Search results for: munessa forest

Authors: Bryan Clark B. Hernandez, Eugene C. Herrera, Kazuo Nadaoka

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Mangrove forests thriving in intertidal zones in tropical and subtropical regions of the world offer a range of ecosystem services including carbon storage and sequestration. They can regulate the detrimental effects of climate change due to carbon releases two to four times greater than that of mature tropical rainforests. Moreover, they are effective natural defenses against storm surges and tsunamis. However, their proliferation depends significantly on the prevailing hydroperiod at the coast. In the Philippines, these coastal ecosystems have been severely threatened with a 50% decline in areal extent observed from 1918 to 2010. The highest decline occurred in 1950 - 1972 when national policies encouraged the development of fisheries and aquaculture. With the intensive land use conversion upstream, changes in the freshwater-saltwater envelope at the coast may considerably impact mangrove growth conditions. This study investigates a developing urban watershed in Kalibo, Aklan province with a 220-hectare mangrove forest replanted for over 30 years from coastal mudflats. Since then, the mangrove forest was sustainably conserved and declared as protected areas. Hybrid land cover classification technique was used to classify Landsat images for years, 1990, 2010, and 2017. Digital elevation model utilized was Interferometric Synthetic Aperture Radar (IFSAR) with a 5-meter resolution to delineate the watersheds. Using numerical modelling techniques, the hydrologic and hydraulic analysis of the influence of land cover change to flow and sediment dynamics was simulated. While significant land cover change occurred upland, thereby increasing runoff and sediment loads, the mangrove forests abundance adjacent to the coasts for the urban watershed, was somehow sustained. However, significant alteration of the coastline was observed in Kalibo through the years, probably due to the massive land-use conversion upstream and significant replanting of mangroves downstream. Understanding the hydrologic-hydraulic response of these watersheds to change land cover is essential to helping local government and stakeholders facilitate better management of these mangrove ecosystems.

Keywords: coastal mangroves, hydrologic model, land cover change, Philippines

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Authors: Mohamad R. Moshtagh, Ahmad Bagheri

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Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

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Authors: Kalangi Rodrigo

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Sri Lanka has been known as an island which has a diverse variety of prehistoric occupation among ecological zones. Defining the paleoecology of the past societies has been an archaeological thought developed in the 1960s. It is mainly concerned with the reconstruction from available geological and biological evidence of past biota, populations, communities, landscapes, environments, and ecosystems. Sri Lanka has dealt with this subject, and considerable research has been already undertaken. The fossil and material record of Sri Lanka’s Wet Zone tropical forests continues from c. 38,000–34,000 ybp. This early and persistent human fossil, technical, and cultural florescence, as well as a collection of well-preserved tropical-forest rock shelters with associated 'on-site' palaeoenvironmental records, makes Sri Lanka a central and unusual case study to determine the extent and strength of early human tropical forest encounters. Excavations carried out in prehistoric caves in the low country wet zone has shown that in the last 50,000 years, the temperature in the lowland rainforests has not exceeded 5°C. When taking Batadombalena alone, the entire seven layers have yielded an uninterrupted occupation of Acavus sp and Canerium zeylanicum, a plant that grows in the middle of the rainforest. Acavus, which is highly sensitive to rainforest ecosystems, has been well documented in many of the lowland caves, confirming that the wetland rainforest environment has remained intact at least for the last 50,000 years. If the dry and arid conditions in the upper hills regions affected the wet zone, the Tufted Gray Lunger (semnopithecus priam), must also meet with the prehistoric caves in the wet zone thrown over dry climate. However, the bones in the low country wet zone do not find any of the fragments belonging to Turfed Gray Lunger, and prehistoric human consumption is bestowed with purple-faced leaf monkey (Trachypithecus vetulus) and Toque Macaque (Macaca Sinica). The skeletal remains of Lyriocephalus scutatus, a full-time resident in rain forests, have also been recorded among lowland caves. But, in zoological terms, these remains may be the remains of the Barking deer (Muntiacus muntjak), which is currently found in the wet zone. For further investigations, the mtDNA test of genetic diversity (Bottleneck effect) and pollen study from lowland caves should determine whether the wet zone climate has persisted over the last 50,000 years, or whether the dry weather affected in the mountainous region has invaded the wet zone.

Keywords: paleoecology, prehistory, zooarchaeology, reconstruction, palaeo-climate

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Authors: Mashad Maramaei, Behrooz Chogan, Reza Ahmadi

