Search results for: forest biodiversity

Authors: Prasanna Ramachandran, Kareem Graham, Helena Kiefel, Sunit Jain, Todd DeSantis

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Microbes that reside inside the mammalian GI tract, commonly referred to as the gut microbiome, have been shown to have therapeutic effects in animal models of disease. We hypothesize that specific proteins produced by these microbes are responsible for this activity and may be used directly as therapeutics. To speed up the discovery of these key proteins from the big-data metagenomics, we have applied machine learning techniques. Using amino acid sequences of known epitopes and their corresponding binding partners, protein interaction descriptors (PID) were calculated, making a positive interaction set. A negative interaction dataset was calculated using sequences of proteins known not to interact with these same binding partners. Using Random Forest and positive and negative PID, a machine learning model was trained and used to predict interacting versus non-interacting proteins. Furthermore, the continuous variable, cosine similarity in the interaction descriptors was used to rank bacterial therapeutic candidates. Laboratory binding assays were conducted to test the candidates for their potential as therapeutics. Results from binding assays reveal the accuracy of the machine learning prediction and are subsequently used to further improve the model.

Keywords: protein-interactions, machine-learning, metagenomics, microbiome

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Yeliz Sarı Nayim, B. N. Nayim

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Vertical natural gardens encountered in urban ecosystems are important elements contributing to urban ecology by raising the quality of urban life. This research covers the investigation of the semi-natural plant walls of Bartın city which is located on the western Black Sea coast of Turkey. Landscape analysis and evaluation as a result of land and office work have resulted in vertical garden ecosystems that have been processed in the urban habitat map, mostly in natural stone walls, wooden garden fences, garden entrance doors, historical buildings and building walls. Structural surfaces on old building facades, especially with abandoned or still in use with natural stone walls, have been found to have many natural vertical gardens over time. Parietaria judaica, Cymbalaria longipes and Hedera helix species were dominant, and other types of content were recorded, providing information on the current biotope potential, human activities and effects on them. It has been emphasized that the described vertical gardens together with the species they contain should be protected in terms of Bartin urban ecology and biodiversity. It has been stated that sustainable urban planning, design and management should be considered as a compensation for open and green area losses.

Keywords: semi-natural vertical gardens, urban ecology, sustainable urban planning and design, Bartın

Procedia PDF Downloads 355

Authors: Isa Muhammad Zumo

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The African Great Green Wall (GGW) is a significant initiative in northern Nigeria because it promotes land restoration and conservation utilizing both commercial and species of forest trees while also helping to mitigate desertification and hazards from the sand dunes and shifting Sahara deserts. Conflicts and weather, however, pose a significant danger to the achievement of these goals. The scientific method for monitoring the vegetation dynamics since inception has not received the required attention, despite the African Development Bank (ADB)'s help in funding the project and its integration into the state's development plans for GGW initiatives. This study will monitor the changes in the vegetation cover of the great green wall within Yobe State Nigeria from 2014 to 2023. The vegetation dynamics will be monitored using Landsat 8 Operational Land Imager (OLI) for 6 years at 2 years intervals. The result will show the fluctuations in the vegetation cover density within the period of study. This will guide the design and implementation of policies of the GGW in achieving its objectives. The result can also contribute to the realization of Sustainable Development Goal (SDG) Target 13.2: Integrate climate change measures into national policies, strategies, and planning.

Keywords: monitoring, green wall, Landsat 8, Nigeria

Procedia PDF Downloads 87

Authors: Regina Streltsov

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The Archipelago of the Azores in the NE Atlantic is a hot spot of marine biodiversity, both pelagic and demersal. Epinephelus marginatus is a solitary species commonly observed in these waters, with distinct territorial/residential behaviors from their post- larva and juvenile stages to the adult phase. Being commercially high valued species, about 13% of all groupers (Family Epinephelidae) face an increasing pressure that has produced known impacts in both the abundance and distribution of this group of fishes. Epinephelus marginatus is currently assessed by the IUCN as a vulnerable species. Dusky gropers inhabit rocky bottoms from shallow waters down to 200 m. Juveniles are usually found in shallow shoreline waters. Population dynamics of juveniles can lead to a better understanding of the competition for resources and predation and further conservation measures that must be taken upon dusky groupers. This study is carried out in rocky reefs from two sheltered bays on the south and north coast of the island in two different spots with four sampling sites in total. Using Transects individuals are counted at the peak of high tide and all abiotic factors are recorded. Our goal is to complete a statistically significant number of observations in order to detail these populations and to better understand their dynamics and dimension.

