scholarly journals Monitoring war destruction from space using machine learning

2021 ◽  
Vol 118 (23) ◽  
pp. e2025400118
Author(s):  
Hannes Mueller ◽  
Andre Groeger ◽  
Jonathan Hersh ◽  
Andrea Matranga ◽  
Joan Serrat
Keyword(s):  
Temporal Structure ◽  
Humanitarian Relief ◽  
Conflict Zones ◽  
Automated Method ◽  
Syrian Civil War ◽  
Temporal Smoothing ◽  
Learning Techniques ◽  
High Resolution Satellite Images ◽  
Eyewitness Reports ◽  
Relief Efforts

Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.

scholarly journals Medical Image Segmentation and Classification Based on Sparse Representation and Dictionary Learnng Algorithms

2021 ◽  
Author(s):  
Mohammadali Julazadeh
Keyword(s):  
Sparse Representation ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Accuracy Rate ◽  
Automated Method ◽  
Classification Framework ◽  
Learning Techniques ◽  
Sparse Representation Classification ◽  
Euclidian Distances

In this thesis a novel classification approach based on sparse representation framework is proposed. The method finds the minimum Euclidian distance between an input patch (pattern) and atoms (templates) of a learned-base dictionary for different classes to perform the classification task. A mathematical approach is developed to map the sparse representation vector to Euclidian distances. We show that the highest coefficient of the sparse vector is not necessarily a suitable indicator for classification. The proposed algorithm is compared with the conventional Sparse Representation Classification (SRC) framework as well as non-sparse based methods to evaluate its performance. Taking advantage of the introduced classification framework, we then propose a novel fully automated method for the purpose of segmenting different organs in medical images of the human body. Our results demonstrated an acceptable accuracy rate for both classification and the segmentation frameworks. To our knowledge, no other method utilizes sparse representation and dictionary learning techniques in order to segment medical images.

scholarly journals CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis

2021 ◽  
Author(s):  
Amin Heyrani Nobari ◽  
Muhammad Fathy Rashad ◽  
Faez Ahmed
Keyword(s):  
Novelty Detection ◽  
Computational Design ◽  
Generative Models ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Creative Design ◽  
Design Synthesis ◽  
Adversarial Networks ◽  
Automated Method ◽  
Learning Techniques

Abstract Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which is crucial for creating products and enabling innovation. Generative models, including generative adversarial networks (GANs), have proven to be effective for design synthesis with applications ranging from product design to metamaterial design. These automated computational design methods can support human designers, who typically create designs by a time-consuming process of iteratively exploring ideas using experience and heuristics. However, there are still challenges remaining in automatically synthesizing ‘creative’ designs. GAN models, however, are not capable of generating unique designs, a key to innovation and a major gap in AI-based design automation applications. This paper proposes an automated method, named CreativeGAN, for generating novel designs. It does so by identifying components that make a design unique and modifying a GAN model such that it becomes more likely to generate designs with identified unique components. The method combines state-of-art novelty detection, segmentation, novelty localization, rewriting, and generative models for creative design synthesis. Using a dataset of bicycle designs, we demonstrate that the method can create new bicycle designs with unique frames and handles, and generalize rare novelties to a broad set of designs. Our automated method requires no human intervention and demonstrates a way to rethink creative design synthesis and exploration. For details and code used in this paper please refer to http://decode.mit.edu/projects/creativegan/.

scholarly journals Humanitarian Action and Peacebuilding: Incompatible or Complementary?

2020 ◽  
pp. 257-275
Author(s):  
Jens Pedersen
Keyword(s):  
Peace Operations ◽  
Humanitarian Relief ◽  
Conflict Zones ◽  
Major Powers ◽  
Contested Space ◽  
Humanitarian Action ◽  
Humanitarian Organizations ◽  
Changing Role ◽  
Hearts And Minds

Abstract This chapter examines the changing role of humanitarian organizations in Africa’s conflict zones and how humanitarianism has become a highly contested space on the battlefield. Through an analysis of several ongoing peace operations in Africa, this chapter demonstrates how the principles of humanitarian relief have been undermined by the major powers and the UN in their pursuit of ostensibly noble objectives. Organizations and donors have become complicit in compromising humanitarianism, especially in multi-mandated UN missions, by inserting humanitarian workers into the realm of both service delivery (associated with the process of building a state) and as a political tool to win “hearts and minds.” Peacebuilding will be better served, the chapter concludes, by restoring humanitarianism to its original role and ethos.

Central African Republic peace deal needs executing

2019 ◽  
Keyword(s):  
Central African Republic ◽  
Technical Assistance ◽  
Power Sharing ◽  
African Union ◽  
Humanitarian Relief ◽  
Peace Agreement ◽  
Content Type ◽  
The Government ◽  
Relief Efforts ◽  
The Eu

Significance The accord, the full contents of which are still not public, differs from previous deals in that it follows the first direct talks between the parties and because the government has reportedly met two key demands of armed groups: amnesty and power-sharing. These are controversial measures, but they may give the deal a greater chance of success than earlier efforts. Impacts If implemented, the peace agreement could facilitate humanitarian relief efforts and lead to gradual economic recovery. The new government should secure additional financial and technical assistance for the transition from the EU, UN and individual states. The actions of the African Union and neighbouring states, particularly Sudan and Chad, will carry more weight than Western partners.

scholarly journals Humanitarian Aid, Security and Ethics

2020 ◽  
Vol 4 (1) ◽  
pp. 135-156
Author(s):  
Hedva Eyal ◽  
Limor Samimian-Darash ◽  
Nadav Davidovitch
Keyword(s):  
Medical Treatment ◽  
Humanitarian Aid ◽  
Standard Of Care ◽  
Conflict Zones ◽  
Security Forces ◽  
Ethical Frameworks ◽  
Syrian Civil War ◽  
Medical Security ◽  
The Relationship