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Human health and the health of his watershed are inseparable. This is because a watershed is an interconnected system of "land", "water", "air" and "life". Nowadays, most of the world's watersheds show symptoms of unhealthiness and require a prompt solution. It is believed that suitable solution is a participatory and Integrated Watershed Management (IWM). In recent decades the Hyrcanian forests in the north of Iran, which belongs to the end of the third geological era, are suffering from many environmental challenges such as land degradation, increasing trends of flood, drought and accelerated soil erosion. These challenges in the main forested area of the country impose many tangible and intangible damages and human losses. This is despite the fact that in the past decades, forestry programs, watershed management and other activities in the region have been implemented in a parallel and uncoordinated manner. Therefore, recently; the Natural Resources and Watershed Management Organization has resorted to the concept of IWM planning the Hyrcanian watersheds. The Chelchai watershed as mostly degraded watershed in the eastern part of the Hyrcanian forests has been selected as a pilot watershed for implementation of the IWM. It has a drainage area of 25680 hectares and receives an average annual precipitation of 650 mm. In this mountainous region, the average temperature is 17.3 degrees Celsius. About 34% of the watershed is under cultivation, 64% under forest cover, 2% under built up areas and etc. In this research, the effectiveness or ineffectiveness of the IWM model implementation of the Natural Resources and Watershed Management Organization has been evaluated based on questionnaire method and field studies. The results indicated that IWM activities in the study area should be reconsidered and revived. Based on this research and the lessons learned during five years' experience in the Chelchai watershed; authors believe that seven important tasks are necessary for socially acceptable and successful implementation of IWM projects. These are: 1) Establishment of Local Coordination Committee (LCC) at the watershed level 2) working for development of a IWM law among government organizations to organize watershed management and eliminate parallel and contradictory activities 3) More investment on education of local communities, especially women and children 4) Development of trust builder and pattern projects that showing best agricultural and livestock management activities at each of 26 villages 5) Assigning forest protection to local communities. 6) Capacity building of government stakeholders. 7) Helping in the marketing of watershed products.

Keywords: integrated watershed management, Chelchai, Hyrcanian forests, Iran

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Authors: Elena Listo, Miguel Marchamalo

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This research studies the soil properties located in the watershed of Jabillo River in the Guanacaste province, Costa Rica. The soils are classified as Alfisols (T. Haplustalfs), in the flatter parts with grazing as Fluventic Haplustalfs or as a consequence of bad drainage as F. Epiaqualfs. The objective of this project is to define the status of the soil, to use remote sensing as a tool for analyzing the evolution of land use and determining the water balance of the watershed in order to improve the efficiency of the water collecting systems. Soil samples were analyzed from trial pits taken from secondary forests, degraded pastures, mature teak plantation, and regrowth -Tectona grandis L. F.- species developed favorably in the area. Furthermore, to complete the study, infiltration measurements were taken with an artificial rainfall simulator, as well as studies of soil compaction with a penetrometer, in points strategically selected from the different land uses. Regarding remote sensing, nearly 40 data samples were collected per plot of land. The source of radiation is reflected sunlight from the beam and the underside of leaves, bare soil, streams, roads and logs, and soil samples. Infiltration reached high levels. The majority of data came from the secondary forest and mature planting due to a high proportion of organic matter, relatively low bulk density, and high hydraulic conductivity. Teak regrowth had a low rate of infiltration because the studies made regarding the soil compaction showed a partial compaction over 50 cm. The secondary forest presented a compaction layer from 15 cm to 30 cm deep, and the degraded pasture, as a result of grazing, in the first 15 cm. In this area, the alfisols soils have high content of iron oxides, a fact that causes a higher reflectivity close to the infrared region of the electromagnetic spectrum (around 700mm), as a result of clay texture. Specifically in the teak plantation where the reflectivity reaches values of 90 %, this is due to the high content of clay in relation to others. In conclusion, the protective function of secondary forests is reaffirmed with regards to erosion and high rate of infiltration. In humid climates and permeable soils, the decrease of runoff is less, however, the percolation increases. The remote sensing indicates that being clay soils, they retain moisture in a better way and it means a low reflectivity despite being fine texture.

Keywords: alfisols, Costa Rica, infiltration, remote sensing

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Authors: Hocine Benabid, M. F. Ghorab

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The exploration of open pit mines and quarries has always been important resources that provide many valuable needed minerals but very often accompanied by large amounts of dust rejected into the air and also many other negative environmental impacts. The dust remains suspended in the atmosphere before being deposited on soils, on forest trees, on plants and also on water, causing at long term allergic and respiratory diseases for residents living in the vicinity or even far away from the mines and quarries. As a consequence of this activity, dust can also disturb the photosynthetic activity of plants and affect water quality. It is for these reasons and because of the intensification of these activities that our motivations have become larger to deal with this kind of topic, which is becoming nowadays an environmental and health concern for almost every country in the world.

Keywords: mines, dust, environmental impacts, environmental concern

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Authors: Carly Cummings, Maria Soledad Peresin

