Search results for: tree algorithms

Authors: Muhammad Shoaib, Hassan M Al-Swaidan

Abstract:

Saudi Arabia is the major date producer in the world. In order to maximize the production from date tree, pruning of the date trees is required annually. Large amount of this agriculture waste material (palm tree fronds) is available in Saudi Arabia and considered as an ideal source as a precursor for production of activated carbon (AC). The single step procedure for the preparation of micro porous activated carbon (AC) from Saudi date tree fronds using mixture of gases (N2 and CO2) is carried out at carbonization/activation temperature at 850°C and at different ramp rates of 10, 20 and 30 degree per minute. Alloy 330 horizontal reactor is used for tube furnace. Flow rate of nitrogen and carbon dioxide gases are kept at 150 ml/min and 50 ml/min respectively during the preparation. Characterization results reveal that the BET surface area, pore volume, and average pore diameter of the resulting activated carbon generally decreases with the increase in ramp rate. The activated carbon prepared at a ramp rate of 10 degrees/minute attains larger surface area and can offer higher potential to produce activated carbon of greater adsorption capacity from agriculture wastes such as date fronds. The BET surface areas of the activated carbons prepared at a ramp rate of 10, 20 and 30 degree/minute after 30 minutes activation time are 1094, 1020 and 515 m2/g, respectively. Scanning electron microscopy (SEM) for surface morphology, and FTIR for functional groups was carried out that also verified the same trend. Moreover, by increasing the ramp rate from 10 and 20 degrees/min the yield remains same, i.e. 18%, whereas at a ramp rate of 30 degrees/min the yield increases from 18 to 20%. Thus, it is feasible to produce high-quality micro porous activated carbon from date frond agro waste using N2 carbonization followed by physical activation with CO2 and N2 mixture. This micro porous activated carbon can be used as adsorbent of heavy metals from wastewater, NOx SOx emission adsorption from ambient air and electricity generation plants, purification of gases, sewage treatment and many other applications.

Keywords: activated carbon, date tree fronds, agricultural waste, applied chemistry

Procedia PDF Downloads 275

Authors: A. Majidian

Abstract:

The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

Procedia PDF Downloads 347

Authors: Mohamed El-Shikh

Abstract:

Phaseolus vulgaris L. is the world’s second most important bean after soybeans; used for human food and animal feed. It has generally been linked to reduced risk of cardiovascular disease, diabetes mellitus, obesity, cancer and diseases of digestive tract. The effectiveness of ISSR in achievement of the genetic diversity among 60 common bean accessions; represent various germplasms around the world was investigated. In general, the studied Phaseolus vulgaris accessions were divided into 2 major groups. All of the South-American accessions were separated into the second major group. These accessions may have different genetic features that are distinct from the rest of the accessions clustered in the major group. Asia and Europe accessions (1-20) seem to be more genetically similar (99%) to each other as they clustered in the same sub-group. The American and African varieties showed similarities as well and clustered in the same sub-tree group. In contrast, Asian and American accessions No. 22 and 23 showed a high level of genetic similarities, although these were isolated from different regions. The phylogenetic tree showed that all the Asian accessions (along with Australian No. 59 and 60) were similar except Indian and Yemen accessions No. 9 and 20. Only Netherlands accession No. 3 was different from the rest of European accessions. Morocco accession No. 52 was genetically different from the rest of the African accessions. Canadian accession No. 44 seems to be different from the other North American accessions including Guatemala, Mexico and USA.