Keywords: Azores, dusky groupers, Epinephelus marginatus, population dynamics

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Authors: Zhang Hui, Wu Caiqiu, Yuan Xuyin, Qiu Jie, Zhang Hanpei

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The heavy metal contaminations were determined with a detailed soil survey in roadside soils along Shenyang-Dalian Highway of Liaoning Province (China) and Pb, Cu, Cd, Ni and Zn were analyzed using the atomic absorption spectrophotometric method. The average concentration of Pb, Cu, Cd, Ni and Zn in roadside soils was determined to be 43.8, 26.5, 0.119, 32.1, 71.3 mg/kg respectively, and all of the heavy metal contents were higher than the background values. Different heavy metal distribution regularity was found in different land use type of roadside soil, there was an obvious peak of heavy concentration at 25m from road edge in the farmland, while in the forest and orchard soil, all heavy metals gradually decreased with the increase of distance from road edge and conformed to the exponential model. Furthermore, the heavy metal contents of heavy metals except Cd were markedly increased compared with those in 1999 and 2007, and the heavy metals concentrations of Shenyang- Dalian Highway were considered medium or low in comparison with those in other cities around the world. The assessment of heavy metal contamination of roadside soils illustrated a common low pollution for all heavy metal and recommended that more attention should be paid to Pb contamination in roadside soils in Shenyang-Dalian Highway.

Keywords: heavy metal contamination, roadside, highway, Nemerow Pollution Index

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Authors: Amritha Nair, Fleur Visser, Ian Maddock, Jonas Schoelynck

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Inland waters such as streams sustain a rich variety of species and are essentially hotspots for biodiversity. Submerged aquatic vegetation, also known as SAV, forms an important part of ecologically healthy river systems. Direct and indirect human influences, such as climate change are putting stress on aquatic plant communities, ranging from the invasion of non-native species and grazing, to changes in the river flow conditions and temperature. There is a need to monitor SAV, because they are in a state of deterioration and their disappearance will greatly impact river ecosystems. Like terrestrial plants, SAV can show visible signs of stress. However, the techniques used to map terrestrial vegetation from its spectral reflectance, are not easily transferable to a submerged environment. Optical remote sensing techniques are employed to detect the stress from remotely sensed images through multispectral imagery and Structure from Motion photogrammetry. The effect of the overlying water column in the form of refraction, attenuation of visible and near infrared bands in water, as well as highly moving targets, are NIR) key challenges that arise when remotely mapping SAV. This study looks into the possibility of mapping the changes in spectral signatures from SAV and their response to certain stresses.

Keywords: submerged aquatic vegetation, structure from motion, photogrammetry, multispectral, spectroscopy

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Authors: Prashant Verma

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Commercial banks are facing immense pressure, including financial disintermediation, interest rate volatility and digital ways of finance. Retaining an existing customer is 5 to 25 less expensive than acquiring a new one. This paper explores customer churn prediction, based on various statistical & machine learning models and uses under-sampling, to improve the predictive power of these models. The results show that out of the various machine learning models, Random Forest which predicts the churn with 78% accuracy, has been found to be the most powerful model for the scenario. Customer vintage, customer’s age, average balance, occupation code, population code, average withdrawal amount, and an average number of transactions were found to be the variables with high predictive power for the churn prediction model. The model can be deployed by the commercial banks in order to avoid the customer churn so that they may retain the funds, which are kept by savings bank (SB) customers. The article suggests a customized campaign to be initiated by commercial banks to avoid SB customer churn. Hence, by giving better customer satisfaction and experience, the commercial banks can limit the customer churn and maintain their deposits.