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Alabama has a long history of racial injustice and unsatisfactory educational performance. In the 1870s Jim Crow laws segregated public schools and disproportionally allocated funding and resources to white institutions across the South. Despite the Supreme Court ruling to integrate schools following Brown vs. the Board of Education in 1954, Alabama’s school system continued to exhibit signs of segregation, compounded by “white flight” and the establishment of exclusive private schools, which still exist today. This discriminatory history has had a lasting impact of the state’s education system, reflected in modern school demographics and achievement data. It is well known that Alabama struggles with education performance, especially in science education. On average, minority groups scored the lowest in science proficiency. In Alabama, minority populations are concentrated in a region known as the Black Belt, which was once home to countless slave plantations and was the epicenter of the Civil Rights Movement. Today the Black Belt is characterized by a high density of woodlands and plays a significant role in Alabama’s leading economic industry-forest products. Given the economic importance of forestry and agriculture to the state, environmental science proficiency is essential to its stability; however, it is neglected in areas where it is needed most. To better understand the inequity of science education within Alabama, our study first investigates how geographic location, demographics and school funding relate to science achievement scores using ArcGIS and Pearson’s correlation coefficient. Additionally, our study explores the implementation of a relevant, problem-based, active learning lesson in schools. Relevant learning engages students by connecting material to their personal experiences. Problem-based active learning involves real-world problem-solving through hands-on experiences. Given Alabama’s significant woodland coverage, educational materials on forest products were developed with consideration of its relevance to students, especially those located in the Black Belt. Furthermore, to incorporate problem solving and active learning, the lesson centered around students using forest products to solve environmental challenges, such as water pollution- an increasing challenge within the state due to climate change. Pre and post assessment surveys were provided to teachers to measure the effectiveness of the lesson. In addition to pedagogical practices, community and mentorship programs are known to positively impact educational achievements. To this end, our work examines the results of surveys measuring educational professionals’ attitudes toward a local mentorship group within the Black Belt and its potential to address environmental and science literacy. Additionally, our study presents survey results from participants who attended an educational community event, gauging its effectiveness in increasing environmental and science proficiency. Our results demonstrate positive improvements in environmental awareness and science literacy with relevant pedagogy, mentorship, and community involvement. Implementing these practices can help provide equitable and inclusive learning environments and can better equip students with the skills and knowledge needed to bridge this historic educational gap within Alabama.

Keywords: equitable education, environmental science, environmental education, science education, racial injustice, sustainability, rural education

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: V. M. Varagyan, G. A. Gevorgyan, K. V. Grigoryan, A. L. Varagyan

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Alaverdi region is situated in the northern part of the Republic of Armenia. Previous studies (1989) in Alaverdi region showed that due to soil irrigation with the highly polluted waters of the Debed and Shnogh rivers, the content of heavy metals in the brown forest steppe soils was significantly higher than the maximum permissible concentration as a result of which the fermentative activity in all the layers of the soils was stressed. Compared to the non-polluted soils, the activity of ferments in the plough layers of the highly polluted soils decreased by 44 - 68% (invertase – 60%, phosphatase – 44%, urease – 66%, catalase – 68%). In case of the soil irrigation with the polluted waters, a decrease in the intensity of fermentative reactions was conditioned by the high content of heavy metals in the soils and changes in chemical composition, physical and physicochemical properties. 20-year changes in the fermentative activity in the brown forest steppe soils in Alaverdi region were investigated. The activity of extracellular ferments in the soils was determined by the unification methods. The study has confirmed that self-recovery process occurs in soils previously polluted with heavy metals which can be revealed by fermentative activity. The investigations revealed that during 1989 – 2009, the activity of ferments in the plough layers of the medium and highly polluted soils increased by 31.2 – 52.6% (invertase – 31.2%, urease – 52.6%, phosphatase – 33.3%, catalase – 41.8%) and 24.1 – 87.0% (invertase – 40.4%, urease – 76.9%, phosphatase – 24.1%, catalase – 87.0%) respectively which indicated that the dynamic properties of the soils, which had been broken due to heavy metal pollution, were improved. In 1989, the activity of the Alaverdi copper smelting plant was temporarily stopped due to financial problems caused by the economic crisis and the absence of market, and the factory again started operation in 1997 and isn’t currently running at full capacity. As a result, the Debed river water has obtained a new chemical composition and comparatively good irrigation properties. Due to irrigation with this water, the gradually recovery of the soil dynamic properties, which had been broken due to irrigation with the waters polluted with heavy metals, was occurred. This is also explained by the fact that in case of irrigation with the partially cleaned water, the soil protective function against pollutants rose due to a content increase in humus and silt fractions. It is supposed that in case of the soil irrigation with the partially cleaned water, the intensity of fermentative reactions wasn’t directly affected by heavy metals.

Keywords: alaverdi region, heavy metal pollution, self-recovery, soil fermentative activity

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Authors: Zulkifli Yusop

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The severity of December 2014 flood on the east coast of Peninsular Malaysia has raised concern over the adequacy of existing land use practices and policies. This article assesses flood responses to selective logging, plantation establishment (oil palm and rubber) and their subsequent management regimes. The hydrological impacts were evaluated on two levels: on-site (mostly in the upstream) and off-site to reflect the cumulative impact at downstream. Results of experimental catchment studies suggest that on-site impact of flood could be kept to a minimum when selecting logging strictly adhere to the existing guidelines. However, increases in flood potential and sedimentation rate were observed with logging intensity and slope steepness. Forest conversion to plantation show the highest impacts. Except on the heavily compacted surfaces, the ground revegetation is usually rapid within two years upon the cessation of the logging operation. The hydrological impacts of plantation opening and replanting could be significantly reduced once the cover crop has fully established which normally takes between three to six months after sowing. However, as oil palms become taller and the canopy gets closer, the cover crop tends to die off due to light competition, and its protecting function gradually diminishes. The exposed soil is further compacted by harvesting machinery which subsequently leads to greater overland flow and erosion rates. As such, the hydrological properties of matured oil palm plantations are generally poorer than in young plantation. In hilly area, the undergrowth in rubber plantation is usually denser compared to under oil palm. The soil under rubber trees is also less compacted as latex collection is done manually. By considering the cumulative effects of land-use over space and time, selective logging seems to pose the least impact on flood potential, followed by planting rubber for latex, oil palm and Latex Timber Clone (LTC). The cumulative hydrological impact of LTC plantation is the most severe because of its shortest replanting rotation (12 to 15 years) compared to oil palm (25 years) and rubber for latex (35 years). Furthermore, the areas gazetted for LTC are mostly located on steeper slopes which are more susceptible to landslide and erosion. Forest has limited capability to store excess rainfall and is only effective in attenuating regular floods. Once the hydrologic storage is exceeded, the excess rainfall will appear as flood water. Therefore, for big floods, rainfall regime has a much bigger influence than land use.