Keywords: phylogenetic tree, Phaseolus vulgaris, ISSR technique, genetics

Procedia PDF Downloads 405

Authors: Shaowei Sun

Abstract:

Let G be a graph with vertex set V(G)={v_1,v_2,...,v_n} and edge set E(G). For any vertex v belong to V(G), let d_v denote the degree of v. The normalized Laplacian matrix of the graph G is the matrix where the non-diagonal (i,j)-th entry is -1/(d_id_j) when vertex i is adjacent to vertex j and 0 when they are not adjacent, and the diagonal (i,i)-th entry is the di. In this paper, we discuss some bounds on the largest and the second smallest normalized Laplacian eigenvalue of trees and graphs. As following, we found some new bounds on the second smallest normalized Laplacian eigenvalue of tree T in terms of graph parameters. Moreover, we use Sage to give some conjectures on the second largest and the third smallest normalized eigenvalues of graph.

Keywords: graph, normalized Laplacian eigenvalues, normalized Laplacian matrix, tree

Procedia PDF Downloads 326

Authors: Nilubon Kurubanjerdjit, Nattakarn Iam-On, Ka-Lok Ng

Abstract:

MicroRNAs are small non-coding RNA found in many different species. They play crucial roles in cancer such as biological processes of apoptosis and proliferation. The identification of microRNA-target genes can be an essential first step towards to reveal the role of microRNA in various cancer types. In this paper, we predict miRNA-target genes for lung cancer by integrating prediction scores from miRanda and PITA algorithms used as a feature vector of miRNA-target interaction. Then, machine-learning algorithms were implemented for making a final prediction. The approach developed in this study should be of value for future studies into understanding the role of miRNAs in molecular mechanisms enabling lung cancer formation.

Keywords: microRNA, miRNAs, lung cancer, machine learning, Naïve Bayes, SVM

Procedia PDF Downloads 396

Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

Abstract:

We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

Procedia PDF Downloads 542

Authors: Behnam Tavakkol

Abstract:

Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

Procedia PDF Downloads 212

Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

Abstract:

Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

Procedia PDF Downloads 443

Authors: Muhammad Zaheer Awan, Adnan Qadir, Zeeshan Anjum

Abstract:

Housefly, Musca domestica Linnaeus is ubiquitous and hazardous for Homo sapiens and livestock in sundry venerations. Musca domestica cart 100 different pathogens, such as typhoid, salmonella, bacillary dysentery, tuberculosis, anthrax and parasitic worms. The flies in rural areas usually carry more pathogens. Houseflies feed on liquid or semi-liquid substances besides solid materials which are softened by saliva. Neem botanically known as Azadirachta indica belongs to the family Meliaceae and is an indigenous tree to Pakistan. The neem tree is also one such tree which has been revered by the Pakistanis and Kashmiris for its medicinal properties. Present study showed neem seed extract has potentially toxic ability that affect Total Haemocyte Count (THC) and Differential Haemocytes Count (DHC) in insect’s blood cells, of the housefly. A significant variation in haemolymph density was observed just after application, 30 minutes and 60 minutes post treatment in term of THC and DHC in comparison with novastar. The study strappingly acclaim use of neem seed extract as insecticide as compare to artificial insecticides.

Keywords: neem, Azadirachta indica, Musca domestica, differential haemocyte count (DHC), total haemocytes count (DHC), novastar

Procedia PDF Downloads 199

Authors: S. Curteanu, F. Leon, M. Gavrilescu, S. A. Floria

Abstract:

Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models.

Keywords: artificial neural networks, human behaviors of learning and cooperation, human competitive behavior, optimization algorithms

Procedia PDF Downloads 105

Authors: A. Boudjemai, A. Zafrane, R. Hocine

Abstract:

Honeycomb sandwich panels are increasingly used in the construction of space vehicles because of their outstanding strength, stiffness and light weight properties. However, the use of honeycomb sandwich plates comes with difficulties in the design process as a result of the large number of design variables involved, including composite material design, shape and geometry. Hence, this work deals with the presentation of an optimal design of hexagonal honeycomb sandwich structures subjected to space environment. The optimization process is performed using a set of algorithms including the gravitational search algorithm (GSA). Numerical results are obtained and presented for a set of algorithms. The results obtained by the GSA algorithm are much better compared to other algorithms used in this study.

