Search results for: type classification

Authors: Zbigniew Wochynski, Justyna Skrzynska, Robert Jedrys, Zdzislaw Kobos

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The aim of the study is to determine the types of motor skills and their impact on achieving results while undergoing Aviation Synthetic Efficiency Test (ASET). The study involved 59 cadets, 21 years-old on average, who are studying on first year for a pilot. The average weight of the respondents is 73.8 kg. The subjects were divided into two groups by weight: up to 73.8 kg -group A (n-30) and above 73,8kg -group B (n-29). All subjects underwent the following tests: running at 40m, 100m, 1000m, 2000m, pull-ups, ASET. In both groups, the cadets were divided into two motor skills types taking into advance 40 m running, pull-ups, 2000 meters running and then subjected to do ASET. There has been shown statistically significant increase in group B in body height, weight and BMI with p <0.0003, p <0.0001, p <0.0001 compared to group A. The results indicate that the dominant motor type in all subjects is the endurance-strength model, which reached the speed V = 1,42m/s in overcoming ASET. This is confirmed by the correlation between 2000m and pull-ups r = 0.37 (p <0.05). In group A, the results indicate that the dominant type of motor is a high-speed-endurance model (26.6%), which reached speed V = 1,42m/s in overcoming ASET. In Group B, there was type of motor speed-strength (20.6%), which reached speed of V = 1.45m/s in overcoming ASET. This confirms the correlation between ASET and pull-ups r = 0.56 (p <0.005). Examined cadets who were having one dominant characteristic achieved worse results is ASET. The best results from all examined cadets in overcoming ASET had the type of motor endurance-strength, in group A endurance-speed model and in group B type of speed-strength

Keywords: ASET, Aviation Synthetic Efficiency Test, motor skills, physical tests, pilot-cadets

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Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez

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The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.

Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning

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Authors: T. R. Sharika, Ashwini Kumar, Kamal Bijlani

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Encryption is the process of converting crucial information’s unreadable to unauthorized persons. Image security is an important type of encryption that secures all type of images from cryptanalysis. A stream cipher is a fast symmetric key algorithm which is used to convert plaintext to cipher text. In this paper we are proposing an image encryption algorithm with Discrete Cosine Transform and Stream Ciphers that can improve compression of images and enhanced security. The paper also explains the use of a shuffling algorithm for enhancing securing.

Keywords: decryption, DCT, encryption, RC4 cipher, stream cipher

Procedia PDF Downloads 363

Authors: Hassan Al Salman

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We consider a nonlinear parabolic cross diffusion model arising in applied mathematics. A fully practical piecewise linear finite element approximation of the model is studied. By using entropy-type inequalities and compactness arguments, existence of a global weak solution is proved. Providing further regularity of the solution of the model, some uniqueness results and error estimates are established. Finally, some numerical experiments are performed.

Keywords: cross diffusion model, entropy-type inequality, finite element approximation, numerical analysis

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Authors: Mona K. Fehling, Jakob Unger, Dietmar J. Hecker, Bernhard Schick, Joerg Lohscheller

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Voice disorders origin from disturbances of the vibration patterns of the two vocal folds located within the human larynx. Consequently, the visual examination of vocal fold vibrations is an integral part within the clinical diagnostic process. For an objective analysis of the vocal fold vibration patterns, the two-dimensional vocal fold dynamics are captured during sustained phonation using an endoscopic high-speed camera. In this work, we present an approach allowing a fully automatic analysis of the high-speed video data including a computerized classification of healthy and pathological voices. The approach bases on a wavelet-based analysis of so-called phonovibrograms (PVG), which are extracted from the high-speed videos and comprise the entire two-dimensional vibration pattern of each vocal fold individually. Using a principal component analysis (PCA) strategy a low-dimensional feature set is computed from each phonovibrogram. From the PCA-space clinically relevant measures can be derived that quantify objectively vibration abnormalities. In the first part of the work it will be shown that, using a machine learning approach, the derived measures are suitable to distinguish automatically between healthy and pathological voices. Within the approach the formation of the PCA-space and consequently the extracted quantitative measures depend on the clinical data, which were used to compute the principle components. Therefore, in the second part of the work we proposed a strategy to achieve a normalization of the PCA-space by registering the PCA-space to a coordinate system using a set of synthetically generated vibration patterns. The results show that owing to the normalization step potential ambiguousness of the parameter space can be eliminated. The normalization further allows a direct comparison of research results, which bases on PCA-spaces obtained from different clinical subjects.