Keywords: selective logging, plantation, extreme rainfall, debris flow

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Authors: Claudia Cocozza, Niccolò Frassinelli, Enrico Marchi, Cristiano Foderi, Alessandro Bizzarri, Margherita Paladini, Maria Laura Traversi, Eleftherious Touloupakis, Alessio Giovannelli

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The development of tools to quickly identify the fate of injured trees after stress is highly relevant when biodiversity restoration of damaged sites is based on nature-based solutions. In this context, an approach to assess irreversible physiological damages within trees could help to support planning management decisions of perturbed sites to restore biodiversity, for the safety of the environment and understanding functionality adjustments of the ecosystems. Tree vitality can be estimated by a series of physiological proxies like cambium activity, starch, and soluble sugars amount in C-sinks whilst the accumulation of ethanol within the cambial cells and phloem is considered an alert of cell death. However, their determination requires time-consuming laboratory protocols, which makes the approach unfeasible as a practical option in the field. The project aims to develop biosensors to assess the concentration of soluble sugars and ethanol in stem tissues. Soluble sugars and ethanol concentrations will be used to define injured trees to discriminate compromised and recovering trees in the forest directly. To reach this goal, we select study sites subjected to prescribed fires or recent wildfires as experimental set-ups. Indeed, in Mediterranean countries, forest fire is a recurrent event that must be considered as a central component of regional and global strategies in forest management and biodiversity restoration programs. A biosensor will be developed through a multistep process related to target analytes characterization, bioreceptor selection, and, finally, calibration/testing of the sensor. To validate biosensor signals, soluble sugars and ethanol will be quantified by HPLC and GC using synthetic media (in lab) and phloem sap (in field) whilst cambium vitality will be assessed by anatomical observations. On burnt trees, the stem growth will be monitored by dendrometers and/or estimated by tree ring analyses, whilst the tree response to past fire events will be assessed by isotopic discrimination. Moreover, the fire characterization and the visual assessment procedure will be used to assign burnt trees to a vitality class. At the end of the project, a well-defined procedure combining biosensor signal and visual assessment will be produced and applied to a study case. The project outcomes and the results obtained will be properly packaged to reach, engage and address the needs of the final users and widely shared with relevant stakeholders involved in the optimal use of biosensors and in the management of post-fire areas. This project was funded by National Recovery and Resilience Plan (NRRP), Mission 4, Component C2, Investment 1.1 - Call for tender No. 1409 of 14 September 2022 – ‘Progetti di Ricerca di Rilevante interesse Nazionale – PRIN’ of Italian Ministry of University and Research funded by the European Union – NextGenerationEU; Grant N° P2022Z5742, CUP B53D23023780001.

Keywords: phloem, scorched crown, conifers, prescribed burning, biosensors

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Authors: Alexander J. Severinsky

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Radiative forces of greenhouse gases (GHG) increase the temperature of the Earth's surface, more on land, and less in oceans, due to their thermal capacities. Given this inertia, the temperature increase is delayed over time. Air temperature, however, is not delayed as air thermal capacity is much lower. In this study, through analysis and synthesis of multidisciplinary science and data, an estimate of atmospheric temperature increase is made. Then, this estimate is used to shed light on current observations of ice and snow loss, desertification and forest fires, and increased extreme air disturbances. The reason for this inquiry is due to the author’s skepticism that current changes cannot be explained by a "~1 ^oC" global average surface temperature rise within the last 50-60 years. The only other plausible cause to explore for understanding is that of atmospheric temperature rise. The study utilizes an analysis of air temperature rise from three different scientific disciplines: thermodynamics, climate science experiments, and climactic historical studies. The results coming from these diverse disciplines are nearly the same, within ± 1.6%. The direct radiative force of GHGs with a high level of scientific understanding is near 4.7 W/m² on average over the Earth’s entire surface in 2018, as compared to one in pre-Industrial time in the mid-1700s. The additional radiative force of fast feedbacks coming from various forms of water gives approximately an additional ~15 W/m². In 2018, these radiative forces heated the atmosphere by approximately 5.1 ^oC, which will create a thermal equilibrium average ground surface temperature increase of 4.6 ^oC to 4.8 ^oC by the end of this century. After 2018, the temperature will continue to rise without any additional increases in the concentration of the GHGs, primarily of carbon dioxide and methane. These findings of the radiative force of GHGs in 2018 were applied to estimates of effects on major Earth ecosystems. This additional force of nearly 20 W/m² causes an increase in ice melting by an additional rate of over 90 cm/year, green leaves temperature increase by nearly 5 ^oC, and a work energy increase of air by approximately 40 Joules/mole. This explains the observed high rates of ice melting at all altitudes and latitudes, the spread of deserts and increases in forest fires, as well as increased energy of tornadoes, typhoons, hurricanes, and extreme weather, much more plausibly than the 1.5 ^oC increase in average global surface temperature in the same time interval. Planned mitigation and adaptation measures might prove to be much more effective when directed toward the reduction of existing GHGs in the atmosphere.