Keywords: savings bank, customer churn, customer retention, random forests, machine learning, under-sampling

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Authors: Punwath Prum

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Superfund site is recognized publicly to be a severe environmental problem to surrounding communities and biodiversity due to its hazardous chemical waste from industrial activities. It contaminates the soil and water but also is a leading potential point-source pollution affecting ecosystem in watershed areas from chemical substances. The risks of Superfund site on watershed can be effectively measured by utilizing publicly available data and geospatial analysis by free and open source application. This study analyzed the vegetation change within high risked contaminated watersheds in Wisconsin. The high risk watersheds were measured by which watershed contained high number Superfund sites. The study identified two potential risk watersheds in Lafayette and analyzed the temporal changes of vegetation within the areas based on Normalized difference vegetation index (NDVI) analysis. The raster statistic was used to compare the change of NDVI value over the period. The analysis results showed that the NDVI value within the Superfund sites’ boundary has a significant lower value than nearby surrounding and provides an analogy for environmental hazard affect by the chemical contamination in Superfund site.

Keywords: soil contamination, spatial analysis, watershed

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Authors: Mário Ernesto Sitoe, Orlando Zacarias

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University education is influenced by several factors that range from the adoption of strategies to strengthen the whole process to the academic performance improvement of the students themselves. This work uses data mining techniques to develop a predictive model to identify students with a tendency to evasion and retention. To this end, a database of real students’ data from the Department of University Admission (DAU) and the Department of Mathematics and Informatics (DMI) was used. The data comprised 388 undergraduate students admitted in the years 2009 to 2014. The Weka tool was used for model building, using three different techniques, namely: K-nearest neighbor, random forest, and logistic regression. To allow for training on multiple train-test splits, a cross-validation approach was employed with a varying number of folds. To reduce bias variance and improve the performance of the models, ensemble methods of Bagging and Stacking were used. After comparing the results obtained by the three classifiers, Logistic Regression using Bagging with seven folds obtained the best performance, showing results above 90% in all evaluated metrics: accuracy, rate of true positives, and precision. Retention is the most common tendency.

Keywords: evasion and retention, cross-validation, bagging, stacking

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Authors: Liya Abraham

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Medicinal plants are globally valuable sources of herbal products, either as lifesaving or life maintaining medicines. Studies reveal that more than 25% of modern drugs in the world are derived from plants. The Horn of Africa as a world hotspot; it has more than 1500 endemic plants. Eritrea, a country located in the Horn of Africa, is blessed with medicinal flora and fauna and marine and terrestrial biodiversity. Previous studies of flora of Ethiopia and Eritrea, incomplete species lists, indicate figures ranging between 6000 and 7000 species, with levels of endemism between 12–20%. In the past two decades, there has been growing interest in natural remedy herbal medicines owing to, but not limited to; resistance to antimicrobials, intolerance of side effects of modern drugs, and rise in chronic diseases like diabetes, hypertension, cancer, etc. Hence, owing to the rising demand for nature based health solutions, deforestation, construction purposes, grazing, and agricultural expansion; several medicinal plants in general and the endemic ones, in particular, are in the verge of extinction. Therefore, conservation strategies of endangered and endemic medicinal plants, especially those located in hot spot regions, must be promoted at global level. Thus, the author aims to share certain information regarding the endemic medicinal plants in Eritrea with the international scientific world.

