Search results for: similarity metrics

Authors: Ravindra Dastikop, G. S. Thyagaraju, U. P. Kulkarni

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Even though the number of context aware applications is increasing day by day along with the users, till today there is no generic programming paradigm for context aware applications. This situation could be remedied by design and developing the appropriate context modeling and programming paradigm for context aware applications. In this paper, we are proposing the static context model and metrics for validating the expressiveness and understandability of the model. The proposed context modeling is a way of describing a situation of user using context entities , attributes and relationships .The model which is an extended and hybrid version of ER model, ontology model and Graphical model is specifically meant for expressing and understanding the user situation in context aware environment. The model is useful for understanding context aware problems, preparing documentation and designing programs and databases. The model makes use of context entity attributes relationship (CEAR) diagram for representation of association between the context entities and attributes. We have identified a new set of graphical notations for improving the expressiveness and understandability of context from the end user perspective .

Keywords: user context, context entity, context entity attributes, situation, sensors, devices, relationships, actors, expressiveness, understandability

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Authors: Oumaima Lahmar

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This paper aims to define a structured topography for finance researchers seeking to navigate the body of knowledge in their extrapolation of finance phenomena. To make sense of the body of knowledge in finance, a probabilistic topic modeling approach is applied on 6000 abstracts of academic articles published in three top journals in finance between 1976 and 2020. This approach combines both machine learning techniques and natural language processing to statistically identify the conjunctions between research articles and their shared topics described each by relevant keywords. The topic modeling analysis reveals 35 coherent topics that can well depict finance literature and provide a comprehensive structure for the ongoing research themes. Comparing the extracted topics to the Journal of Economic Literature (JEL) classification system, a significant similarity was highlighted between the characterizing keywords. On the other hand, we identify other topics that do not match the JEL classification despite being relevant in the finance literature.

Keywords: finance literature, textual analysis, topic modeling, perplexity

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Authors: Liana Lortkipanidze, Nino Amirezashvili, Nino Javashvili

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The purpose of the grammatical dictionary is to provide information on the morphological and syntactic characteristics of the basic word in the dictionary entry. The electronic grammatical dictionaries are used as a tool of automated morphological analysis for texts processing. The Georgian Grammatical Dictionary should contain grammatical information for each word: part of speech, type of declension/conjugation, grammatical forms of the word (paradigm), alternative variants of basic word/lemma. In this paper, we present the system for compiling the Georgian Grammatical Dictionary automatically. We propose dictionary-based methods for extending grammatical lexicons. The input lexicon contains only a few number of words with identical grammatical features. The extension is based on similarity measures between features of words; more precisely, we add words to the extended lexicons, which are similar to those, which are already in the grammatical dictionary. Our dictionaries are corpora-based, and for the compiling, we introduce the method for lemmatization of unknown words, i.e., words of which neither full form nor lemma is in the grammatical dictionary.

Keywords: acquisition of lexicon, Georgian grammatical dictionary, lemmatization rules, morphological processor

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Authors: Abeer M. Algeblawi

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Fifteen isolates of Agrobacterium tumefaciens were obtained from crown gall samples collected from six locations (Tripoli, Alzahra, Ain-Zara, Alzawia, Alazezia in Libya) from Grape (Vitis vinifera L.), Pear (Pyrus communis L.), Peach (Prunus persica L.) and Alexandria in Egypt from Guava (Psidium guajava L.) trees, Artichoke (Cynara cardunculus L.) and Sugar beet (Beta vulgaris L.). Total DNA was extracted from the eight isolates as well as the identification of six isolates used into Polymerase Chain Reaction (PCR) analysis and Random Amplified Polymorphic DNA (RAPD) technique were used. High similarity (55.5%) was observed among the eight A. tumefaciens isolates (Agro1, Agro2, Agro3, Agro4, Agro5, Agro6, Agro7, and Agro8). The PCR amplification products were resulting from the use of two specific primers (virD2A-virD2C). Analysis induction six isolates of A. tumefaciens obtained from different hosts. A visible band was specific to A. tumefaciens of (220 bp, 224 bp) and 338 bp produced with total DNA extracted from bacterial cells.

