Search results for: random forest classifier

Authors: Mindaye Teshome, Nesibu Yahya, Carlos Moreira Miquelino Eleto Torres, Pedro Manuel Villaa, Mehari Alebachew

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Arbagugu forest is one of the remnant dry Afromontane forests under severe anthropogenic disturbances in central Ethiopia. Despite this fact, up-to-date information is lacking about the status of the forest and its role in climate change mitigation. In this study, we evaluated the woody species composition, structure, biomass, and carbon stock in this forest. We employed a systematic random sampling design and established fifty-three sample plots (20 × 100 m) to collect the vegetation data. A total of 37 woody species belonging to 25 families were recorded. The density of seedlings, saplings, and matured trees were 1174, 101, and 84 stems ha-1, respectively. The total basal area of trees with DBH (diameter at breast height) ≥ 2 cm was 21.3 m2 ha-1. The characteristic trees of dry Afromontane Forest such as Podocarpus falcatus, Juniperus procera, and Olea europaea subsp. cuspidata exhibited a fair regeneration status. On the contrary, the least abundant species Lepidotrichilia volkensii, Canthium oligocarpum, Dovyalis verrucosa, Calpurnia aurea, and Maesa lanceolata exhibited good regeneration status. Some tree species such as Polyscias fulva, Schefflera abyssinica, Erythrina brucei, and Apodytes dimidiata lack regeneration. The total carbon stored in the forest ranged between 6.3 Mg C ha-1 and 835.6 Mg C ha-1. This value is equivalent to 639.6 Mg C ha-1. The forest had a very low number of woody species composition and diversity. The regeneration study also revealed that a significant number of tree species had unsatisfactory regeneration status. Besides, the forest had a lower carbon stock density compared with other dry Afromontane forests. This implies the urgent need for forest conservation and restoration activities by the local government, conservation practitioners, and other concerned bodies to maintain the forest and sustain the various ecosystem goods and services provided by the Arbagugu forest.

Keywords: aboveground biomass, forest regeneration, climate change, biodiversity conservation, restoration

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Authors: M. C. A. Leite, B. Chen-Charpentier, F. Agusto

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A long-term goal of sustainable forest management is a relatively stable source of wood and a stable forest age-class structure has become the goal of many forest management practices. In the absence of disturbances, this forest management goal could easily be achieved. However, in the face of recurring insect outbreaks and other disruptive processes forest planning becomes more difficult, requiring knowledge of the effects on the forest of a wide variety of environmental factors (e.g., habitat heterogeneity, fire size and frequency, harvesting, insect outbreaks, and age distributions). The association between distinct forest disturbances and the potential effect on forest dynamics is a complex matter, particularly when evaluated over time and at large scale, and is not well understood. However, gaining knowledge in this area is crucial for a sustainable forest management. Mathematical modeling is a tool that can be used to broader the understanding in this area. In this talk we will introduce mathematical models formulation incorporating the effect of insect outbreaks either as a single disturbance in the forest population dynamics or coupled with other disturbances: either wildfire or harvesting. The results and ecological insights will be discussed.

Keywords: age-structured forest population, disturbances interaction, harvesting insects outbreak dynamics, mathematical modeling

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Authors: Prem Bahadur Giri

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The nationalization of forests during the early 1960s had become counterproductive for the conservation of forests in Nepal. Realizing this fact, the Government of Nepal initiated a paradigm shift from a government-controlled forestry system to people’s direct participation in managing forestry, conceptualizing a community forest approach in the early 1980s. The community forestry approach is expected to promote sustainable forest management, restoring degraded forests to enhance the forest condition on the one hand, and on the other, improvement of livelihoods, particularly of low-income people and forest-dependent communities, as well as promoting community ownership of a forest. As a result, the establishment of community forests started and had taken faster momentum in Nepal. Of the total land in Nepal, forest occupies 6.5 million hectares which are around 45 percent of the forest area. Of the total forest area, 1.8 million hectares have been handed over to community management. A total of 19,361 ‘community forest users groups’ are already created to manage the community forest. To streamline the governance of community forests, the enactment of ‘The Forest Act 1993’ provides a clear legal basis for managing community forests in Nepal. This article is based on an in-depth study taking the case of Tarpakha Community Forest (TCF) located in Siranchok Rural Municipality of Gorkha District in Nepal. It mainly discusses the extent to which the TCF is able to achieve the twin objectives of this community forest for catalyzing socio-economic improvement of the targeted community and conservation of the forest. The primary information was generated through in-depth interviews along with group discussions with members, the management committee, and other relevant stakeholders. The findings reveal that there is a significant improvement in the regeneration of the forest and also changes in the socio-economic status of the local community. However, coordination with local municipalities and forest governing entities is still weak.

