A Comparison of Grading Models for Neighborhood Level of Family Housing Units

More recently Turkey has witnessed fast housing development and real estate sector growth because of the mortgage preparations. With this development, property location quality has been considered important for selecting and paying them. This study uses a data set of new single family housing units in Kocaeli University Campus Area. By using 4 location quality criteria, 27 single family housing units are graded at the neighborhood level. It is aimed to examine the applications of grading property at the neighborhood level based on property location quality by testing with three methods. Traditional method and fuzzy logic method were discussed in our antecedent studies. In this study, an easy used numerical calculation method; Neural Networks (NN), is introduced. Its grading performance is compared with the previous methods. NN method is found to be more accurate and realistic than traditional grading approach where its designing stage is more practical and faster than fuzzy logic approach.

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

Environmentally Extended Input–Output Analysis of the UK Economy: Key Sector Analysis

The paper assesses the sustainability of investment in various economic sectors, with the aim of minimizing resource use and generation of emissions. The broad development focus of the paper and the potential for the proposed methodology to be applied in many different countries make it a useful methodological contribution to the global sustainability debate. The UK case is taken for illustration purposes, and (given the availability of the necessary data) this methodology could be applied in countries with various economic structures and specialisations. An environmentally extended static 123-sector UK input–output model is used, linking a range of physical flows (domestic extraction, use of water, and emissions of CO2, CH4, NOx) with the economic structure of the UK. A range of environmentally adjusted forward and backward linkage coefficients has been developed, adjusted according to final demand, domestic extraction, publicly supplied and directly abstracted water, amd emissions of CO2 and NOx,. The data on the final demandadjusted and environmentally adjusted forward and backward linkage coefficients were used in a multi-criteria decision-aid assessment, employing a NAIADE method in three different sustainability settings. The assessment was constructed in such a way that each sector of the UK economy was assessed by means of a panel of sustainability criteria, maximizing economic effects and minimizing environmental effects. This type of multi-criteria analysis, applied here for the first time, could prove to be a valuable basis for similar studies, especially in the developing world, where trade-offs between economic development and environmental protection have been the subject of considerable debate.input–output analysis; environmentally extended; MCDA; key sectors; sustainability; ecological economics; UK

A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/