Keywords: Agrobacterium tumefaciens, crown gall, identification, molecular characterization, PCR, RAPD

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Authors: Tanish Mittal, Preyansh Agrawal, Esha Pahwa, Aarya Makwana

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Multi-Scale and U-shaped Networks are widely used in various image restoration problems, including deblurring. Keeping in mind the wide range of applications, we present a comparison of these architectures and their effects on image deblurring. We also introduce a new block called as NFResblock. It consists of a Fast Fourier Transformation layer and a series of modified Non-Linear Activation Free Blocks. Based on these architectures and additions, we introduce NFResnet and NFResnet+, which are modified multi-scale and U-Net architectures, respectively. We also use three differ-ent loss functions to train these architectures: Charbonnier Loss, Edge Loss, and Frequency Reconstruction Loss. Extensive experiments on the Deep Video Deblurring dataset, along with ablation studies for each component, have been presented in this paper. The proposed architectures achieve a considerable increase in Peak Signal to Noise (PSNR) ratio and Structural Similarity Index (SSIM) value.

Keywords: multi-scale, Unet, deblurring, FFT, resblock, NAF-block, nfresnet, charbonnier, edge, frequency reconstruction

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Authors: Gon Park

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Seoul in South Korea established the 2030 Seoul City Master Plan that contains green-link projects to connect critical green areas within the city. However, the plan does not have detailed analyses for green infrastructure to incorporate land-cover information to many structural classes. This study maps green infrastructure networks of Seoul for complementing their green plans with identifying and raking green areas. Hubs and links of main elements of green infrastructure have been identified from incorporating cadastral data of 967,502 parcels to 135 of land use maps using geographic information system. Network analyses were used to rank hubs and links of a green infrastructure map with applying a force-directed algorithm, weighted values, and binary relationships that has metrics of density, distance, and centrality. The results indicate that network analyses using cadastral parcel data can be used as the framework to identify and rank hubs, links, and networks for the green infrastructure planning under a variable scenarios of green areas in cities.

Keywords: cadastral data, green Infrastructure, network analysis, parcel data

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

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In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

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Authors: Aadhya Kaul, Gautam Manocha, Preeti Nagrath

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Our environment comprises of a wide variety of plants that are similar to each other and sometimes the similarity between the plants makes the identification process tedious thus increasing the workload of the botanist all over the world. Now all the botanists cannot be accessible all the time for such laborious plant identification; therefore, there is an urge for a quick classification model. Also, along with the identification of the plants, it is also necessary to classify the plant as healthy or not as for a good lifestyle, humans require good food and this food comes from healthy plants. A large number of techniques have been applied to classify the plants as healthy or diseased in order to provide the solution. This paper proposes one such method known as anomaly detection using autoencoders using a set of collections of leaves. In this method, an autoencoder model is built using Keras and then the reconstruction of the original images of the leaves is done and the threshold loss is found in order to classify the plant leaves as healthy or diseased. A dataset of plant leaves is considered to judge the reconstructed performance by convolutional autoencoders and the average accuracy obtained is 71.55% for the purpose.

Keywords: convolutional autoencoder, anomaly detection, web application, FLASK

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Ibrahim Ilker Ozyigit, Ertugrul Filiz, Seda Birbilener, Semsettin Kulac, Zeki Severoglu

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In this study, we have performed genetic diversity analysis of Tilia tomentosa genotypes by using randomly amplified polymorphic DNA (RAPD) primers. A total of 28 genotypes, including 25 members from the urban ecosystem and 3 genotypes from forest ecosystem as outgroup were used. 8 RAPD primers produced a total of 53 bands, of which 48 (90.6 %) were polymorphic. Percentage of polymorphic loci (P), observed number of alleles (Na), effective number of alleles (Ne), Nei's (1973) gene diversity (h), and Shannon's information index (I) were found as 94.29 %, 1.94, 1.60, 0.34, and 0.50, respectively. The unweighted pair-group method with arithmetic average (UPGMA) cluster analysis revealed that two major groups were observed. The genotypes of urban and forest ecosystems showed a high genetic similarity between 28% and 92% and these genotypes did not separate from each other in UPGMA tree. Also, urban and forest genotypes clustered together in principal component analysis (PCA).