Keywords: community forest, socio-economic benefit, sustainable forest management, Nepal

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Authors: K. Lalhmingsangi, U. K. Sahoo

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Non-Timber Forest Products represent one of the key sources of income and subsistence to the fringe communities living in rural areas. A study was conducted for the assessment of NTFP within the community forest of five villages under Thenzawl forest division. Participatory Rural Appraisal (PRA), questionnaire, field exercise, discussion and interview with the first hand NTFP exploiter and sellers was adopted for the field study. Fuel wood, medicinal plants, fodder, wild vegetables, fruits, broom grass, thatch grass, bamboo pole and cane species are the main NTFP harvested from the community forest. Among all the NTFPs, the highest percentage of household involvement was found in fuel wood, i.e. 53% of household and least in medicinal plants 5%. They harvest for their own consumption as well as for selling to the market to meet their needs. Edible food and fruits are sold to the market and it was estimated that 300 (Rs/hh/yr) was earned by each household through the selling of this NTFP from the community forest alone. No marketing channels are linked with fuelwood, medicinal plants and fodder since they harvest only for their own consumption.

Keywords: community forest, subsistence, non-timber forest products, Thenzawl Forest Division

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

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Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

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Authors: Saurabh Shukla, G. N. Pandey

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The fires have become the most serious disasters to forest resources and the human environment. In recent years, due to climate change, human activities and other factors the frequency of forest fires has increased considerably. The monitoring and prevention of forest fires have now become a global concern for forest fire prevention organizations. Currently, the methods for forest fire prevention largely consist of patrols, observation from watch towers. Thus, software like deployment of the wireless sensor network to prevent forest fire is being developed to get a better estimate of the temperature and humidity prospects. Now days, wireless sensor networks are beginning to be deployed at an accelerated pace. It is not unrealistic to expect that in coming years the world will be covered with wireless sensor networks. This new technology has lots of unlimited potentials and can be used for numerous application areas including environmental, medical, military, transportation, entertainment, crisis management, homeland defense, and smart spaces.

Keywords: deployment, sensors, wireless sensor networks, forest fires

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Ahmed A. A. Fadol, Guangji Tong, Wlaa Mohamed

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This paper provides a comprehensive analysis of food insecurity in Sudan, focusing on consumption patterns and their implications, employing the Integrated Food Security Phase Classification (IPC) assessment framework. Years of conflict and economic instability have driven large segments of the population in Sudan into crisis levels of acute food insecurity according to the (IPC). A substantial number of people are estimated to currently face emergency conditions, with an additional sizeable portion categorized under less severe but still extreme hunger levels. In this study, we explore the multifaceted nature of food insecurity in Sudan, considering its historical, political, economic, and social dimensions. An analysis of consumption patterns and trends was conducted, taking into account cultural influences, dietary shifts, and demographic changes. Furthermore, we employ logistic regression and random forest analysis to identify significant independent variables influencing food security status in Sudan. Random forest clearly outperforms logistic regression in terms of area under curve (AUC), accuracy, precision and recall. Forward projections of the IPC for Sudan estimate that 15 million individuals are anticipated to face Crisis level (IPC Phase 3) or worse acute food insecurity conditions between October 2023 and February 2024. Of this, 60% are concentrated in Greater Darfur, Greater Kordofan, and Khartoum State, with Greater Darfur alone representing 29% of this total. These findings emphasize the urgent need for both short-term humanitarian aid and long-term strategies to address Sudan's deepening food insecurity crisis.