The article examines the relationship between humanitarianism, security, and ethics in the case of the provision of medical humanitarian aid by Israel to casualties from the Syrian civil war, between 2013 and 2018. We argue that this humanitarian project differs from the type of humanitarian intervention commonly seen in conflict zones and can be identified as a new form of humanitarian governance. Our case study deals with humanitarian care provided in the country of origin of the medical and security forces involved, rather than in the country of the injured. In this articulation of humanitarianism at home a new nature of life governance and new subjects of security, emerge. We argue that the politics of life shifts and is subordinated to two different ethical frameworks founded on two different logics: that of the human (as in the type of medical treatment seen in traditional humanitarian aid provision, which is often related to short-term immediate treatment) and that of the citizen (the standard of care provided to all official residents of Israel. The conflict between these two moralities, the shifting standard of medical treatment, and the new medical-security space – together, raise a new set of ethical and political questions.

scholarly journals TOWARDS "DRONE-BORNE" DISASTER MANAGEMENT: FUTURE APPLICATION SCENARIOS

2016 ◽  
Vol III-8 ◽  
pp. 181-189 ◽  
Author(s):  
Tullio Joseph Tanzi ◽  
Madhu Chandra ◽  
Jean Isnard ◽  
Daniel Camara ◽  
Olivier Sebastien ◽  
...  
Keyword(s):  
Disaster Management ◽  
Situational Awareness ◽  
Data Gathering ◽  
Future Application ◽  
Use Case ◽  
Humanitarian Relief ◽  
Security Risks ◽  
Flight Operations ◽  
New Generation ◽  
Relief Efforts

Information plays a key role in crisis management and relief efforts for natural disaster scenarios. Given their flight properties, UAVs (Unmanned Aerial Vehicles) provide new and interesting perspectives on the data gathering for disaster management. A new generation of UAVs may help to improve situational awareness and information assessment. Among the advantages UAVs may bring to the disaster management field, we can highlight the gain in terms of time and human resources, as they can free rescue teams from time-consuming data collection tasks and assist research operations with more insightful and precise guidance thanks to advanced sensing capabilities. However, in order to be useful, UAVs need to overcome two main challenges. The first one is to achieve a sufficient autonomy level, both in terms of navigation and interpretation of the data sensed. The second major challenge relates to the reliability of the UAV, with respect to accidental (safety) or malicious (security) risks. <br><br> This paper first discusses the potential of UAV in assisting in different humanitarian relief scenarios, as well as possible issues in such situations. Based on recent experiments, we discuss the inherent advantages of autonomous flight operations, both lone flights and formation flights. The question of autonomy is then addressed and a secure embedded architecture and its specific hardware capabilities is sketched out. <br><br> We finally present a typical use case based on the new detection and observation abilities that UAVs can bring to rescue teams. Although this approach still has limits that have to be addressed, technically speaking as well as operationally speaking, it seems to be a very promising one to enhance disaster management efforts activities.

scholarly journals Machine learning and deep learning techniques to support the clinical diagnosis of arboviral diseases: A systematic review

2021 ◽  
Author(s):  
Sebastião Rogério da Silva Neto ◽  
Thomás Tabosa Oliveira ◽  
Igor Vitor Teixeira ◽  
Samuel Benjamin Aguiar de Oliveira ◽  
Vanderson Souza Sampaio ◽  
...  
Keyword(s):  
Clinical Diagnosis ◽  
Rural Areas ◽  
Binary Classification ◽  
Clinical Decision ◽  
Conflict Zones ◽  
Use Of Resources ◽  
Learning Techniques ◽  
Arboviral Diseases

Abstract Background: NTDs primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on ML and DL models. Method: We carried out a SLR in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and 7 from single backward snowballing technique), only 15 relevant papers were identified. Results: Results show that current research is focused on the binary classification of Dengue, primarily using Tree based ML algorithms and only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its levels) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life.

scholarly journals INTRAHEPATIC CHOLANGIOCARCINOMA DETECTION AND SEGMENTATION OF IMAGES USING DEEP LEARNING TECHNIQUES

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Muhammad Shoaib Kareem ◽  
Zeeshan Ahmad ◽  
Talha Farooq Khan ◽  
Mohsin Shahzad ◽  
Mohsin Bashir ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Intrahepatic Cholangiocarcinoma ◽  
Detection System ◽  
Tumor Segmentation ◽  
Cancer Disease ◽  
Automated Method ◽  
Learning Techniques ◽  
Diagnosis And Classification ◽  
Segmentation Of Images ◽  
Processing Techniques

Intrahepatic cholangiocarcinoma is a form of cancer that forms in the cells of the bile ducts, both inside and outside of the liver. Cholangiocarcinoma and bile duct cancer are two words that are often used interchangeably to describe the same disease. Therefore, we have proposed an intelligent Hepatoma detection system. So, the main purpose of this research is to develop and implement an automated method that will help to detect and classify the Liver Cancer disease by processing hepatomic images. We have used liver-tumor-segmentation dataset for the testing our proposed methodology, it contains 130 images of Liver Cancer patients. We have applied pre-processing techniques on these images such as morphological filtering, in order to enhance images from input data for post processing. After obtaining the resultant image we have applied slicing. We have used UNets (modified form of convolutional Neural Network) for classification purpose with ResNet34, 50 and 100 architecture for downsampling and upsampling of shifted pixels. The proposed technique provides a sophisticated diagnosis and classification accuracy when compared with previous techniques. The parameter we used to validate the performance of our proposed technique is Top-N accuracy. Our proposed method shows the accuracy of about 99.8%.

Sign in / Sign up

Export Citation Format

Share Document