Keywords: greenhouse radiative force, greenhouse air temperature, greenhouse thermodynamics, greenhouse historical, greenhouse radiative force on ice, greenhouse radiative force on plants, greenhouse radiative force in air

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Authors: Claus N. Holm, Oliver F. Grooss, Robert A. Alphinas

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This research strives to predict the remaining product life cycle of a song on radio after it has been played for one or two months. The best results were achieved using a k-d tree to calculate the most similar songs to the test songs and use a Random Forest model to forecast radio plays. An 82.78% and 83.44% accuracy is achieved for the two time periods, respectively. This explorative research leads to over 4500 test metrics to find the best combination of models and pre-processing techniques. Other algorithms tested are KNN, MLP and CNN. The features only consist of daily radio plays and use no musical features.

Keywords: hit song science, product life cycle, machine learning, radio

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Authors: Stephen Osakue Amiandamhen, Schwaller Andreas, Martina Meincken, Luvuyo Tyhoda

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Invasive alien tree species are currently being cleared in South Africa as a result of the forest and water imbalances. These species grow wildly constituting about 40% of total forest area. They compete with the ecosystem for natural resources and are considered as ecosystem engineers by rapidly changing disturbance regimes. As such, they are harvested for commercial uses but much of it is wasted because of their form and structure. The waste is being sold to local communities as fuel wood. These species can be considered as potential feedstock for the production of phosphate bonded composites. The presence of bark in wood-based composites leads to undesirable properties, and debarking as an option can be cost implicative. This study investigates the potentials of these invasive species processed without debarking on some fundamental properties of wood-based panels. Some invasive alien tree species were collected from EC Biomass, Port Elizabeth, South Africa. They include Acacia mearnsii (Black wattle), A. longifolia (Long-leaved wattle), A. cyclops (Red-eyed wattle), A. saligna (Golden-wreath wattle) and Eucalyptus globulus (Blue gum). The logs were chipped as received. The chips were hammer-milled and screened through a 1 mm sieve. The wood particles were conditioned and the quantity of bark in the wood was determined. The binding matrix was prepared using a reactive magnesia, phosphoric acid and class S fly ash. The materials were mixed and poured into a metallic mould. The composite within the mould was compressed at room temperature at a pressure of 200 KPa. After initial setting which took about 5 minutes, the composite board was demoulded and air-cured for 72 h. The cured product was thereafter conditioned at 20°C and 70% relative humidity for 48 h. Test of physical and strength properties were conducted on the composite boards. The effect of binder formulation and fly ash content on the properties of the boards was studied using fitted response surface technology, according to a central composite experimental design (CCD) at a fixed wood loading of 75% (w/w) of total inorganic contents. The results showed that phosphate/magnesia ratio of 3:1 and fly ash content of 10% was required to obtain a product of good properties and sufficient strength for intended applications. The proposed products can be used for ceilings, partitioning and insulating wall panels.

Keywords: invasive alien tree species, phosphate bonded composites, physical properties, strength

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Hadj Miloua, Abbes Azzi

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The objective of the present paper is to understand, predict and modeled the fire behavior in confined and open area in different conditions and diverse fuels such as liquid pool fire and the vegetative materials. The distinctive problems are a ventilated road tunnel used for urban transport, by the characterization installations of ventilation and his influence in the mode of smoke dispersion and the flame shape. A general investigation is relatively traditional, based on the modeling and simulation the scenario of the pool fire interacted with wind ventilation by the use of numerical software fire dynamic simulator FDS ver.5 to simulate the fire in ventilated tunnel. The second simulation by WFDS.5 is Wildland fire which is always occurs in forest and rangeland fire environments and will thus have an impact on people, property and resources.

Keywords: fire, road tunnel, simulation, vegetation, wildland

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Manpreet Kaur

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When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