Keywords: Tilia tomentosa, genetic diversity, urban ecosystem, RAPD, UPGMA

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Authors: Heba M. Mohamed

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Owing to scientific and public concern about health and environment and seeking for a better quality of life; “Green”, “Environmentally” and “Eco” friendly practices have been presented and implemented in different research areas. Subsequently, researchers’ attention is drawn in the direction of greening the analytical methodologies and taking the Green Analytical Chemistry principles (GAC) into consideration. It is of high importance to appraise the environmental impact of each of the implemented green approaches. Compared to the other traditional green metrics (E-factor, Atom economy and the process profile), the eco scale is the optimum choice to assess the environmental impact of the analytical procedures used for pharmaceuticals analysis. For analytical methodologies, Eco-Scale is calculated by allotting penalty points to any factor of the used analytical procedure which disagree and not match with the model green analysis, where the perfect green analysis has its Eco-Scale value of 100. In this work, calculation and comparison of the Eco-Scale for some of the reported green analytical methods was done, to accentuate their greening potentials. Where the different scores can reveal how green the method is, compared to the ideal value. The study emphasizes that greenness measurement is not only about the waste quantity determination but also dictates a holistic scheme, considering all factors.

Keywords: eco scale, green analysis, environmentally friendly, pharmaceuticals analysis

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Authors: Roslinda Nazar, Ezad Hafidz Hafidzuddin, Norihan M. Arifin, Ioan Pop

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In this paper, the problem of steady laminar boundary layer flow and heat transfer over a permeable exponentially stretching/shrinking sheet with generalized slip velocity is considered. The similarity transformations are used to transform the governing nonlinear partial differential equations to a system of nonlinear ordinary differential equations. The transformed equations are then solved numerically using the bvp4c function in MATLAB. Dual solutions are found for a certain range of the suction and stretching/shrinking parameters. The effects of the suction parameter, stretching/shrinking parameter, velocity slip parameter, critical shear rate, and Prandtl number on the skin friction and heat transfer coefficients as well as the velocity and temperature profiles are presented and discussed.

Keywords: boundary layer, exponentially stretching/shrinking sheet, generalized slip, heat transfer, numerical solutions

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Authors: Varong Pongsai

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The objective of this paper is introducing a new method of accounting posting which is called Matrix Method Posting. This method is based on the Matrix operation of pure Mathematics. Although, accounting field is classified as one of the social-science knowledge, many of accounting operations are placed by Mathematics sign and operation. Through the operation applying, it seems to be that the operations of Mathematics should be applied to accounting possibly. So, this paper tries to over-lap Mathematics logic to accounting logic smoothly. According to the context of discovery, deductive approach is employed to prove a simultaneously logical concept of both Mathematics and Accounting. The result proves that the Matrix can be placed to operate accounting perfectly, because Matrix and accounting logic also have a similarity concept which is balancing 2 sides during operations. Moreover, the Matrix posting also has a lot of benefit. It can help financial analyst calculating financial ratios comfortably. Furthermore, the matrix determinant which is a signature operation itself also helps auditors checking out the correction of clients’ recording. If the determinant is not equaled to 0, it will point out that the recording process of clients getting into the problem. Finally, the Matrix should be easily determining a concept of merger and consolidation far beyond the present day concept.

Keywords: matrix method posting, deductive approach, determinant, accounting application

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Authors: Raja Jarray, Imen Zaghbani, Soufiene Bouallègue

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Path planning for Unmanned Aerial Vehicles (UAVs) in dynamic environments poses a significant challenge. Adapting planning algorithms to these complex environments with moving obstacles is a major task in real-world robotics. This article introduces a path-planning strategy based on a Q-learning algorithm, which enables an effective response to avoid moving obstacles while ensuring mission feasibility. A dynamic reward function is introduced, causing the UAV to use the real-time distance between its current position and the destination as training data. The objective of the proposed Q-learning-based path planning algorithm is to guide the drone through an optimal flight itinerary in a dynamic, collision-free environment. The proposed Q-learning-based UAV planner is evaluated considering numerous commonly used performance metrics. Demonstrative results are provided and discussed to show the effectiveness and practicability of such an artificial intelligence-based path planning approach.

Keywords: unmanned aerial vehicles, dynamic path planning, moving obstacles, reinforcement-learning, Q-learning

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Authors: Mst. Tuhin Akter, Sharun Akter Khushbu, S. M. Shaqib

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A real-time system architecture is highly effective for monitoring and detecting various damaged products or fruits that may deteriorate over time or become infected with diseases. Deep learning models have proven to be effective in building such architectures. However, building a deep learning model from scratch is a time-consuming and costly process. A more efficient solution is to utilize deep neural network (DNN) based transfer learning models in the real-time monitoring architecture. This study focuses on using a novel strawberry dataset to develop effective transfer learning models for the proposed real-time monitoring system architecture, specifically for evaluating and detecting strawberry freshness. Several state-of-the-art transfer learning models were employed, and the best performing model was found to be Xception, demonstrating higher performance across evaluation metrics such as accuracy, recall, precision, and F1-score.