Keywords: optimization, gravitational search algorithm, genetic algorithm, honeycomb plate

Procedia PDF Downloads 373

Authors: Moung Young Lee, Chul Gyu Song

Abstract:

Photoacoustic imaging is the imaging technology that combines the optical imaging and ultrasound. This provides the high contrast and resolution due to optical imaging and ultrasound imaging, respectively. We developed the real-time photoacoustic tomography (PAT) system using linear-ultrasound transducer and digital acquisition (DAQ) board. There are two types of algorithm for reconstructing the photoacoustic signal. One is back-projection algorithm, the other is FFT algorithm. Especially, we used the non-uniform FFT algorithm. To evaluate the performance of our system and algorithms, we monitored two wires that stands at interval of 2.89 mm and 0.87 mm. Then, we compared the images reconstructed by algorithms. Finally, we monitored the two hairs crossed and compared between these algorithms.

Keywords: back-projection, image comparison, non-uniform FFT, photoacoustic tomography

Procedia PDF Downloads 430

Authors: Eman W. Boghdady, A. R. Shehata, M. A. Azem

Abstract:

Database (DB) security demands permitting authorized users and prohibiting non-authorized users and intruders actions on the DB and the objects inside it. Organizations that are running successfully demand the confidentiality of their DBs. They do not allow the unauthorized access to their data/information. They also demand the assurance that their data is protected against any malicious or accidental modification. DB protection and confidentiality are the security concerns. There are four types of controls to obtain the DB protection, those include: access control, information flow control, inference control, and cryptographic. The cryptographic control is considered as the backbone for DB security, it secures the DB by encryption during storage and communications. Current cryptographic techniques are classified into two types: traditional classical cryptography using standard algorithms (DES, AES, IDEA, etc.) and chaos cryptography using continuous (Chau, Rossler, Lorenz, etc.) or discreet (Logistics, Henon, etc.) algorithms. The important characteristics of chaos are its extreme sensitivity to initial conditions of the system. In this paper, DB-security systems based on chaotic algorithms are described. The Pseudo Random Numbers Generators (PRNGs) from the different chaotic algorithms are implemented using Matlab and their statistical properties are evaluated using NIST and other statistical test-suits. Then, these algorithms are used to secure conventional DB (plaintext), where the statistical properties of the ciphertext are also tested. To increase the complexity of the PRNGs and to let pass all the NIST statistical tests, we propose two hybrid PRNGs: one based on two chaotic Logistic maps and another based on two chaotic Henon maps, where each chaotic algorithm is running side-by-side and starting from random independent initial conditions and parameters (encryption keys). The resulted hybrid PRNGs passed the NIST statistical test suit.

Keywords: algorithms and data structure, DB security, encryption, chaotic algorithms, Matlab, NIST

Procedia PDF Downloads 263

Authors: Ken Hampshire, Thomas Mazzuchi, Shahram Sarkani

Abstract:

As a subset of metaheuristics, nature-inspired optimization algorithms such as particle swarm optimization (PSO) have shown promise both in solving intractable problems and in their extensibility to novel problem formulations due to their general approach requiring few assumptions. Unfortunately, single instantiations of algorithms require detailed tuning of parameters and cannot be proven to be best suited to a particular illustrative problem on account of the “no free lunch” (NFL) theorem. Using these algorithms in real-world problems requires exquisite knowledge of the many techniques and is not conducive to reconciling the various approaches to given classes of problems. This research aims to present a unified view of PSO-based approaches from the perspective of relevant systems engineering problems, with the express purpose of then eliciting the best solution for any problem formulation in an ensemble learning bucket of models approach. The central hypothesis of the research is that extending the PSO algorithms found in the literature to real-world optimization problems requires a general ensemble-based method for all problem formulations but a specific implementation and solution for any instance. The main results are a problem-based literature survey and a general method to find more globally optimal solutions for any systems engineering optimization problem.