Keywords: food insecurity, consumption patterns, logistic regression, random forest analysis

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Authors: Mark Joseph Quinto, Roan Beronilla, Guiller Damian, Eliza Camaso, Ronaldo Alberto

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Forest inventories are essential to assess the composition, structure and distribution of forest vegetation that can be used as baseline information for management decisions. Classical forest inventory is labor intensive and time-consuming and sometimes even dangerous. The use of Light Detection and Ranging (LiDAR) in forest inventory would improve and overcome these restrictions. This study was conducted to determine the possibility of using LiDAR derived data in extracting high accuracy forest biophysical parameters and as a non-destructive method for forest status analysis of San Manual, Pangasinan. Forest resources extraction was carried out using LAS tools, GIS, Envi and .bat scripts with the available LiDAR data. The process includes the generation of derivatives such as Digital Terrain Model (DTM), Canopy Height Model (CHM) and Canopy Cover Model (CCM) in .bat scripts followed by the generation of 17 composite bands to be used in the extraction of forest classification covers using ENVI 4.8 and GIS software. The Diameter in Breast Height (DBH), Above Ground Biomass (AGB) and Carbon Stock (CS) were estimated for each classified forest cover and Tree Count Extraction was carried out using GIS. Subsequently, field validation was conducted for accuracy assessment. Results showed that the forest of San Manuel has 73% Forest Cover, which is relatively much higher as compared to the 10% canopy cover requirement. On the extracted canopy height, 80% of the tree’s height ranges from 12 m to 17 m. CS of the three forest covers based on the AGB were: 20819.59 kg/20x20 m for closed broadleaf, 8609.82 kg/20x20 m for broadleaf plantation and 15545.57 kg/20x20m for open broadleaf. Average tree counts for the tree forest plantation was 413 trees/ha. As such, the forest of San Manuel has high percent forest cover and high CS.

Keywords: carbon stock, forest inventory, LiDAR, tree count

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Authors: Kan Yu, Khui Hung Lee, Eben Afrifa-Yamoah, Jing Guo, Katrina Harrison, Jack Goldblatt, Nicholas Pachter, Jitian Xiao, Guicheng Brad Zhang

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Background and Significance of the Study: Congenital Heart Defects (CHDs) are the most common malformation at birth and one of the leading causes of infant death. Although the exact etiology remains a significant challenge, epigenetic modifications, such as DNA methylation, are thought to contribute to the pathogenesis of congenital heart defects. At present, no existing DNA methylation biomarkers are used for early detection of CHDs. The existing CHD diagnostic techniques are time-consuming and costly and can only be used to diagnose CHDs after an infant was born. The present study employed a machine learning technique to analyse genome-wide methylation data in children with and without CHDs with the aim to find methylation biomarkers for CHDs. Methods: The Illumina Human Methylation EPIC BeadChip was used to screen the genome‐wide DNA methylation profiles of 24 infants diagnosed with congenital heart defects and 24 healthy infants without congenital heart defects. Primary pre-processing was conducted by using RnBeads and limma packages. The methylation levels of top 600 genes with the lowest p-value were selected and further investigated by using a random forest approach. ROC curves were used to analyse the sensitivity and specificity of each biomarker in both training and test sample sets. The functionalities of selected genes with high sensitivity and specificity were then assessed in molecular processes. Major Findings of the Study: Three genes (MIR663, FGF3, and FAM64A) were identified from both training and validating data by random forests with an average sensitivity and specificity of 85% and 95%. GO analyses for the top 600 genes showed that these putative differentially methylated genes were primarily associated with regulation of lipid metabolic process, protein-containing complex localization, and Notch signalling pathway. The present findings highlight that aberrant DNA methylation may play a significant role in the pathogenesis of congenital heart defects.

Keywords: biomarker, congenital heart defects, DNA methylation, random forest

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Authors: Dung Nguyen, Yu Wei, Eric Henderson

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Analytical tools have been used for decades to assist in the development of forest plans. In 2016, a new decision support system, PRISM, was jointly developed by United States Forest Service (USFS) Northern Region and Colorado State University to support the forest planning process. Prism has a friendly user interface with functionality for database management, model development, data visualization, and sensitivity analysis. The software is tailored for USFS planning, but it is flexible enough to support planning efforts by other forestland owners and managers. Here, the core capability of PRISM and its applications in developing plans for several United States national forests are presented. The strengths of PRISM are also discussed to show its potential of being a preferable tool for managers and experts in the domain of forest management and planning.

Keywords: decision support, forest management, forest plan, graphical user interface, software

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Authors: Melvin Ballera, Aldrich Olivar, Mary Soriano

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Digital computers nowadays must be able to have a utility that is capable of generating random numbers. Usually, computer-generated random numbers are not random given predefined values such as starting point and end points, making the sequence almost predictable. There are many applications of random numbers such business simulation, manufacturing, services domain, entertainment sector and other equally areas making worthwhile to design a unique method and to allow unpredictable random numbers. Applying stochastic simulation using linear congruential algorithm, it shows that as it increases the numbers of the seed and range the number randomly produced or selected by the computer becomes unique. If this implemented in an environment where random numbers are very much needed, the reliability of the random number is guaranteed.