Keywords: strawberry freshness evaluation, deep neural network, transfer learning, image augmentation

Procedia PDF Downloads 63

Authors: Abhijith Asok

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This paper presents a generalized approach for the simple linear data transformation, Y=bX, through an integration of multidimensional coordinate geometry, vector space theory and polygonal geometry. The scaling is performed by adding an additional ’Dummy Dimension’ to the n-dimensional data, which helps plot two dimensional component-wise straight lines on pairs of dimensions. The end result is a set of scaled extensions of observations in any of the 2n spatial divisions, where n is the total number of applicable dimensions/dataset variables, created by shifting the n-dimensional plane along the ’Dummy Axis’. The derived scaling factor was found to be dependent on the coordinates of the common point of origin for diverging straight lines and the plane of extension, chosen on and perpendicular to the ’Dummy Axis’, respectively. This result indicates the geometrical interpretation of a linear data transformation and hence, opportunities for a more informed choice of the factor ’b’, based on a better choice of these coordinate values. The paper follows on to identify the effect of this transformation on certain popular distance metrics, wherein for many, the distance metric retained the same scaling factor as that of the features.

Keywords: data transformation, dummy dimension, linear transformation, scaling

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Authors: Dipti Patra, Guguloth Uma, Smita Pradhan

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Registration is a fundamental task in image processing. It is used to transform different sets of data into one coordinate system, where data are acquired from different times, different viewing angles, and/or different sensors. The registration geometrically aligns two images (the reference and target images). Registration techniques are used in satellite images and it is important in order to be able to compare or integrate the data obtained from these different measurements. In this work, mutual information is considered as a similarity metric for registration of satellite images. The transformation is assumed to be a rigid transformation. An attempt has been made here to optimize the transformation function. The proposed image registration technique hybrid PSO-GA incorporates the notion of Particle Swarm Optimization and Genetic Algorithm and is used for finding the best optimum values of transformation parameters. The performance comparision obtained with the experiments on satellite images found that the proposed hybrid PSO-GA algorithm outperforms the other algorithms in terms of mutual information and registration accuracy.

Keywords: image registration, genetic algorithm, particle swarm optimization, hybrid PSO-GA algorithm and mutual information

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Authors: Devatha Kalyan Kumar, R. Poovarasan

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In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets.

Keywords: correlation, congenital diabetics, linear relationship, monotonic function, ranking samples, pediatric

Procedia PDF Downloads 241

Authors: Jaewoo Choi, Ki-Tae Hwang, Eunyoung Ko

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Backgrounds/Aims: Patients with breast cancer are currently classified according to TNM stage. Nevertheless, the actual volume would be mis-estimated, and it would bring on inappropriate diagnosis. Tridimensional volume-stage derived from the ellipsoid formula was presented as useful measure. Methods: The medical records of 480 consecutive breast cancer between January 2001 and March 2013 were retrospectively reviewed. All patients were divided into three groups according to tumor volume by receiver operating characteristic analysis, and the ranges of each volume-stage were that V1 was below 2.5 cc, V2 was exceeded 2.5 and below 10.9 cc, and V3 was exceeded 10.9 cc. We analyzed outcomes of volume-stage and compared disease-free survival (DFS) and overall survival (OS) between size-stage and volume-stage with variant intrinsic factor. Results: In the T2 stage, there were patients who had a smaller volume than 4.2 cc known as maximum value of T1. These findings presented that patients in T1c had poorer DFS than T2-lesser (mean of DFS 48.7 vs. 51.8, p = 0.011). Such is also the case in OS (mean of OS 51.1 vs. 55.3, p = 0.006). The cumulative survival curves for V1, V2 compared T1, T2 showed similarity in DFS (HR 1.9 vs. 1.9), and so did it for V3 compared T3 (HR 3.5 vs. 2.6) significantly. Conclusion: This study demonstrated that tumor volume had good feasibility on the prognosis of patients with breast cancer. We proposed that volume-stage should be considered for an additional stage indicator, particularly in early breast cancer.

