A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Afromontane forest ecosystem studies with multi-source satellite data

The Afromontane Forest of north Eastern Nigeria is an important ecological ecosystem endowed with flora and fauna species. The main goals of this thesis were to explore the potential of multi-source satellite remote sensing for the assessment of the biodiversity-rich Afromontane Forest ecosystem using different methods and algorithms to retrieve two major remote sensing -essential biodiversity variables (RS-EBV) which are related and are also the major determinants of biological and ecosystem stability

ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Aim: Alzheimer's disease is a neurodegenerative disease that causes 60–70% of all cases of dementia. This study is to provide a novel method that can identify AD more accurately.Methods: We first propose a VGG-inspired network (VIN) as the backbone network and investigate the use of attention mechanisms. We proposed an Alzheimer's Disease VGG-Inspired Attention Network (ADVIAN), where we integrate convolutional block attention modules on a VIN backbone. Also, 18-way data augmentation is proposed to avoid overfitting. Ten runs of 10-fold cross-validation are carried out to report the unbiased performance.Results: The sensitivity and specificity reach 97.65 ± 1.36 and 97.86 ± 1.55, respectively. Its precision and accuracy are 97.87 ± 1.53 and 97.76 ± 1.13, respectively. The F1 score, MCC, and FMI are obtained as 97.75 ± 1.13, 95.53 ± 2.27, and 97.76 ± 1.13, respectively. The AUC is 0.9852.Conclusion: The proposed ADVIAN gives better results than 11 state-of-the-art methods. Besides, experimental results demonstrate the effectiveness of 18-way data augmentation

Ancient DNA in Archaeologically Charred Zea Mays L: Prospects and Limitations

Plant remains are an integral part of any archaeological investigation given the large role they play in ancient subsistence economies, medicinal practices, technologies and folklore. However, despite new developments in ancient genetics, research in plant ancient DNA (aDNA) is a relatively young and untouched discipline accounting for less than 7% of all aDNA analyses published in academic literature. As a result, paleoethnobotanists, archaeologists and geneticists have not understood the feasibility and limitations of each other’s field. Few are aware that DNA extraction from charred plant remains is rare and without any kind of standard or working protocol. The possibilities of retrieving aDNA from charred Zea mays L. is considered in this study using modern maize for polymerase chain reaction (PCR) optimization and combining purification methods on ancient samples (1150-1250 AD), resolving the question of whether or not archaeologically charred plants are a viable source for genetic material. The confirmed positive results generate questions about the added-value of maize and how knowledge of genetic attributes can contribute to the growing field of archaeology and ethnobiology while demonstrating the value of these findings as they pertain to the treatment of charred floral remains by archaeologists and First Nation communities

Citizen Observatories and the New Earth Observation Science

Earth observation is diversifying, and now includes new types of systems, such as citizen observatories, unmanned aerial vehicles and wireless sensor networks. However, the Copernicus Programme vision of a seamless chain from satellite data to usable information in the hands of decision makers is still largely unrealized, and remote sensing science lacks a conceptual framework to explain why. This paper reviews the literatures on citizen science, citizen observatories and conceptualization of remote sensing systems. It then proposes a Conceptual Framework for Earth Observation which can be used in a new Earth observation science to explain blockages in the chain from collecting data to disseminating information in any Earth observation system, including remote sensing systems. The framework differs from its predecessors by including social variables as well as technological and natural ones. It is used here, with evidence from successful citizen science projects, to compare the factors that are likely to influence the effectiveness of satellite remote sensing systems and citizen observatories. The paper finds that constraints on achieving the seamless “Copernicus Chain” are not solely technical, as assumed in the new Space Strategy for Europe, but include social constraints too. Achieving the Copernicus Chain will depend on the balance between: (a) the ‘forward’ momentum generated by the repetitive functioning of each component in the system, as a result of automatic operation or human institutions, and by the efficiency of interfaces between components; and (b) the ‘backward’ flow of information on the information needs of end users. Citizen observatories will face challenges in components which for satellite remote sensing systems are: (a) automatic or straightforward, e.g., sensor design and launch, data collection, and data products; and (b) also challenging, e.g., data processing. Since citizen observatories will rely even more on human institutions than remote sensing systems to achieve repetitive functioning, one of their greatest strengths—using a “crowd” of hand-held sensors to cover large areas—could also be one of their greatest weaknesses

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given