Procedia PDF Downloads 115

Authors: Neelam Kadyan, Pratima Ranga, Yogender

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Indigenous people are the One who are affected by the climate change the most, although there have contributed little to its causes. This is largely a result of their historic dependence on local biological diversity, ecosystem services and cultural landscapes as a source of their sustenance and well-being. Comprising only four percent of the world’s population they utilize 22 percent of the world’s land surface. Despite their high exposure-sensitivity indigenous peoples and local communities are actively responding to changing climatic conditions and have demonstrated their resourcefulness and resilience in the face of climate change. Traditional Indigenous territories encompass up to 22 percent of the world’s land surface and they coincide with areas that hold 80 percent of the planet’s biodiversity. Also, the greatest diversity of indigenous groups coincides with the world’s largest tropical forest wilderness areas in the Americas (including Amazon), Africa, and Asia, and 11 percent of world forest lands are legally owned by Indigenous Peoples and communities. This convergence of biodiversity-significant areas and indigenous territories presents an enormous opportunity to expand efforts to conserve biodiversity beyond parks, which tend to benefit from most of the funding for biodiversity conservation. Tapping on Ancestral Knowledge Indigenous Peoples are carriers of ancestral knowledge and wisdom about this biodiversity. Their effective participation in biodiversity conservation programs as experts in protecting and managing biodiversity and natural resources would result in more comprehensive and cost effective conservation and management of biodiversity worldwide. Addressing the Climate Change Agenda Indigenous Peoples has played a key role in climate change mitigation and adaptation. The territories of indigenous groups who have been given the rights to their lands have been better conserved than the adjacent lands (i.e., Brazil, Colombia, Nicaragua, etc.). Preserving large extensions of forests would not only support the climate change objectives, but it would respect the rights of Indigenous Peoples and conserve biodiversity as well. A climate change agenda fully involving Indigenous Peoples has many more benefits than if only government and/or the private sector are involved. Indigenous peoples are some of the most vulnerable groups to the negative effects of climate change. Also, they are a source of knowledge to the many solutions that will be needed to avoid or ameliorate those effects. For example, ancestral territories often provide excellent examples of a landscape design that can resist the negatives effects of climate change. Over the millennia, Indigenous Peoples have developed adaptation models to climate change. They have also developed genetic varieties of medicinal and useful plants and animal breeds with a wider natural range of resistance to climatic and ecological variability.

Keywords: ancestral knowledge, cost effective conservation, management, indigenous peoples, climate change

Procedia PDF Downloads 678

Authors: P. Řehoř, D. Holátová, V. Doležalová

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Paper deals with analysis of strategic management methods in non-profit making organization in the Czech Republic. Strategic management represents an aggregate of methods and approaches that can be applied for managing organizations - in this article the organizations which associate owners and keepers of non-state forest properties. Authors use these methods of strategic management: analysis of stakeholders, SWOT analysis and questionnaire inquiries. The questionnaire was distributed electronically via e-mail. In October 2013 we obtained data from a total of 84 questionnaires. Based on the results the authors recommend the using of confrontation strategy which improves the competitiveness of non-profit making organizations.

Keywords: strategic management, non-profit making organization, strategy analysis, SWOT analysis, strategy, competitiveness

Procedia PDF Downloads 484

Authors: Sebastiano Candamano, Antonio Iorfida, Patrizia Frontera, Anastasia Macario, Fortunato Crea

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Alkali-activated materials are promising binders obtained by an alkaline attack on fly-ashes, metakaolin, blast slag among others. In order to guarantee the highest ecological and cost efficiency, a proper selection of precursors and alkaline activators has to be carried out. These choices deeply affect the microstructure, chemistry and performances of this class of materials. Even if, in the last years, several researches have been focused on mix designs and curing conditions, the lack of exhaustive activation models, standardized mix design and curing conditions and an insufficient investigation on shrinkage behavior, efflorescence, additives and durability prevent them from being perceived as an effective and reliable alternative to Portland. The aim of this study is to develop alkali-activated cements mortars containing high amounts of industrial by-products and waste, such as ground granulated blast furnace slag (GGBFS) and ashes obtained from the combustion process of forest biomass in thermal power plants. In particular, the experimental campaign was performed in two steps. In the first step, research was focused on elucidating how the workability, mechanical properties and shrinkage behavior of produced mortars are affected by the type and fraction of each precursor as well as by the composition of the activator solutions. In order to investigate the microstructures and reaction products, SEM and diffractometric analyses have been carried out. In the second step, their durability in harsh environments has been evaluated. Mortars obtained using only GGBFS as binder showed mechanical properties development and shrinkage behavior strictly dependent on SiO2/Na2O molar ratio of the activator solutions. Compressive strengths were in the range of 40-60 MPa after 28 days of curing at ambient temperature. Mortars obtained by partial replacement of GGBFS with metakaolin and forest biomass ash showed lower compressive strengths (≈35 MPa) and shrinkage values when higher amount of ashes were used. By varying the activator solutions and binder composition, compressive strength up to 70 MPa associated with shrinkage values of about 4200 microstrains were measured. Durability tests were conducted to assess the acid and thermal resistance of the different mortars. They all showed good resistance in a solution of 5%wt of H2SO4 also after 60 days of immersion, while they showed a decrease of mechanical properties in the range of 60-90% when exposed to thermal cycles up to 700°C.