Keywords: breast cancer, tridimensional volume of tumor, TNM stage, volume stage

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Authors: Abdelsabour G. A. Khaled, Galal A.R. El-Sherbeny, Ahmed M. Hassanein, Gameel M. G. Aly

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Classical methods of identification, based on agronomical characterization, are not always the most accurate way due to the instability of these characteristics under the influence of the different environments. In order to estimate the genetic diversity, molecular markers provided excellent tools. In this study, Genetic variation of nine Egyptian jojoba shrubs was tested using ISSR (inter simple sequences repeats), RAPD (random amplified polymorphic DNA) markers and based on the morphological characterization. The average of the percentage of polymorphism (%P) ranged between 58.17% and 74.07% for ISSR and RAPD markers, respectively. The range of genetic similarity percents among shrubs based on ISSR and RAPD markers were from 82.9 to 97.9% and from 85.5 to 97.8%, respectively. The average of PIC (polymorphism information content) values were 0.19 (ISSR) and 0.24 (RAPD). In the present study, RAPD markers were more efficient than the ISSR markers. Where the RAPD technique exhibited higher marker index (MI) average (1.26) compared to ISSR one (1.11). There was an insignificant correlation between the ISSR and RAPD data (0.076, P > 0.05). The dendrogram constructed by the combined RAPD and ISSR data gave a relatively different clustering pattern.

Keywords: correlation, molecular markers, polymorphism, marker index

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Authors: Volkan Kaya, Ersin Elbasi

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Watermarking and stenography are getting importance recently because of copyright protection and authentication. In watermarking we embed stamp, logo, noise or image to multimedia elements such as image, video, audio, animation and text. There are several works have been done in watermarking for different purposes. In this research work, we used watermarking techniques to embed patient information into the medical magnetic resonance (MR) images. There are two methods have been used; frequency domain (Digital Wavelet Transform-DWT, Digital Cosine Transform-DCT, and Digital Fourier Transform-DFT) and spatial domain (Least Significant Bits-LSB) domain. Experimental results show that embedding in frequency domains resist against one type of attacks, and embedding in spatial domain is resist against another group of attacks. Peak Signal Noise Ratio (PSNR) and Similarity Ratio (SR) values are two measurement values for testing. These two values give very promising result for information hiding in medical MR images.

Keywords: watermarking, medical image, frequency domain, least significant bits, security

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Authors: Amar Partap Singh Pharwaha, Balkrishan Jindal

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In this paper, a new method is developed for hiding image in a digital image with multilayer security. In the proposed method, the secret image is encrypted in the first instance using a flexible matrix based symmetric key to add first layer of security. Then another layer of security is added to the secret data by encrypting the ciphered data using Pythagorean Theorem method. The ciphered data bits (4 bits) produced after double encryption are then embedded within digital image in the spatial domain using Least Significant Bits (LSBs) substitution. To improve the image quality of the stego-image, an improved form of pixel adjustment process is proposed. To evaluate the effectiveness of the proposed method, image quality metrics including Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), entropy, correlation, mean value and Universal Image Quality Index (UIQI) are measured. It has been found experimentally that the proposed method provides higher security as well as robustness. In fact, the results of this study are quite promising.

Keywords: Pythagorean theorem, pixel adjustment, ciphered data, image hiding, least significant bit, flexible matrix

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Authors: Thota Sai Prakash, B. Yaswanth, Jhade Bhuvaneswar, Marreddy Divakar Reddy, Shyam Ji Gupta

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Across the globe, there are a lot of chronic diseases, and heart disease stands out as one of the most perilous. Sadly, many lives are lost to this condition, even though early intervention could prevent such tragedies. However, identifying heart disease in its initial stages is not easy. To address this challenge, we propose an automated system aimed at predicting the presence of heart disease using advanced techniques. By doing so, we hope to empower individuals with the knowledge needed to take proactive measures against this potentially fatal illness. Our approach towards this problem involves meticulous data preprocessing and the development of predictive models utilizing classification algorithms such as Support Vector Machines (SVM), Decision Tree, and Random Forest. We assess the efficiency of every model based on metrics like accuracy, ensuring that we select the most reliable option. Additionally, we conduct thorough data analysis to reveal the importance of different attributes. Among the models considered, Random Forest emerges as the standout performer with an accuracy rate of 96.04% in our study.

