Optimization of deep learning features for age-invariant face recognition

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods

An Assessment Of Seaport Privatization In Saudi Arabia : A Case Study Utilizing Grounded Theory Approach

Saudi Arabia, one of the late adopters of the privatization as compared to other countries, has embarked on the privatization policy in late 1990s with seaports being one of the earliest sectors that the government opened up for private sector involvement. The purpose of this research is to study the seaports privatization outcome after nearly 10 years of its implementation and to evaluate whether the declared privatization objectives have been achieved and the contributory positive / negative factors, as well as to suggest curative measures to mitigate the shortcomings, if any

Improving environmental sustainability of agriculture in Egypt through a life-cycle perspective

Soil plays an essential role as a habitat, source of nutrients and support for vegetation. Promoting food security and environmental sustainability of agricultural systems requires an integrated approach to soil fertility management. Agricultural activities should be developed with preventive approaches aimed at avoiding or reducing negative impacts on the soil physicochemical and biological properties and the depletion of soil nutrient reserves. In this regard, Egypt has developed the Sustainable Agricultural Development Strategy to encourage environmentally friendly practices among farmers, such as crop rotation and water management, in addition to extending agriculture to desert areas, favoring the socio-economic development of the region. In order to evaluate the outcomes of the plan beyond quantitative data of production, yield, consumption and emissions, the environmental profile of agriculture in Egypt has been assessed under a life-cycle perspective in order to identify the associated environmental burdens and ultimately contribute to improving the sustainability policies of agricultural activity within the framework of a crop rotation system. In particular, a two-year crop rotation (Egyptian clover-maize-wheat) was analyzed in two distinct agricultural areas in Egypt: New Lands in desert regions and Old Lands along the Nile River, traditionally recognized as fertile areas due to the river alluvium and water availability. The New Lands had the worst environmental profile for all impact categories, except for Soil organic carbon deficit and Global potential species loss. Irrigation and on-field emissions associated with mineral fertilization were identified as the most critical hotspots of Egyptian agriculture. In addition, land occupation and land transformation were reported as the main drivers of biodiversity loss and soil degradation, respectively. Beyond these results, further research on biodiversity and soil quality indicators is needed to more accurately assess the environmental damage caused by the conversion of deserts into agricultural areas, given the species richness these regions holdThis research is supported by the project Enhancing diversity in Mediterranean cereal farming systems (CerealMed), funded by PRIMA Programme, FEDER/Ministry of Science and Innovation– Spanish National Research Agency (PCI2020-111978); and Academy of Scientific Research and Technology (ASRT); and the project Transition to sustainable agri-food sector bundling life cycle assessment and ecosystem services approaches (ALISE), funded by the Spanish National Research Agency (TED2021-130309B-I00). S.L.O., M.T.M. and S.G.G belong to the Galician Competitive Research Group (GRC ED431C-2021/37) and to the Cross-disciplinary Research in Environmental Technologies (CRETUS Research Center, ED431E 2018/01)This research is supported by the project Enhancing diversity in Mediterranean cereal farming systems (CerealMed), funded by PRIMA Programme, FEDER/Ministry of Science and Innovation– Spanish National Research Agency (PCI2020-111978); and Academy of Scientific Research and Technology (ASRT); and the project Transition to sustainable agri-food sector bundling life cycle assessment and ecosystem services approaches (ALISE), funded by the Spanish National Research Agency (TED2021-130309B-I00). S.L.O., M.T.M. and S.G.G belong to the Galician Competitive Research Group (GRC ED431C-2021/37) and to the Cross-disciplinary Research in Environmental Technologies (CRETUS Research Center, ED431E 2018/01)S