Keywords: particle swarm optimization, nature-inspired optimization, metaheuristics, systems engineering, ensemble learning

Procedia PDF Downloads 93

Authors: Wafa Slaibi Alsharafat

Abstract:

Genetic algorithms (GA) are random algorithms as random numbers that are generated during the operation of the algorithm determine what happens. This means that if GA is applied twice to optimize exactly the same problem it might produces two different answers. In this project, we propose an evolutionary algorithm and Genetic Algorithm (GA) to be implemented in symmetric encryption and decryption. Here, user's message and user secret information (key) which represent plain text to be transferred into cipher text.

Keywords: GA, encryption, decryption, crossover

Procedia PDF Downloads 443

Authors: Faisal Aburub, Wael Hadi

Abstract:

Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: classification, data mining, evaluation measures, groundwater

Procedia PDF Downloads 275

Authors: Oluwatobi Emmanuel Olaniyi, Babafemi George Ogunjemite

Abstract:

The high rate of infrastructural development in Ikogosi warm spring towards harnessing her great ecotourism potentials calls for a serious concern, as more forest areas are been opened up for public access and the landscape is modified. On this note, we investigated the implication of ecotourism development on the floristic composition and forest structure in Ikogosi. The study aimed at identifying the past and present status of infrastructural development, assessing and comparing the floristic composition and structure of the built- up/ recreational areas and undisturbed forested areas, to infer on the impact of ecotourism development on the study site. We conducted stakeholder interview and field observation to identify the past and present status of infrastructural development respectively. A total of ten quadrants were employed in the vegetation assessment to characterize the woody tree species composition, diameter at breast height and height, to obtain mean indices characterizing each part of the site. These indices were compared using T – test analysis. A total of 49 different woody tree species distributed in 21 families were identified in the built-in/ recreational areas while 67 different woody tree species belonging to 25 families were recorded in the undeveloped forested areas. Although, the latter has a higher mean diameter at breast height of woody trees, it was not significantly different from the former (T-test = -0.74, p = 0.46). On the contrary, the built-up area had a higher mean trees height than the undeveloped areas, but the difference was not statistically significant (T-test= 1.04, p = 0.30). Despite these, the slight reduction in richness and diversity of the woody tree species in the built- up/ recreational areas implies mitigating the negative effects of infrastructural development on the warm spring's vegetation.

Keywords: ecosystem services, forest structure, vegetation assessment, warm-spring

Procedia PDF Downloads 506

Authors: Getamesay Kassaye Dimru

Abstract:

The cultivation of fruit tree in degraded areas has two-fold importance. Firstly, it improves food availability and income, and secondly, it promotes the conservation of soil and water improving, in turn, the productivity of the land. The main objectives of this study are to identify the determinant of farmer's fruit trees plantation decision and to major fruit production challenges and opportunities of the study area. The analysis was made using primary data collected from 60 sample household selected randomly from the study area in 2016. The primary data was supplemented by data collected from a key informant. In addition to the descriptive statistics and statistical tests (Chi-square test and t-test), a logit model was employed to identify the determinant of fruit tree plantation decision. Drought, pest incidence, land degradation, lack of input, lack of capital and irrigation schemes maintenance, lack of misuse of irrigation water and limited agricultural personnel are the major production constraints identified. The opportunities that need to further exploited are better access to irrigation, main road access, endowment of preferred guava variety, experience of farmers, and proximity of the study area to research center. The result of logit model shows that from different factors hypothesized to determine fruit tree plantation decision, age of the household head accesses to market and perception of farmers about fruits' disease and pest resistance are found to be significant. The result has revealed important implications for the promotion of fruit production for both land degradation control and rehabilitation and increasing the livelihood of farming households.