Keywords: support vector machines, decision tree, random forest

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Authors: Mallikarjuna Rao Yamarthy, Subramanyam Makam Venkata, Satya Prasad Kodati

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The main objective of the proposed work is to reduce the overall network traffic incurred by mobility management, packet delivery cost and to increase the resource utilization. The proposed algorithm, An Efficient Resource Management Algorithm (ERMA) for mobility management in wireless mesh networks, relies on pointer based mobility management scheme. Whenever a mesh client moves from one mesh router to another, the pointer is set up dynamically between the previous mesh router and current mesh router based on the distance constraints. The algorithm evaluated for signaling cost, data delivery cost and total communication cost performance metrics. The proposed algorithm is demonstrated for both internet sessions and intranet sessions. The proposed algorithm yields significantly better performance in terms of signaling cost, data delivery cost, and total communication cost.

Keywords: data delivery cost, mobility management, pointer forwarding, resource management, wireless mesh networks

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Authors: Ilian Badjakov, Ivayla Dincheva, Violeta Kondakova, Rossitza Batchvarova

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In this study, we present our initial results on the assessment of genetic diversity among wild Bulgarian berry accessions (Rubus idaeus L. Fragaria Vesca L., Vaccinium vitis-idaea L., Vaccinium myrtillus L.) using Random Amplified Polymorphic DNA (RAPDs) markers. Leaves and fruits were collected from two natural habitats - the Balkan Mountain and the Mountain of Orpheus - Rhodope Mountain. All accessions were screened for their polymorphism using five RAPD primers. The phylogenetic distances calculated from RAPD data ranged from 0.29 to 0.82 thus indicating that a high level of gene diversity is present in the selected genotypes. In order to characterize further the structure and grouping of berry accessions, a dendrogram deriving from UPGMA cluster analysis based on the genetic similarity (GS) coefficient matrix was designed. RAPD analysis provided to be efficient for discrimination of accessions within the same species with similar morphological characters

Keywords: Bulgarian wild berries, genetic diversity, RAPD, UPGMA

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Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

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Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

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Authors: Faheema Khan

Abstract:

To investigate the ability of sensitive and tolerant genotype of Glycine max to adapt to a saline environment in a field, we examined the growth performance, water relation and activities of antioxidant enzymes in relation to photosynthetic rate, chlorophyll a fluorescence, photosynthetic pigment concentration, protein and proline in plants exposed to salt stress. Ten soybean genotypes (Pusa-20, Pusa-40, Pusa-37, Pusa-16, Pusa-24, Pusa-22, BRAGG, PK-416, PK-1042, and DS-9712) were selected and grown hydroponically. After 3 days of proper germination, the seedlings were transferred to Hoagland’s solution (Hoagland and Arnon 1950). The growth chamber was maintained at a photosynthetic photon flux density of 430 μmol m−2 s−1, 14 h of light, 10 h of dark and a relative humidity of 60%. The nutrient solution was bubbled with sterile air and changed on alternate days. Ten-day-old seedlings were given seven levels of salt in the form of NaCl viz., T1 = 0 mM NaCl, T2=25 mM NaCl, T3=50 mM NaCl, T4=75 mM NaCl, T5=100 mM NaCl, T6=125 mM NaCl, T7=150 mM NaCl. The investigation showed that genotype Pusa-24, PK-416 and Pusa-20 appeared to be the most salt-sensitive. genotypes as inferred from their significantly reduced length, fresh weight and dry weight in response to the NaCl exposure. Pusa-37 appeared to be the most tolerant genotype since no significant effect of NaCl treatment on growth was found. We observed a greater decline in the photosynthetic variables like photosynthetic rate, chlorophyll fluorescence and chlorophyll content, in salt-sensitive (Pusa-24) genotype than in salt-tolerant Pusa-37 under high salinity. Numerous primers were verified on ten soybean genotypes obtained from Operon technologies among which 30 RAPD primers shown high polymorphism and genetic variation. The Jaccard’s similarity coefficient values for each pairwise comparison between cultivars were calculated and similarity coefficient matrix was constructed. The closer varieties in the cluster behaved similar in their response to salinity tolerance. Intra-clustering within the two clusters precisely grouped the 10 genotypes in sub-cluster as expected from their physiological findings.Salt tolerant genotype Pusa-37, was further analysed by 2-Dimensional gel electrophoresis to analyse the differential expression of proteins at high salt stress. In the Present study, 173 protein spots were identified. Of these, 40 proteins responsive to salinity were either up- or down-regulated in Pusa-37. Proteomic analysis in salt-tolerant genotype (Pusa-37) led to the detection of proteins involved in a variety of biological processes, such as protein synthesis (12 %), redox regulation (19 %), primary and secondary metabolism (25 %), or disease- and defence-related processes (32 %). In conclusion, the soybean plants in our study responded to salt stress by changing their protein expression pattern. The photosynthetic, biochemical and molecular study showed that there is variability in salt tolerance behaviour in soybean genotypes. Pusa-24 is the salt-sensitive and Pusa-37 is the salt-tolerant genotype. Moreover this study gives new insights into the salt-stress response in soybean and demonstrates the power of genomic and proteomic approach in plant biology studies which finally could help us in identifying the possible regulatory switches (gene/s) controlling the salt tolerant genotype of the crop plants and their possible role in defence mechanism.