Keywords: alkali activated cement, biomass ash, durability, shrinkage, slag

Procedia PDF Downloads 326

Authors: Ruchirek Ratchaburi, W. Kevin. Hicks, Christopher S. Malley, Lisa D. Emberson

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Air pollution problems in Thailand have improved over the last few decades, but in some areas, concentrations of coarse particulate matter (PM₁₀) are above health and regulatory guidelines. It is, therefore, useful to investigate how PM₁₀ varies across Thailand, what conditions cause this variation, and how could PM₁₀ concentrations be reduced. This research uses data collected by the Thailand Pollution Control Department (PCD) from 17 monitoring sites, located across 12 provinces, and obtained between 2011 and 2015 to assess PM₁₀ concentrations and the conditions that lead to different levels of pollution. This is achieved through exploration of air mass pathways using trajectory analysis, used in conjunction with the monitoring data, to understand the contribution of different months, an hour of the day and source regions to annual PM₁₀ concentrations in Thailand. A focus is placed on locations that exceed the national standard for the protection of human health. The analysis shows how this approach can be used to explore the influence of biomass burning on annual average PM₁₀ concentration and the difference in air pollution conditions between Northern and Southern Thailand. The results demonstrate the substantial contribution that open biomass burning from agriculture and forest fires in Thailand and neighboring countries make annual average PM₁₀ concentrations. The analysis of PM₁₀ measurements at monitoring sites in Northern Thailand show that in general, high concentrations tend to occur in March and that these particularly high monthly concentrations make a substantial contribution to the overall annual average concentration. In 2011, a > 75% reduction in the extent of biomass burning in Northern Thailand and in neighboring countries resulted in a substantial reduction not only in the magnitude and frequency of peak PM₁₀ concentrations but also in annual average PM₁₀ concentrations at sites across Northern Thailand. In Southern Thailand, the annual average PM₁₀ concentrations for individual years between 2011 and 2015 did not exceed the human health standard at any site. The highest peak concentrations in Southern Thailand were much lower than for Northern Thailand for all sites. The peak concentrations at sites in Southern Thailand generally occurred between June and October and were associated with air mass back trajectories that spent a substantial proportion of time over the sea, Indonesia, Malaysia, and Thailand prior to arrival at the monitoring sites. The results show that emissions reductions from biomass burning and forest fires require action on national and international scales, in both Thailand and neighboring countries, such action could contribute to ensuring compliance with Thailand air quality standards.

Keywords: annual average concentration, long-range transport, open biomass burning, particulate matter

Procedia PDF Downloads 184

Authors: Emily A. Gilbert, Jephte Sompud, Andy R. Mojiol, Cynthia B. Sompud, Alim Biun

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Gaya Island of Sabah is known for its wildlife and marine biodiversity. It has marks itself as one of the hot destinations of tourists from all around the world. Gaya Island tourism activities have contributed to Sabah’s economy revenue with the high number of tourists visiting the island. However, it has led to the increased anthropogenic noise derived from tourism activities. This may greatly interfere with the animals such as understory birds that rely on acoustic signals as a tool for communication. Many studies in other parts of the regions reveal that anthropogenic noise does decrease species richness of avian community. However, in Malaysia, published research regarding the impact of anthropogenic noise on the understory birds is still very lacking. This study was conducted in order to fill up this gap. This study aims to investigate the anthropogenic noise’s impact towards understory bird population. There were three sites within the Primary forest of Gaya Island that were chosen to sample the level of anthropogenic noise in relation to the understory bird population. Noise mapping method was used to measure the anthropogenic noise level and identify the zone with high anthropogenic noise level (> 60dB) and zone with low anthropogenic noise level (< 60dB) based on the standard threshold of noise level. The methods that were used for this study was solely mist netting and ring banding. This method was chosen as it can determine the diversity of the understory bird population in Gaya Island. The preliminary study was conducted from 15th to 26th April and 5th to 10th May 2015 whereby there were 2 mist nets that were set up at each of the zones within the selected site. The data was analyzed by using the descriptive analysis, presence and absence analysis, diversity indices and diversity t-test. Meanwhile, PAST software was used to analyze the obtain data. The results from this study present a total of 60 individuals that consisted of 12 species from 7 families of understory birds were recorded in three of the sites in Gaya Island. The Shannon-Wiener index shows that diversity of species in high anthropogenic noise zone and low anthropogenic noise zone were 1.573 and 2.009, respectively. However, the statistical analysis shows that there was no significant difference between these zones. Nevertheless, based on the presence and absence analysis, it shows that the species at the low anthropogenic noise zone was higher as compared to the high anthropogenic noise zone. Thus, this result indicates that there is an impact of anthropogenic noise on the population diversity of understory birds. There is still an urgent need to conduct an in-depth study by increasing the sample size in the selected sites in order to fully understand the impact of anthropogenic noise towards the understory birds population so that it can then be in cooperated into the wildlife management for a sustainable environment in Gaya Island.

