Junfeng Zhang ◽

Qing Xue

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Decision Making ◽

Reinforcement Learning ◽

Convolutional Neural Network ◽

Difficult Problem ◽

Learning Method ◽

Rule Based ◽

Autonomous Decision ◽

Decision Making Problem

In a tactical wargame, the decisions of the artificial intelligence (AI) commander are critical to the final combat result. Due to the existence of fog-of-war, AI commanders are faced with unknown and invisible information on the battlefield and lack of understanding of the situation, and it is difficult to make appropriate tactical strategies. The traditional knowledge rule-based decision-making method lacks flexibility and autonomy. How to make flexible and autonomous decision-making when facing complex battlefield situations is a difficult problem. This paper aims to solve the decision-making problem of the AI commander by using the deep reinforcement learning (DRL) method. We develop a tactical wargame as the research environment, which contains built-in script AI and supports the machine–machine combat mode. On this basis, an end-to-end actor–critic framework for commander decision making based on the convolutional neural network is designed to represent the battlefield situation and the reinforcement learning method is used to try different tactical strategies. Finally, we carry out a combat experiment between a DRL-based agent and a rule-based agent in a jungle terrain scenario. The result shows that the AI commander who adopts the actor–critic method successfully learns how to get a higher score in the tactical wargame, and the DRL-based agent has a higher winning ratio than the rule-based agent.

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Expert Guided Rule Based Prioritization of Scientifically Relevant Images for Downlinking over Limited Bandwidth from Planetary Orbiters

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019440 ◽

2019 ◽

Vol 33 ◽

pp. 9440-9445

Author(s):

Srija Chakraborty ◽

Subhasish Das ◽

Ayan Banerjee ◽

Sandeep K. S. Gupta ◽

Philip R. Christensen

Keyword(s):

Decision Making ◽

Expert Knowledge ◽

Test Set ◽

Rule Based ◽

Limited Bandwidth ◽

Autonomous Decision ◽

Test Instance ◽

A Priority ◽

Rule Based Approach

Instruments onboard spacecraft acquire large amounts of data which is to be transmitted over a very low bandwidth. Consequently for some missions, the volume of data collected greatly exceeds the volume that can be downlinked before the next orbit. This necessitates the introduction of an intelligent autonomous decision making module that maximizes the return of the most scientifically relevant dataset over the low bandwidth for experts to analyze further. We propose an iterative rule based approach, guided by expert knowledge, to represent scientifically interesting geological landforms with respect to expert selected attributes. The rules are utilized to assign a priority based on how novel a test instance is with respect to its rule. High priority instances from the test set are used to iteratively update the learned rules. We then determine the effectiveness of the proposed approach on images acquired by a Mars orbiter and observe an expert-acceptable prioritization order generated by the rules that can potentially increase the return of scientifically relevant observations.

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Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00034 ◽

2019 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Imon Banerjee ◽

Selen Bozkurt ◽

Jennifer Lee Caswell-Jin ◽

Allison W. Kurian ◽

Daniel L. Rubin

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Metastatic Breast Cancer ◽

Natural Language Processing ◽

Language Processing ◽

Cancer Recurrence ◽

Metastatic Breast ◽

Test Set ◽

Rule Based ◽

Metastatic Recurrence

PURPOSE Electronic medical records (EMRs) and population-based cancer registries contain information on cancer outcomes and treatment, yet rarely capture information on the timing of metastatic cancer recurrence, which is essential to understand cancer survival outcomes. We developed a natural language processing (NLP) system to identify patient-specific timelines of metastatic breast cancer recurrence. PATIENTS AND METHODS We used the OncoSHARE database, which includes merged data from the California Cancer Registry and EMRs of 8,956 women diagnosed with breast cancer in 2000 to 2018. We curated a comprehensive vocabulary by interviewing expert clinicians and processing radiology and pathology reports and progress notes. We developed and evaluated the following two distinct NLP approaches to analyze free-text notes: a traditional rule-based model, using rules for metastatic detection from the literature and curated by domain experts; and a contemporary neural network model. For each 3-month period (quarter) from 2000 to 2018, we applied both models to infer recurrence status for that quarter. We trained the NLP models using 894 randomly selected patient records that were manually reviewed by clinical experts and evaluated model performance using 179 hold-out patients (20%) as a test set. RESULTS The median follow-up time was 19 quarters (5 years) for the training set and 15 quarters (4 years) for the test set. The neural network model predicted the timing of distant metastatic recurrence with a sensitivity of 0.83 and specificity of 0.73, outperforming the rule-based model, which had a specificity of 0.35 and sensitivity of 0.88 ( P < .001). CONCLUSION We developed an NLP method that enables identification of the occurrence and timing of metastatic breast cancer recurrence from EMRs. This approach may be adaptable to other cancer sites and could help to unlock the potential of EMRs for research on real-world cancer outcomes.