Keywords: endemic, eritrea, horn of Africa, medicinal plants, species

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Authors: Yunwei Tang, Linhai Jing, Hui Li, Qingjie Liu, Xiuxia Li, Qi Yan, Haifeng Ding

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The giant panda (Ailuropoda melanoleuca) is an endangered species, mainly live in central China, where bamboos act as the main food source of wild giant pandas. Knowledge of spatial distribution of bamboos therefore becomes important for identifying the habitat of giant pandas. There have been ongoing studies for mapping bamboos and other tree species using remote sensing. WorldView-2 (WV-2) is the first high resolution commercial satellite with eight Multi-Spectral (MS) bands. Recent studies demonstrated that WV-2 imagery has a high potential in classification of tree species. The advanced classification techniques are important for utilising high spatial resolution imagery. It is generally agreed that object-based image analysis is a more desirable method than pixel-based analysis in processing high spatial resolution remotely sensed data. Classifiers that use spatial information combined with spectral information are known as contextual classifiers. It is suggested that contextual classifiers can achieve greater accuracy than non-contextual classifiers. Thus, spatial correlation can be incorporated into classifiers to improve classification results. The study area is located at Wuyipeng area in Wolong, Sichuan Province. The complex environment makes it difficult for information extraction since bamboos are sparsely distributed, mixed with brushes, and covered by other trees. Extensive fieldworks in Wuyingpeng were carried out twice. The first one was on 11th June, 2014, aiming at sampling feature locations for geometric correction and collecting training samples for classification. The second fieldwork was on 11th September, 2014, for the purposes of testing the classification results. In this study, spectral separability analysis was first performed to select appropriate MS bands for classification. Also, the reflectance analysis provided information for expanding sample points under the circumstance of knowing only a few. Then, a spatially weighted object-based k-nearest neighbour (k-NN) classifier was applied to the selected MS bands to identify seven land cover types (bamboo, conifer, broadleaf, mixed forest, brush, bare land, and shadow), accounting for spatial correlation within classes using geostatistical modelling. The spatially weighted k-NN method was compared with three alternatives: the traditional k-NN classifier, the Support Vector Machine (SVM) method and the Classification and Regression Tree (CART). Through field validation, it was proved that the classification result obtained using the spatially weighted k-NN method has the highest overall classification accuracy (77.61%) and Kappa coefficient (0.729); the producer’s accuracy and user’s accuracy achieve 81.25% and 95.12% for the bamboo class, respectively, also higher than the other methods. Photos of tree crowns were taken at sample locations using a fisheye camera, so the canopy density could be estimated. It is found that it is difficult to identify bamboo in the areas with a large canopy density (over 0.70); it is possible to extract bamboos in the areas with a median canopy density (from 0.2 to 0.7) and in a sparse forest (canopy density is less than 0.2). In summary, this study explores the ability of WV-2 imagery for bamboo extraction in a mountainous region in Sichuan. The study successfully identified the bamboo distribution, providing supporting knowledge for assessing the habitats of giant pandas.

Keywords: bamboo mapping, classification, geostatistics, k-NN, worldview-2

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Authors: Budoor Al Abid

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Educational systems are increasingly provided as open online services, providing guidance and support for individual learners. To adapt the learning systems, a proper evaluation must be made. This paper builds the evaluation model Fuzzy C Means Adaptive System (FCMAS) based on data mining techniques to assess the difficulty of the questions. The following steps are implemented; first using a dataset from an online international learning system called (slepemapy.cz) the dataset contains over 1300000 records with 9 features for students, questions and answers information with feedback evaluation. Next, a normalization process as preprocessing step was applied. Then FCM clustering algorithms are used to adaptive the difficulty of the questions. The result is three cluster labeled data depending on the higher Wight (easy, Intermediate, difficult). The FCM algorithm gives a label to all the questions one by one. Then Random Forest (RF) Classifier model is constructed on the clustered dataset uses 70% of the dataset for training and 30% for testing; the result of the model is a 99.9% accuracy rate. This approach improves the Adaptive E-learning system because it depends on the student behavior and gives accurate results in the evaluation process more than the evaluation system that depends on feedback only.