Keywords: stochastic simulation, random numbers, linear congruential algorithm, pseudorandomness

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Mohammed Sghir Taleb

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Forest ecosystems in Morocco are subject increasingly to natural and human pressures. Conscious of this problem, Morocco set a strategy that focuses on programs of in-situ and ex-situ biodiversity conservation. This study is the result of a synthesis of various existing studies on biodiversity and forest ecosystems. It gives an overview of Moroccan mountain forest ecosystems and flora diversity. It also focuses on the efforts made by Morocco to conserve and sustainably manage biodiversity.

Keywords: mountain, ecosystems, conservation, Morocco

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Authors: Laxuman Thakuri

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Community-based forest management is often glorified as one of the best forest management alternatives in the developing countries like Nepal. It is also believed that the transfer of forest management authorities to local communities is decisive to take efficient decisions, maximize the forest benefits and improve the people’s livelihood. The community forestry of Nepal also aims to maximize the forest benefits; share them among the user households and improve their livelihood. However, how the local communities fix the price of forest products and local pricing made by the forest user groups affects to equitable forest benefits-sharing among the user households and their livelihood improvement objectives, the answer is largely silent among the researchers and policy-makers alike. This study examines local pricing system of forest products in the lowland community forestry and its effects on equitable benefit-sharing and livelihood improvement objectives. The study discovered that forest user groups fixed the price of forest products based on three criteria: i) costs incur in harvesting, ii) office operation costs, and iii) livelihood improvement costs through community development and income generating activities. Since user households have heterogeneous socio-economic conditions, the forest user groups have been applied low pricing strategy even for high-value forest products that the access of socio-economically worse-off households can be increased. However, the results of forest products distribution showed that as a result of low pricing strategy the access of socio-economically better-off households has been increasing at higher rate than worse-off and an inequality situation has been created. Similarly, the low pricing strategy is also found defective to livelihood improvement objectives. The study suggests for revising the forest products pricing system in community forest management and reforming the community forestry policy as well.

Keywords: community forestry, forest products pricing, equitable benefit-sharing, livelihood improvement, Nepal

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Authors: Teng-Chiu Lin, Pei-Jen Lee Shaner, Shin-Yu Lin

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Both forest age and physical damages due to weather events such as tropical cyclones can influence forest characteristics and subsequently its capacity to sequester carbon. Detangling these influences is therefore a pressing issue under climate change. In this study, we compared the compositional and structural characteristics of three forests in Taiwan differing in age and severity of typhoon disturbances. We found that the two forests (one old-growth forest and one secondary forest) experiencing more severe typhoon disturbances had shorter stature, higher wood density, higher tree species diversity, and lower typhoon-induced tree mortality than the other secondary forest experiencing less severe typhoon disturbances. On the other hand, the old-growth forest had a larger amount of woody debris than the two secondary forests, suggesting a dominant role of forest age on woody debris accumulation. Of the three forests, only the two experiencing more severe typhoon disturbances formed new gaps following two 2015 typhoons, and between these two forests, the secondary forest gained more gaps than the old-growth forest. Consider that older forests generally have more gaps due to a higher background tree mortality, our findings suggest that the age effects on gap dynamics may be reversed by typhoon disturbances. This study demonstrated the effects of typhoons on forest characteristics, some of which could negate the age effects and rejuvenate older forests. If cyclone disturbances were to intensity under climate change, the capacity of older forests to sequester carbon may be reduced.

Keywords: typhoon, canpy gap, coarse woody debris, forest stature, forest age

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Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: Samuel Hyellamada Jerry

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This study focused on assessing conservation priorities within the Karlahi Forest Reserve of Fufore Local Government in Adamawa State. The main objective was to identify specific areas within the forest reserve that require immediate conservation attention. The research employed remote sensing and GIS techniques to achieve this goal. By overlaying the IDRIS Silva module results, a spatial distribution map was generated, highlighting the cumulative priority areas within and outside the forest. Among the total vegetated area of 26.38 km² in the Karlahi Forest Reserve, the analysis revealed that 16.16 km² were classified as high-priority conservation zones. Additionally, 4.59 km² and 5.63 km² were identified as medium and low-priority areas, respectively. In light of these findings, it is recommended that conservation efforts incorporate detailed land cover information and regular assessments of species diversity. Furthermore, strict adherence to national and state policies regarding forest reserves and parks is crucial for effective conservation management.