Keywords: glycine max, salt stress, RAPD, genomic and proteomic variability

Procedia PDF Downloads 399

Authors: Hafiza Khaddija Saman, Muhammad Sufyan

Abstract:

The increase in availability and popularity of mobile wireless devices has led researchers to develop a wide variety of Mobile Ad-hoc Networking (MANET) protocols to exploit the unique communication opportunities presented by these devices. Devices are able to communicate directly using the wireless spectrum in a peer-to-peer fashion, and route messages through intermediate nodes, however, the nature of wireless shared communication and mobile devices result in many routing and security challenges which must be addressed before deploying a MANET. In this paper, we investigate the range of MANET routing protocols available and discuss the functionalities of several ranging from early protocols such as DSDV to more advanced such as MAODV, our protocol study focuses upon works by Perkins in developing and improving MANET routing. A range of literature relating to the field of MANET routing was identified and reviewed, we also reviewed literature on the topic of securing AODV based MANETs as this may be the most popular MANET protocol. The literature review identified a number of trends within research papers such as exclusive use of the random waypoint mobility model, excluding key metrics from simulation results and not comparing protocol performance against available alternatives.

Keywords: protocol, MANET, ad-Hoc, communication

Procedia PDF Downloads 229

Authors: Atefeh Heydari, Mohammadali Tavakoli, Zuriati Ismail, Naomie Salim

Abstract:

Seeking and sharing knowledge on online forums have made them popular in recent years. Although online forums are valuable sources of information, due to variety of sources of messages, retrieving reliable threads with high quality content is an issue. Majority of the existing information retrieval systems ignore the quality of retrieved documents, particularly, in the field of thread retrieval. In this research, we present an approach that employs various quality features in order to investigate the quality of retrieved threads. Different aspects of content quality, including completeness, comprehensiveness, and politeness, are assessed using these features, which lead to finding not only textual, but also conceptual relevant threads for a user query within a forum. To analyse the influence of the features, we used an adopted version of voting model thread search as a retrieval system. We equipped it with each feature solely and also various combinations of features in turn during multiple runs. The results show that incorporating the quality features enhances the effectiveness of the utilised retrieval system significantly.

Keywords: content quality, forum search, thread retrieval, voting techniques

Procedia PDF Downloads 190

Authors: Nichole Haag, Kimberly Velk, Tyler McCune, Chun Wu

Abstract:

Methicillin/multiple-resistant Staphylococcus aureus (MRSA) are infectious bacteria that are resistant to common antibiotics. A previous in silico study in our group has identified a hypothetical protein SAV1226 as one of the potential drug targets. In this study, we reported the bioinformatics characterization, as well as cloning, expression, purification and kinetic assays of hypothetical protein SAV1226 from methicillin/vancomycin-resistant Staphylococcus aureus Mu50 strain. MALDI-TOF/MS analysis revealed a low degree of structural similarity with known proteins. Kinetic assays demonstrated that hypothetical protein SAV1226 is neither a domain of an ATP dependent dihydroxyacetone kinase nor of a phosphotransferase system (PTS) dihydroxyacetone kinase, suggesting that the function of hypothetical protein SAV1226 might be misannotated on public databases such as UniProt and InterProScan 5.

Keywords: Methicillin-resistant Staphylococcus aureus, dihydroxyacetone kinase, essential genes, drug target, phosphoryl group donor

Procedia PDF Downloads 384