Keywords: machine learning, adaptive, fuzzy logic, data mining

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Authors: Aldrin R. Logdat

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The insights and wisdom of the Alangan Mangyans offer valuable guidance for developing alternative ecological and development frameworks. Their reverence for the sacredness of the land, rooted in their traditional cosmology, guides their harmonious relationship with nature. Through their practice of swidden farming, ecosystem preservation takes precedence as they carefully manage agricultural activities and allow for forest regeneration. This approach aligns with natural processes, reflecting their profound understanding of the natural world. Similar to early advocates like Aldo Leopold, the emphasis is on shifting our perception of land from a commodity to a community. The indigenous wisdom of the Alangan Mangyans provides practical and sustainable approaches to preserving the interdependence of the biotic community and ecosystems. By integrating their cultural heritage, we can transcend the prevailing anthropocentric mindset and foster a meaningful and sustainable connection with nature. The revitalization of cultural energy and the embrace of alternative frameworks require learning from indigenous peoples like the Alangan Mangyans, where reverence for the land and the recognition of the interconnectedness between humanity and nature are prioritized. This paves the way for a future where harmony with nature and the well-being of the Earth community prevail.

Keywords: Alangan Mangyans, ecological frameworks, sacredness of the land, cultural energy

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Authors: Raahil Sheikh, Abhishek Maurya, Priya Gujjar, Himanshu Dwivedi, Prathamesh Minde

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UAVs have been an initial member of our environment since it's the first used by Austrian warfare in Venice. At that stage, they were just pilotless balloons equipped with bombs to be dropped on enemy territory. Over time, technological advancements allowed UAVs to be controlled remotely or autonomously. This study shall mainly focus on the intensification of pre-existing manual drones equipping them with a variety of sensors and making them autonomous, and capable, and purposing them for a variety of roles, including thermal sensing, data collection, tracking creatures, forest fires, volcano detection, hydrothermal studies, urban heat, Island measurement, and other environmental research. The system can also be used for reconnaissance, research, 3D mapping, and search and rescue missions. This study mainly focuses on automating tedious tasks and reducing human errors as much as possible, reducing deployment time, and increasing the overall efficiency, efficacy, and reliability of the UAVs. Creation of a comprehensive Ground Control System UI (GCS) enabling less trained professionals to be able to use the UAV with maximum potency. With the inclusion of such an autonomous system, artificially intelligent paths and environmental gusts and concerns can be avoided.

Keywords: UAV, drone, autonomous system, thermal imaging

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

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Authors: Raahil Sheikh, Prathamesh Minde, Priya Gujjar, Himanshu Dwivedi, Abhishek Maurya

Abstract:

UAVs have been an initial member of our environment since it's the first used by Austrian warfare in Venice. At that stage, they were just pilotless balloons equipped with bombs to be dropped on enemy territory. Over time, technological advancements allowed UAVs to be controlled remotely or autonomously. This study shall mainly focus on the intensification of pre-existing manual drones equipping them with a variety of sensors and making them autonomous, and capable, and purposing them for a variety of roles, including thermal sensing, data collection, tracking creatures, forest fires, volcano detection, hydrothermal studies, urban heat, Island measurement, and other environmental research. The system can also be used for reconnaissance, research, 3D mapping, and search and rescue missions. This study mainly focuses on automating tedious tasks and reducing human errors as much as possible, reducing deployment time, and increasing the overall efficiency, efficacy, and reliability of the UAVs. Creation of a comprehensive Ground Control System UI (GCS) enabling less trained professionals to be able to use the UAV with maximum potency. With the inclusion of such an autonomous system, artificially intelligent paths and environmental gusts and concerns can be avoided

Keywords: UAV, autonomous systems, drones, geo thermal imaging

Procedia PDF Downloads 86

Authors: Cherak Imen, Sellami Mehdi

Abstract:

