مطالب

People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.

Limited energy capacity, physical distance between two nodes and the stochastic link quality are the major parameters in the selection of routing path in the internet of things network. To alleviate the problem of stochastic link quality as channel gain reinforcement based Q-learning energy balanced routing is presented in this paper. Using above mentioned parameter an optimization problem has been formulated termed as reward or utility of network. Further, formulated optimization problem converted into Markov decision problem (MDP) and their state, value, action and reward function are described. Finally, a QRL algorithm is presented and their time complexity is analyses. To show the effectiveness of proposed QRL algorithm extensive simulation is performed in terms of convergence property, energy consumption, residual energy and reward with respect to state-of-art-algorithms.

The real-time video surveillance system has become an integral part of our life and Highways play a very crucial role in transportation. For a transportation system to work, the management of highways are necessary. It also prevents accident and other challenging issues on highways. Various machine learning and artificial intelligence based techniques are evolving with numerous advancement in this domain. These algorithms are efficient and very less time consuming. So the use of machine learning and artificial intelligence in transportation systems and highways could be very beneficial. In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. The literature also reveals for existing research for the machine learning and AI based methodologies to resolve more complex real-time problems. The proposed work is also compared with the existing peer methods and demonstrated better performance achieved experimentally.

The article examines the development of theoretical and methodological approaches and practical recommendations for improving the ecological and economic foundations of land management and land management at the local level in the example of the European Union. In practice, regional, spatial, strategic, landscape, and integrated types of land use development planning are most often used in the European Union. With the development of the market economy of European society, environmental problems arose, which prompted the development of landscape planning, both landscape-ecological and socio-economic aspects of territorial development. The experience of landscape planning was introduced in Germany, which later became used in almost all European countries. The study and direct comparison of existing landscape planning systems in different European countries, and their reduction to several basic features are not possible and impractical because these systems are very different. This is due to a number of reasons: history, features of political systems, cultural traditions, level of economic development, the nature of legal systems and property relations. Landscape planning in the European Union is going to be one of the tensest areas of activity in the future, and Ukraine must take an active part in this process.

INTRODUCTION: Despite the prevalence of the term “household disaster preparedness”, there is no consensus over a single and clear definition in this regard. The present study aimed to identify the components and explain the concept of household disaster preparedness. METHODS: The present study was conducted based on a qualitative design. After reviewing the concept and components of household disaster preparedness in previous studies, the subjects were selected by purposive sampling from managers and experts active in the field of disaster risk reduction, as well as heads of families. The data were collected through semi-structured interviews and were analyzed by the thematic analysis method, then the main and sub-components of household disaster preparedness were identified. FINDINGS: The classification of the components of household disaster preparedness revealed that this concept encompasses cognitive, physical-operational, financial, social, and psychological dimensions. However, household disaster preparedness was defined based on these dimensions. CONCLUSION: Household disaster preparedness consists of different dimensions. In order to evaluate this preparedness, an appropriate instrument is needed to assess all its dimensions.

INTRODUCTION: Crisis management maintains security, stability, and flexibility in society by integrating different social resources with appropriate decisions and strengthening the ability of the community to recover from the negative impacts. Therefore, the present study aimed to conduct a model analysis of effective factors in implementing crisis management policies. METHODS: The present study is a descriptive survey research with a mixed methods design. A total of 18 experts were non-randomly and purposefully selected for the qualitative section based on expertise and availability. In addition, 248 individuals were selected from managers and experts (658 people) of the National Disaster Management Organization of Iran and partner organizations for the quantitative section based on Krejcie and Morgan Table using Cochran's formula and simple random sampling. The research instrument was a researcher-made questionnaire including 33 factors affecting the implementation of crisis management policy, the content validity of which was confirmed by professors, and its reliability by Cronbach's alpha test (α = 0.89). Data were analyzed using SPSS and AMOS software and the AHP method was used to prioritize the factors. FINDINGS: Findings indicated that 33 components in the form of structural, organizational, environmental, and management factors, as well as financial and human resources, uniform practices, communication, information, and inter-organizational coordination, regulations, and policy implementers successfully affect the implementation of crisis management policies. Structural, organizational, and environmental factors significantly affect the implementation of policies. CONCLUSION: Defining the responsibilities of partner organizations as well as developing transparency and designing specific goals contribute to the successful implementation of crisis management policies.