Keywords: degradation, fruit, irrigation, pest

Procedia PDF Downloads 225

Authors: Mehdi Ameur, Ahmed Essadki, Tamou Nasser

Abstract:

The purpose of this paper is the evaluation of photovoltaic system with MPPT techniques. This system is developed by combining the models of established solar module and DC-DC converter with the algorithms of perturbing and observing (P&O), incremental conductance (INC) and fuzzy logic controller (FLC). The system is simulated under different climate conditions and MPPT algorithms to determine the influence of these conditions on characteristic power-voltage of PV system. According to the comparisons of the simulation results, the photovoltaic system can extract the maximum power with precision and rapidity using the MPPT algorithms discussed in this paper.

Keywords: fuzzy logic controller, FLC, hill climbing, HC, incremental conductance (INC), perturb and observe (P&O), maximum power point, MPP, maximum power point tracking, MPPT

Procedia PDF Downloads 506

Authors: Sagri Sharma

Abstract:

Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

Procedia PDF Downloads 426

Authors: Sahin Emrah Amrahov, Fatih Aybar, Serhat Dogan

Abstract:

In this paper we consider the rule reduct generation problem. Rule Reduct Generation (RG) and Modified Rule Generation (MRG) algorithms, that are used to solve this problem, are well-known. Alternative to these algorithms, we develop Pruning Rule Generation (PRG) algorithm. We compare the PRG algorithm with RG and MRG.

Keywords: rough sets, decision rules, rule induction, classification

Procedia PDF Downloads 524

Authors: Tigabu Dagne Akal

Abstract:

While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

Procedia PDF Downloads 293

Authors: Neelu Raina, Parvez Singh Slathia, Deepali Bhagat, Preeti Sharma

Abstract:

Pretreatment of lignocellulosic biomass for generating suitable substrates (starch/ sugars) for conversion to bioethanol is the most crucial step. In present study waste from furniture industry i.e sawdust from softwood Cedrus deodara (deodar) and hardwood Shorea robusta (sal) was used as lignocellulosic biomass. Thermochemical pretreatment was given by autoclaving at 121°C temperature and 15 psi pressure. Acids (H2SO4,HCl,HNO3,H3PO4), alkali (NaOH,NH4OH,KOH,Ca(OH)2) and organic acids (C6H8O7,C2H2O4,C4H4O4) were used at 0.1%, 0.5% and 1% concentration without giving any residence time. 1% HCl gave maximum sugar yield of 3.6587g/L in deodar and 6.1539 g/L in sal. For biological pretreatment a fungi isolated from decaying wood was used , sawdust from deodar tree species was used as a lignocellulosic substrate and before thermochemical pretreatment sawdust was treated with fungal culture at 37°C under submerged conditions with a residence time of one week followed by a thermochemical pretreatment methodology. Higher sugar yields were obtained with sal tree species followed by deodar tree species, i.e., 6.0334g/L in deodar and 8.3605g/L in sal was obtained by a combined biological and thermochemical pretreatment. Use of acids along with biological pretreatment is a favourable factor for breaking the lignin seal and thus increasing the sugar yield. Sugar estimation was done using Dinitrosalicyclic assay method. Result validation is being done by statistical analysis.

Keywords: lignocellulosic biomass, bioethanol, pretreatment, sawdust

Procedia PDF Downloads 409

Authors: Xinghang Lv, Tao Peng, Jia Chen, Junping Liu, Xinrong Hu, Ruhan He, Minghua Jiang, Wenli Cao

Abstract:

As one of the most dangerous vulnerabilities, effective detection of buffer overflow vulnerabilities is extremely necessary. Traditional detection methods are not accurate enough and consume more resources to meet complex and enormous code environment at present. In order to resolve the above problems, we propose the method for Buffer overflow detection based on Abstract syntax tree, Control flow graph, and Data dependency graph (BodeACD) in C/C++ programs with source code. Firstly, BodeACD constructs the function samples of buffer overflow that are available on Github, then represents them as code representation sequences, which fuse control flow, data dependency, and syntax structure of source code to reduce information loss during code representation. Finally, BodeACD learns vulnerability patterns for vulnerability detection through deep learning. The results of the experiments show that BodeACD has increased the precision and recall by 6.3% and 8.5% respectively compared with the latest methods, which can effectively improve vulnerability detection and reduce False-positive rate and False-negative rate.