The Atlas cedar, Cedrus atlantica, is endemic to the mountains of North Africa. This is one of the most valuable softwood, both economically, ecologically, aesthetically and culturally. In Algeria, the cedar forests currently have worrying symptoms of decline which therefore require special monitoring. Fungal endophytes are involved in various diseases of the Atlas cedar. They attack all organs on which they cause many symptoms. These microflora live in complex interaction with plants. In this study, we identified a total of 09 mycotaxons collected needles Cedarwood at three stations with different degrees of decline (Talmet, Boumerzoug and Tuggurt) in the National Park Belezma (Batna, Algeria). The study conducted on a total of 12 trees were identified 08 mycoendophytes in Talmet station, 04 species in the Boumerzoug station and 03 in Tuggurt station. The total species richness mycoendophytes depending on the types of cedar forests showed that the largest diversity was recorded at the cedar forest healthy, Alternaria is the most common type in all stations. This work should be completed by further detailed studies to identify other endophyte species and better know its interactions with the Atlas cedar.

Keywords: Cedrus atlantica, endophytic fungi, microflora, mycotaxons, mycoendophyte

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Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

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The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

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Authors: Khalid Al Mutairi, Mashhor Mansor, Magdy El-Bana, Saud L. Al-Rowaily, Asyraf Mansor

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The present study was carried out in 20 islands of Farasan Archipelago in Saudi Arabia to describe the biogeography patterns of plants. A total of 191 species belonging to 129 genera and 53 families were identified. Following island biogeography theory, total plant species richness and their ecological groups were positively influenced by island size, number of habitats,elevation and were not affected by isolation. The high level of nestedness, the strong effect of area on total plant species richness and ecological groups, and the similarity of vegetation composition on the islands has several implications for conservation. In conclusion the large and richest islands in Farasan Archipelago such as Farasan Alkbir would conserve higher diversity than several smaller islands. This island also includes rare habitats like coral rocks and rare species. The invasion of the unique habitats such as wadi channels and water catchments in this island by the exotic tree Prosopis juliflora should be managed to conserve the native biodiversity. The protection of such critical habitats is very important on the other large island (e.g. Zufaf), due to their limited distribution in the country.

Keywords: island biogeography, conservation, farasan archipelago, saudi arabia, plant diversity

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Authors: Rabia Munsaf Khan, Eshrat Fatima

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The analysis of the spatial extent and temporal change of vegetation cover using remotely sensed data is of critical importance to agricultural sciences. Pakistan, being an agricultural country depends on this resource as it makes 70% of the GDP. The case study is of Lal Suhanra National Park, which is not only the biggest forest reserve of Pakistan but also of Asia. The study is performed using different temporal images of Landsat. Also, the results of Landsat are cross-checked by using Sentinel-2 imagery as it has both higher spectral and spatial resolution. Vegetation can easily be detected using NDVI which is a common and widely used index. It is an important vegetation index, widely applied in research on global environmental and climatic change. The images are then classified to observe the change occurred over 15 years. Vegetation cover maps of 2000 and 2016 are used to generate the map of vegetation change detection for the respective years and to find out the changing pattern of vegetation cover. Also, the NDVI values aided in the detection of percentage decrease in vegetation cover. The study reveals that vegetation cover of the area has decreased significantly during the year 2000 and 2016.

Keywords: Landsat, normalized difference vegetation index (NDVI), sentinel 2, Greenland monitoring

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Authors: Shivam Dwivedi, Sumit Prakash Gupta, Durga Toshniwal

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It is a challenging task in today’s date to keep defence forces in the highest state of combat readiness with budgetary constraints. A huge amount of time and money is squandered in the unnecessary and expensive traditional maintenance activities. To overcome this limitation Defence Intelligent Mission Readiness and Maintenance Scheduling System has been proposed, which ameliorates the maintenance system by diagnosing the condition and predicting the maintenance requirements. Based on new data mining algorithms, this system intelligently optimises mission readiness for imminent operations and maintenance scheduling in repair echelons. With modified data mining algorithms such as Weighted Feature Ranking Genetic Algorithm and SVM-Random Forest Linear ensemble, it improves the reliability, availability and safety, alongside reducing maintenance cost and Equipment Out of Action (EOA) time. The results clearly conclude that the introduced algorithms have an edge over the conventional data mining algorithms. The system utilizing the intelligent condition-based maintenance approach improves the operational and maintenance decision strategy of the defence force.