Keywords: priority, Karlahi, forest, reserve, IDRISI Silva, species diversity

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Authors: Ratun Rahman, Rafid Islam, Akin Ahmed, Kamrul Hasan, Hasan Mahmud

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Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and TensorFlow Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. The test set consists of 1,168 samples (among these android applications, 602 are malware and 566 are benign applications), each consisting of 948 features (permissions). Using the permission-based dataset, the machine learning algorithms then produce accuracy rates above 80%, except the Naive Bayes Algorithm with 65% accuracy. Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%.

Keywords: android malware detection, machine learning, malware, malware analysis

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Authors: D. S. Palimkar

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The FRIGDI (functional random integrodifferential inclusion) seems to be new and includes several known random differential inclusions already studied in the literature as special cases have been discussed in the literature for various aspects of the solutions. In this paper, we prove the existence result for FIGDI under the non-convex case of multi-valued function involved in it.Using random fixed point theorem of B. C. Dhage and caratheodory condition. This result is new to the theory of differential inclusion.

Keywords: caratheodory condition, random differential inclusion, random solution, integro-differential inclusion

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Authors: Sony Baral, Harald Vacik

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This study analyzes the sustainability of community forest management in two community forests in Terai and Hills of Nepal, representing four forest types: 1) Shorearobusta, 2) Terai hardwood, 3) Schima-Castanopsis, and 4) other Hills. The sustainability goals for this region include maintaining and enhancing the forest stocks. Considering this, we analysed changes in species composition, stand density, growing stock volume, and growth-to-removal ratio at 3-5 year intervals from 2005-2016 within 109 permanent forest plots (57 in the Terai and 52 in the Hills). To complement inventory data, forest users, forest committee members, and forest officials were consulted. The results indicate that the relative representation of economically valuable tree species has increased. Based on trends in stand density, both forests are being sustainably managed. Pole-sized trees dominated the diameter distribution, however, with a limited number of mature trees and declined regeneration. The forests were over-harvested until 2013 but under-harvested in the recent period in the Hills. In contrast, both forest types were under-harvested throughout the inventory period in the Terai. We found that the ecological dimension of sustainable forest management is strongly achieved while the economic dimension is lacking behind the current potential. Thus, we conclude that maintaining a large number of trees in the forest does not necessarily ensure both ecological and economical sustainability. Instead, priority should be given on a rational estimation of the annual harvest rates to enhance forest resource conditions together with regular benefits to the local communities.

Keywords: community forests, diversity, growing stock, forest management, sustainability, nepal

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Authors: Dhruba Khatri

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Despite the short history of community based forest management, the community forestry program of Nepal has produced substantial positive effects to organize the local people at a local level institution called Community Forest User Group and manage the local forest resources in the line of poverty reduction since its inception in 1970s. Moreover, each CFUG has collected a community fund from the sale of forest products and non-forestry sources as well and the fund has played a vital role to improve the livelihood of user households living in and around the forests. The specific study sites were selected based on the criteria of i) community forests having dominancy of Sal forests, and ii) forests having 3-5 years experience of community forest management. The price rates of forest products fixed by the CFUGs and the distribution records were collected from the respective community forests. Nonetheless, the relation between pricing strategy and community fund collection revealed that the small change in price of forest products could greatly affect in community fund collection and carry out of forest management, community development, and income generation activities in the line of poverty reduction at local level.

Keywords: benefit sharing, community forest, equitable, Nepal

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Authors: A. O. Arowolo, M. U. Agbonlahor, P. A. Okuneye, A. E. Obayelu

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Nigeria is rich with abundant resources with an immense contribution of the forest resource to her economic development and to the livelihood of the rural populace over the years. However, this important resource has continued to shrink because it is not sustainably used, managed or conserved. The loss of forest cover has far reaching consequences on regional, national and global economy as well as the environment. This paper reviewed the Nigeria forest management policies, the challenges and willingness to pay (WTP) for management of the community forests in Ogun State, Nigeria. Data for the empirical investigation were obtained using a cross-section survey of 160 rural households by multistage sampling technique. The WTP was assessed by the Dichotomous Choice Contingent Valuation. One major findings is that, the Nigerian forest reserves is established in order to conserve and manage forest resources but has since been neglected while the management plans are either non-existent or abandoned. Also, the free areas termed the community forests where people have unrestricted access to exploit are fast diminishing in both contents and scale. The mean WTP for sustainable management of community forests in the study area was positive with a value of ₦389.04/month. The study recommends policy measures aimed at participatory forest management plan which will include the rural communities in the management of community forests. This will help ensure sustainable management of forest resources as well as improve the welfare of the rural households.