Keywords: vulnerability detection, abstract syntax tree, control flow graph, data dependency graph, code representation, deep learning

Procedia PDF Downloads 165

Authors: Rishabh Srivastav, Divyam Sharma

Abstract:

We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic.

Keywords: categorical data, fuzzy logic, genetic algorithm, K modes clustering, rough sets

Procedia PDF Downloads 243

Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

Abstract:

Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

Procedia PDF Downloads 414

Authors: Muhammad A. Khan, Waseem Shahzad

Abstract:

Analysis of protein sequences is carried out for the purpose to discover their structural and ancestry relationship. Sequence similarity determines similar protein structures, similar function, and homology detection. Biological sequences composed of amino acid residues or nucleotides provide significant information through sequence alignment. In this paper, we present a new similarity/dissimilarity measure to sequence alignment based on the primary structure of a protein. The approach finds the distance between the two given sequences using the novel sequence alignment algorithm and a mathematical model. The algorithm runs at a time complexity of O(n²). A distance matrix is generated to construct a phylogenetic tree of different species. The new similarity/dissimilarity measure outperforms other existing methods.

Keywords: alignment, distance, homology, mathematical model, phylogenetic tree

Procedia PDF Downloads 175

Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

Abstract:

Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

Procedia PDF Downloads 75

Authors: Khater N., Rahmine S., Bougoffa C., Bouguenna T., Ouanes H.

Abstract:

Moringa oleifera L. species is considered one of the most useful trees in the world, possessing many interesting properties that make it of great scientific interest. It has been described as the miracle tree, the tree of a thousand virtues, the tree of life and God's gift to man. The present study aims to introduce, produce, and develop Moringa Oleifera as a species with high ecological potential (resistance to biotic and abiotic stresses and productivity), high added value, and multiple virtues. The aim of this work is to study the germination potential of this species under different temperature conditions. In this study, the germination assay was tested in two different temperature ranges: internal (laboratory ambient temperature between 22°c and 25°c) and external (seasonal temperature between 4°c and 8°c). Morphological and physiological analyses were carried out by Shoot length (SL), root length (RL), diameter at the crown (DC), fresh weight of shoots (FWS), fresh weight of roots (FWR), dry weight of shoots (DWS) and dry weight of roots (DWS). For all these variables, the results of the study reveal a significant difference between the two temperature intervals, with a high germination rate of 81. 81% and plant growth was rapid (7cm during 24h) in the laboratory temperature; in contrast to the external temperatures, a germination rate value of around 27% was recorded, and germination took place after 20 days of sowing, with slower plant growth. The results obtained show that a temperature greater than or equal to 25° is the ideal temperature for the germination and growth of moringa seeds and has a positive influence on the speed and percentage of germination.

Keywords: moringa oleifera, temperature, germination rate, growth, biomass

Procedia PDF Downloads 57

Authors: Shajida M., Sakthiyadharshini N. P., Kamalesh S., Aswitha B.

Abstract:

Natural Language Processing (NLP) continues to play a strategic part in complaint discovery and medicine discovery during the current epidemic. This abstract provides an overview of performance analysis with a combination of visualization and classification techniques of NLP for a medical chatbot. Sentiment analysis is an important aspect of NLP that is used to determine the emotional tone behind a piece of text. This technique has been applied to various domains, including medical chatbots. In this, we have compared the combination of the decision tree with heatmap and Naïve Bayes with Word Cloud. The performance of the chatbot was evaluated using accuracy, and the results indicate that the combination of visualization and classification techniques significantly improves the chatbot's performance.

Keywords: sentimental analysis, NLP, medical chatbot, decision tree, heatmap, naïve bayes, word cloud

Procedia PDF Downloads 68