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Identifying Earth-impacting asteroids using an artificial neural network

Astronomy and Astrophysics ◽

10.1051/0004-6361/201935983 ◽

2020 ◽

Vol 634 ◽

pp. A45

Author(s):

John D. Hefele ◽

Francesco Bortolussi ◽

Simon Portegies Zwart

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Test Set ◽

Hazardous Object ◽

Artificial Neural ◽

Fully Connected

By means of a fully connected artificial neural network, we identified asteroids with the potential to impact Earth. The resulting instrument, named the Hazardous Object Identifier (HOI), was trained on the basis of an artificial set of known impactors which were generated by launching objects from Earth’s surface and integrating them backward in time. HOI was able to identify 95.25% of the known impactors simulated that were present in the test set as potential impactors. In addition, HOI was able to identify 90.99% of the potentially hazardous objects identified by NASA, without being trained on them directly.

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Deep Learning for Plant Classification and Content-Based Image Retrieval

Cybernetics and Information Technologies ◽

10.2478/cait-2019-0005 ◽

2019 ◽

Vol 19 (1) ◽

pp. 88-100

Author(s):

Bálint Pál Gyires-Tóth ◽

Márton Osváth ◽

Dávid Papp ◽

Gábor Szűcs

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Retrieval ◽

Search Task ◽

Feature Learning ◽

Content Based Image Retrieval ◽

Network Architectures ◽

Average Precision ◽

Test Set ◽

Fully Connected

Abstract The main goal of the present research is to classify images of plants to species with deep learning. We used convolutional neural network architectures for feature learning and fully connected layers with logsoftmax output for classification. Pretrained models on ImageNet were used, and transfer learning was applied. In the current research image sets published in the scope of the PlantCLEF 2015 challenge were used. The proposed system surpasses the results of all top competitors of the challenge by 8% and 7% at observation and image levels, respectively. Our secondary goal was to satisfy the users’ needs in content-based image retrieval to give relevant hits during species search task. We optimized the length of the returned lists in order to maximize MAP (Mean Average Precision), which is critical to the performance of image retrieval. Thus, we achieved more than 50% improvement of MAP in the test set compared to the baseline.

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Self Learning Gaming Bot using CNN.

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1833.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 2397-2401

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Learning Algorithm ◽

Intelligent Agent ◽

Maximum Score ◽

Input Method ◽

Present Universe ◽

High Level ◽

Fully Connected ◽

Self Learning

In the present universe of current gaming condition bots are the intelligent agent that assumes a prominent job in the popularity of a game in the market. As these bots have gotten very unsurprising to the games. So here we are proposed an AI model for playing games with high level inputs using reinforcement learning. Algorithm works in the Atari Environment i.e. we are using 2D game. This model consists of the CNN (convolution neural network) for the inputs which is fully connected layers and find out the actions according to the inputs. In this learning -based approach, bots learned how to attack and ignore opponents so that bot can get maximum score. In this learning -based approach, bots learned how to attack and ignore opponents so that bot can get maximum score Then we tried the combine the input method which results maximum score of the bot in the environment for the better performance.

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KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Jurnal Ilmiah Informatika Komputer ◽

10.35760/ik.2019.v24i3.2364 ◽

2019 ◽

Vol 24 (3) ◽

pp. 220-228

Author(s):

Gusti Alfahmi Anwar ◽

Desti Riminarsih

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Input Layer ◽

Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

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