Organization for Technical Education and Vocational Training (GOTEVT),KSA

Abstract: The present paper introduces a new algorithm for industrial processes by using neural network (N.N). The suggested method aims to make a combination between PLC and N.N. PLC (programmable logic control) is a good method for controlling any industrial processing but it did not have full picture about this process in any time. Neural network can made a survey on the process at all time. Learning N.N can expect the next instruction of the industrial operation before applying the input signal controlling. This paper introduces the new suggested technology presenting the architecture of the control system, and its components. A practical system will be constructed to satisfy the suggested technology. Key words: Neural network, PLC, Learning, Training. Using neural network in control can be classified into two phases:Neural network only as aid i.e help in control and Neural network as controller.The first phase [16][9] (neural network as aid) is possible if three roles are satisfied: The network assists or replaces a conventional model that is based either on a derived goal, such as minimum prediction error of the controlled variable, or directly on the control goal, such as minimization of a performance index,Th

Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia

Abstract Background The health and development of children during their first year of full time school is known to impact their social, emotional, and academic capabilities throughout and beyond early education. Physical health, motor development, social and emotional well-being, learning styles, language and communication, cognitive skills, and general knowledge are all considered to be important aspects of a child’s health and development. It is important for many organisations and governmental agencies to continually improve their understanding of the factors which determine or influence development vulnerabilities among children. This article studies the relationships between development vulnerabilities and educational factors among children in Queensland, Australia. Methods Spatial statistical machine learning models are reviewed and compared in the context of a study of geographic variation in the association between development vulnerabilities and attendance at preschool among children in Queensland, Australia. A new spatial random forest (SRF) model is suggested that can explain more of the spatial variation in data than other approaches. Results In the case study, spatial models were shown to provide a better fit compared to models that ignored the spatial variation in the data. The SRF model was shown to be the only model which can explain all of the spatial variation in each of the development vulnerabilities considered in the case study. The spatial analysis revealed that the attendance at preschool factor has a strong influence on the physical health domain vulnerability and emotional maturity vulnerability among children in their first year of school. Conclusion This study confirmed that it is important to take into account the spatial nature of data when fitting statistical machine learning models. A new spatial random forest model was introduced and was shown to explain more of the spatial variation and provide a better model fit in the case study of development vulnerabilities among children in Queensland. At small-area population level, increased attendance at preschool was strongly associated with reduced physical and emotional development vulnerabilities among children in their first year of school

How Can Smart Mobility Bridge the First/Last Mile Gap? Empirical Evidence on Public Attitudes from Australia

Under the umbrella concept of smart mobility, new transport innovations such as peer-to-peer transport, shared autonomous vehicles, and mobility-as-a-service have been identified for their potential to improve accessibility and bridge the first/last-mile gap between origin, destination, and good quality public transport. Any future mobility plan, nevertheless, will need to appeal to a population reluctant to break habits. This study explores quantitative data collected from major Australian cities to provide a geographic context between attitudes towards smart mobility with a particular focus on eight attitudinal factors—i.e., technology, public transport, sharing, multimodality, peer-to-peer transport, smart phones and apps, environmental consciousness, and reducing private vehicle use. The quantitative analysis disclosed that regardless of location, overcoming private vehicle use, user aversion to multimodality, and reluctance to share rides with strangers’ presence significant barriers to some smart mobility options. Furthermore, respondents in inner ring areas of cities have more positive views towards public transport, the environment, and smart phones, while the middle/outer ring residents on the contrary have more positive views towards private vehicles. The study findings offer policy insights and potential opportunities and challenges associated with the implementation of smart mobility in urban areas

Framework for Traffic Congestion Prediction

Traffic Congestion is a complex dilemma facing most major cities. It has undergone a lot of research since the early 80s in an attempt to predict traffic in the short-term. Recently, Intelligent Transportation Systems (ITS) became an integral part of traffic research which helped in modeling and forecasting traffic conditions. In this paper, two frameworks for traffic congestion prediction are proposed. The first framework is based on NeuroFuzzy model which is well surveyed in traffic literature. The second framework is based on Hidden Markov Models (HMM) which is rarely used in traffic prediction. The methods are used to define traffic congestion during morning rush hours. The results of the two methods are compared. </p