Keywords: condition based maintenance, data mining, defence maintenance, ensemble, genetic algorithms, maintenance scheduling, mission capability

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Authors: Jorge R. Santos, Pedro Sebastiao

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In recent years there has been a great growth in the use of drones, being used in several areas such as security, agriculture, or research. The existence of some systems that allow the remote control of drones is a reality; however, these systems are quite simple and directed to specific functionality. This paper proposes the development of a web platform made in Vue.js and Node.js to control, manage, and monitor drones in real time. Using a microservice architecture, the proposed project will be able to integrate algorithms that allow the optimization of processes. Communication with remote devices is suggested via HTTP through 3G, 4G, and 5G networks and can be done in real time or by scheduling routes. This paper addresses the case of forest fires as one of the services that could be included in a system similar to the one presented. The results obtained with the elaboration of this project were a success. The communication between the web platform and drones allowed its remote control and monitoring. The incorporation of the fire detection algorithm in the platform proved possible a real time analysis of the images captured by the drone without human intervention. The proposed system has proved to be an asset to the use of drones in fire detection. The architecture of the application developed allows other algorithms to be implemented, obtaining a more complex application with clear expansion.

Keywords: drone control, microservices, node.js, unmanned aerial vehicles, vue.js

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

Procedia PDF Downloads 113

Authors: Nour El Houda Bensiradj, Nafila Zouaghi, Taha Bensiradj

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The pollution of water resources is characterized by the presence of microorganisms, chemicals, or industrial waste. Generally, this waste generates effluents containing large quantities of heavy metals, making the water unsuitable for consumption and causing the death of aquatic life and associated biodiversity. Currently, it is very important to assess the impact of heavy metals in water pollution as well as the processes for treating and reducing them. Among the methods of water treatment and disinfection, we mention the complexation of metal ions using ligands which serve to precipitate and subsequently eliminate these ions. In this context, we are interested in the study of complexes containing heavy metals such as zinc, nickel, and cadmium, which are present in several industrial discharges and are discharged into water sources. We will use the ligands of triethanolamine (TEA) and nitrilotriacetic acid (NTA). The theoretical study is based on molecular modeling, using the density functional theory (DFT) implemented in the Gaussian 09 program. The geometric and energetic properties of the above complexes will be calculated. Spectral properties such as infrared, as well as reactivity descriptors, and thermodynamic properties such as enthalpy and free enthalpy will also be determined.

Keywords: heavy metals, NTA, TEA, DFT, IR, reactivity descriptors

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Authors: Y. Fekir, K. Mederbal, M. A. Hammadouche, D. Anteur

Abstract:

The cultural park of the Tassili, occupying a large area of Algeria, is characterized by a rich vegetative biodiversity to be preserved and managed both in time and space. The management of a large area (case of Tassili), by its complexity, needs large amounts of data, which for the most part, are spatially localized (DEM, satellite images and socio-economic information etc.), where the use of conventional and traditional methods is quite difficult. The remote sensing, by its efficiency in environmental applications, became an indispensable solution for this kind of studies. Multispectral imaging sensors have been very useful in the last decade in very interesting applications of remote sensing. They can aid in several domains such as the de¬tection and identification of diverse surface targets, topographical details, and geological features. In this work, we try to extract vegetative areas using fusion techniques between data acquired from sensor on-board the Earth Observing 1 (EO-1) satellite and Landsat ETM+ and TM sensors. We have used images acquired over the Oasis of Djanet in the National Park of Tassili in the south of Algeria. Fusion technqiues were applied on the obtained image to extract the vegetative fraction of the different classes of land use. We compare the obtained results in vegetation end member extraction with vegetation indices calculated from both Hyperion and other multispectral sensors.