Keywords: anthropogenic noise, biodiversity, Gaya Island, understory bird

Procedia PDF Downloads 365

Authors: Domingos Viegas, Miguel Almeida, Carmen Rocha, Ilda Novo, Yolanda Luna

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Forest fires along the more than 1200km of the Spanish-Portuguese border are more and more frequent, currently achieving around 2000 fire events per year. Some of these events develop to large international wildfire requiring concerted operations based on shared information between the two countries. The fire event of Valencia de Alcantara (2003) causing several fatalities and more than 13000ha burnt, is a reference example of these international events. Currently, Portugal and Spain have a specific cross-border cooperation protocol on wildfires response for a strip of about 30km (15 km for each side). It is recognized by public authorities the successfulness of this collaboration however it is also assumed that this cooperation should include more functionalities such as the development of a common risk information system for transboundary fire events. Since Portuguese and Spanish authorities use different approaches to determine the fire risk indexes inputs and different methodologies to assess the fire risk, sometimes the conjoint firefighting operations are jeopardized since the information is not harmonized and the understanding of the situation by the civil protection agents from both countries is not unique. Thus, a methodology aiming the harmonization of the fire risk calculation and perception by Portuguese and Spanish Civil protection authorities is hereby presented. The final results are presented as well. The fire risk index used in this work is the Canadian Fire Weather Index (FWI), which is based on meteorological data. The FWI is limited on its application as it does not take into account other important factors with great effect on the fire appearance and development. The combination of these factors is very complex since, besides the meteorology, it addresses several parameters of different topics, namely: sociology, topography, vegetation and soil cover. Therefore, the meaning of FWI values is different from region to region, according the specific characteristics of each region. In this work, a methodology for FWI calibration based on the number of fire occurrences and on the burnt area in the transboundary regions of Portugal and Spain, in order to assess the fire risk based on calibrated FWI values, is proposed. As previously mentioned, the cooperative firefighting operations require a common perception of the information shared. Therefore, a common classification of the fire risk for the fire events occurred in the transboundary strip is proposed with the objective of harmonizing this type of information. This work is integrated in the ECHO project SpitFire - Spanish-Portuguese Meteorological Information System for Transboundary Operations in Forest Fires, which aims the development of a web platform for the sharing of information and supporting decision tools to be used in international fire events involving Portugal and Spain.

Keywords: data harmonization, FWI, international collaboration, transboundary wildfires

Procedia PDF Downloads 254

Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

Procedia PDF Downloads 96

Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

Procedia PDF Downloads 95

Authors: Kristina Amaleviciute, Ieva Jokubauskaite, Alvyra Slepetiene, Jonas Volungevicius, Inga Liaudanskiene

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The main task of this research was to investigate the chemical composition of the differently used soil in profiles. To identify the differences in the soil were investigated organic carbon (SOC) and its fractional composition: dissolved organic carbon (DOC), mobile humic acids (MHA) and C to N ratio of natural and anthropogenically affected Luvisols. Research object: natural and anthropogenically affected Luvisol, Akademija, Kedainiai, distr. Lithuania. Chemical analyses were carried out at the Chemical Research Laboratory of Institute of Agriculture, LAMMC. Soil samples for chemical analyses were taken from the genetics soil horizons. SOC was determined by the Tyurin method modified by Nikitin, measuring with spectrometer Cary 50 (VARIAN) in 590 nm wavelength using glucose standards. For mobile humic acids (MHA) determination the extraction procedure was carried out using 0.1 M NaOH solution. Dissolved organic carbon (DOC) was analyzed using an ion chromatograph SKALAR. pH was measured in 1M H2O. N total was determined by Kjeldahl method. Results: Based on the obtained results, it can be stated that transformation of chemical composition is going through the genetic soil horizons. Morphology of the upper layers of soil profile which is formed under natural conditions was changed by anthropomorphic (agrogenic, urbogenic, technogenic and others) structure. Anthropogenic activities, mechanical and biochemical disturbances destroy the natural characteristics of soil formation and complicates the interpretation of soil development. Due to the intensive cultivation, the pH values of the curve equals (disappears acidification characteristic for E horizon) with natural Luvisol. Luvisols affected by agricultural activities was characterized by a decrease in the absolute amount of humic substances in separate horizons. But there was observed more sustainable, higher carbon sequestration and thicker storage of humic horizon compared with forest Luvisol. However, the average content of humic substances in the soil profile was lower. Soil organic carbon content in anthropogenic Luvisols was lower compared with the natural forest soil, but there was more evenly spread over in the wider thickness of accumulative horizon. These data suggest that the organization of geo-ecological declines and agroecological increases in Luvisols. Acknowledgement: This work was supported by the National Science Program ‘The effect of long-term, different-intensity management of resources on the soils of different genesis and on other components of the agro-ecosystems’ [grant number SIT-9/2015] funded by the Research Council of Lithuania.

Keywords: agrogenization, dissolved organic carbon, luvisol, mobile humic acids, soil organic carbon

Procedia PDF Downloads 237

Authors: Arpit Patil, Atharav Bhagwat, Rajas Bhope, Pramod Bide

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This research integrates IoT and ML to predict heart failure risks, utilizing the Framingham dataset. IoT devices gather real-time physiological data, focusing on heart rate dynamics, while ML, specifically Random Forest, predicts heart failure. Rigorous feature selection enhances accuracy, achieving over 90% prediction rate. This amalgamation marks a transformative step in proactive healthcare, highlighting early detection's critical role in cardiovascular risk mitigation. Challenges persist, necessitating continual refinement for improved predictive capabilities.

Keywords: cardiovascular diseases, internet of things, machine learning, cardiac risk assessment, heart failure prediction, early detection, cardio data analysis

Procedia PDF Downloads 14

Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

Procedia PDF Downloads 157

Authors: Kapil Pandey, Vishnu Goyal

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Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes.

Keywords: GIS, landuse/landcover, spatial and temporal data, remote sensing

Procedia PDF Downloads 433