Keywords: forests, management, WTP, Nigeria

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Authors: Rajkumar N. Ingle

Abstract:

In this paper, the existence of a solution of nonlinear functional random differential equations of the first order is proved under caratheodory condition. The study of the functional random differential equation has got importance in the random analysis of the dynamical systems of universal phenomena. Objectives: Nonlinear functional random differential equation is useful to the scientists, engineers, and mathematicians, who are engaged in N.F.R.D.E. analyzing a universal random phenomenon, govern by nonlinear random initial value problems of D.E. Applications of this in the theory of diffusion or heat conduction. Methodology: Using the concepts of probability theory, functional analysis, generally the existence theorems for the nonlinear F.R.D.E. are prove by using some tools such as fixed point theorem. The significance of the study: Our contribution will be the generalization of some well-known results in the theory of Nonlinear F.R.D.E.s. Further, it seems that our study will be useful to scientist, engineers, economists and mathematicians in their endeavors to analyses the nonlinear random problems of the universe in a better way.

Keywords: Random Fixed Point Theorem, functional random differential equation, N.F.R.D.E., universal random phenomenon

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Authors: Ashish Dhamaniya, Vineet Jain, Rajesh Chouhan

Abstract:

Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS.

Keywords: stream speed, urban roads, machine learning, traffic flow

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Authors: Sri Wartini

Abstract:

Based on the sovereignty over natural resources principle, the Indonesian government has an authority to exploit the natural resources within a national jurisdiction of Indonesia. The forest is one of the natural resources which is very valuable for Indonesia. It becomes the source of raw material for many industrial activities, such as pharmaceutical industry, pulp industry, and household furniture industry. Hence, it contributes to the economic development of Indonesia. However, the exploitation of the forest may cause negative impacts, such as environmental pollution and environmental degradation. The implementation of the sovereignty over natural resources principle in Indonesia may jeopardize the forest and affect the sustainability of the forest if there is no appropriate policy of the government to exploit the forest in a sustainable manner. The exploitation of the forest in Indonesia, in some extent, has caused serious impact to environment and biodiversity. Hence, in order to sustain and to maintain the forest as the valuable resources to the future generation, the government of Indonesia has already adopted many programmes and action plans. The aim of the research is to undertake a critical examination of the issues relating to the the implementation of sovereignty over natural resources to the exploitation of the forest in Indonesia. It is a normative research and the methodology employed in this research is library research. While the approaches employed in the research are conceptual approach., statutory approach, and comparative approach. The research finds that the implementation of sovereignty over natural resources principle in the exploitation of the forest in Indonesia is limited by other principles of international environmental law, such as sustainable development principle, intergenerational principle and common concern principle which have been adopted in the government policy and various regulations regarding the exploitation of the forest in Indonesia.

Keywords: Environmental damage, negative impacts, pollution, the sovereignty over natural resources

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Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

Abstract:

This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: Janece Jean A. Polizon, Victor B. Amoroso

Abstract:

Mt. Hamiguitan Range in Davao Oriental, Mindanao Island, Philippines is the only protected area with pygmy forest and a priority site for protection and conservation. This range harbors different vegetation types such as agroecosystem, dipterocarp forest, montane forest and mossy forest. This study was conducted to determine the diversity of trees and shrubs in different vegetation types of Mt. Hamiguitan Range. Transect walk and 16 sampling plots of 20 x 20 m were established in the different vegetation types. Specimens collected were classified and identified using the Flora Malesiana and type images. Assessment of status was determined based on International Union for the Conservation of Nature (IUCN). There were 223 species of trees, 141 genera and 71 families. Of the vegetation types, the pygmy forest obtained a comparatively high diversity value of H=1.348 followed by montane forest with H=1.284. The high species importance value (SIV) of Diospyros philippinensis for trees indicates that these species have an important role in regulating the stability of the ecosystem. The tree profile of the pygmy forest is different due to the ultramafic substrate causing the dwarfness of the trees. These forest types should be given high priority for protection and conservation.

Keywords: diversity, Mt Hamiguitan, vegetation, trees, shrubs

Procedia PDF Downloads 371