Keywords: Landsat ETM+, EO1, data fusion, vegetation, Tassili, Algeria

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Authors: Kitda Praraththajariya

Abstract:

The main purpose of this research was to study how to communicate the identity of the Mueang district, SamutSongkram province for ecotourism. The qualitative data was collected through studying related materials, exploring the area, in-depth interviews with three groups of people: three directly responsible officers who were key informants of the district, twenty foreign tourists and five Thai tourist guides. A content analysis was used to analyze the qualitative data. The two main findings of the study were as follows: (1) The identity of Amphur (District) Mueang, SamutSongkram province. This establishment was near the Mouth of Maekong River for normal people and tourists, consisting of rest accommodations. There are restaurants where food and drinks are served, rich mangrove forests, Hoy Lod (Razor Clam) and mangrove trees. Don Hoy Lod, is characterized by muddy beaches, is a coastal wetland for Ramsar Site. It is at 1099th ranging where the greatest number of Hoy Lod (Razor Clam) can be seen from March to May each year. (2) The communication of the identity of AmphurMueang, SamutSongkram province which the researcher could find and design to present in English materials can be summed up in 4 items: 1) The history of AmphurMueang, SamutSongkram province 2) WatPhetSamutWorrawihan 3) The Learning source of Ecotourism: Don Hoy Lod and Mangrove forest 4) How to keep AmphurMueang, SamutSongkram province for ecotourism.

Keywords: foreigner tourists, signified, semiotics, ecotourism

Procedia PDF Downloads 305

Authors: Nyasha Agnes Gurira, Petronella Katekwe

Abstract:

Traditional management systems (TMS) are a vital source of knowledge for conserving cultural and natural heritage. TMS’s are renowned for their ability to preserve both tangible and intangible manifestations of heritage. They are a construct of the intricate relationship that exists between heritage and host communities, where communities are recognized as owners of heritage and so, set up management mechanisms to ensure its adequate conservation. Multiple heritage condition surveys were conducted to assess the effectiveness of using TMS in the conservation of both natural and cultural heritage. Surveys were done at Nharira Hills, Mahwemasimike, Dzimbahwe, Manjowe Rock art sites and Norumedzo forest which are heritage places in Zimbabwe. It assessed the state of conservation of the five case studies and assessed the role that host communities play in the management of these heritage places. It was revealed that TMS’s are effective in the conservation of natural heritage, however in relation to heritage forms with cultural manifestations, there are major disparities. These range from differences in appreciation and perception of value within communities leading to vandalism, over emphasis in the conservation of the intangible element as opposed to the tangible. This leaves the tangible element at risk. Despite these issues, TMS are a reliable knowledge base which enables more holistic conservation approaches for cultural and natural heritage.

Keywords: communities, cultural intangible, tangible heritage, traditional management systems, natural

Procedia PDF Downloads 565

Authors: Luís Pina

Abstract:

The conservation of marine biodiversity keeps ecosystems in balance and ensures the sustainable use of resources. In this context, technological resources have been used for monitoring marine species to allow biologists to obtain data in real-time. There are different mobile applications developed for data collection for monitoring purposes, but these systems are designed to be utilized only on third-generation (3G) phones or smartphones with Internet access and in rural parts of the developing countries, Internet services and smartphones are scarce. Thus, the objective of this work is to develop a system to monitor marine turtles using Unstructured Supplementary Service Data (USSD), which users can access through basic mobile phones. The system aims to improve the data collection mechanism and enhance the effectiveness of current systems in monitoring sea turtles using any type of mobile device without Internet access. The system will be able to report information related to the biological activities of marine turtles. Also, it will be used as a platform to assist marine conservation entities to receive reports of illegal sales of sea turtles. The system can also be utilized as an educational tool for communities, providing knowledge and allowing the inclusion of communities in the process of monitoring marine turtles. Therefore, this work may contribute with information to decision-making and implementation of contingency plans for marine conservation programs.

Keywords: GSM, marine biology, marine turtles, unstructured supplementary service data (USSD)

Procedia PDF Downloads 206

Authors: Aya Salama

Abstract:

Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

Procedia PDF Downloads 89