Keywords: Wavelet-based analysis, Multiscale product, normalization, computer assisted classification, high-speed laryngoscopy, vocal fold analysis, phonovibrogram

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Authors: Rahil Bahrami, Kaveh Ashenayi

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This paper presents a systematic classification and comparative analysis of non-isolated bi-directional DC-DC converters. The increasing demand for efficient energy conversion in diverse applications has spurred the development of various converter topologies. In this study, we categorize bi-directional converters into three distinct classes: Inverting, Non-Inverting, and Interleaved. Each category is characterized by its unique operational characteristics and benefits. Furthermore, a practical comparison is conducted by evaluating the results of simulation of each bi-directional converter. BDCs can be classified into isolated and non-isolated topologies. Non-isolated converters share a common ground between input and output, making them suitable for applications with minimal voltage change. They are easy to integrate, lightweight, and cost-effective but have limitations like limited voltage gain, switching losses, and no protection against high voltages. Isolated converters use transformers to separate input and output, offering safety benefits, high voltage gain, and noise reduction. They are larger and more costly but are essential for automotive designs where safety is crucial. The paper focuses on non-isolated systems.The paper discusses the classification of non-isolated bidirectional converters based on several criteria. Common factors used for classification include topology, voltage conversion, control strategy, power capacity, voltage range, and application. These factors serve as a foundation for categorizing converters, although the specific scheme might vary depending on contextual, application, or system-specific requirements. The paper presents a three-category classification for non-isolated bi-directional DC-DC converters: inverting, non-inverting, and interleaved. In the inverting category, converters produce an output voltage with reversed polarity compared to the input voltage, achieved through specific circuit configurations and control strategies. This is valuable in applications such as motor control and grid-tied solar systems. The non-inverting category consists of converters maintaining the same voltage polarity, useful in scenarios like battery equalization. Lastly, the interleaved category employs parallel converter stages to enhance power delivery and reduce current ripple. This classification framework enhances comprehension and analysis of non-isolated bi-directional DC-DC converters. The findings contribute to a deeper understanding of the trade-offs and merits associated with different converter types. As a result, this work aids researchers, practitioners, and engineers in selecting appropriate bi-directional converter solutions for specific energy conversion requirements. The proposed classification framework and experimental assessment collectively enhance the comprehension of non-isolated bi-directional DC-DC converters, fostering advancements in efficient power management and utilization.The simulation process involves the utilization of PSIM to model and simulate non-isolated bi-directional converter from both inverted and non-inverted category. The aim is to conduct a comprehensive comparative analysis of these converters, considering key performance indicators such as rise time, efficiency, ripple factor, and maximum error. This systematic evaluation provides valuable insights into the dynamic response, energy efficiency, output stability, and overall precision of the converters. The results of this comparison facilitate informed decision-making and potential optimizations, ensuring that the chosen converter configuration aligns effectively with the designated operational criteria and performance goals.

Keywords: bi-directional, DC-DC converter, non-isolated, energy conversion

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Authors: T. Ganesan, M. S. Aris, I. Elamvazuthi, Momen Kamal Tageldeen

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Terms set in power purchase agreements (PPA) challenge power utility companies in balancing between the returns (from maximizing power production) and securing long term supply contracts at capped production. The production limitation set in the PPA has driven efforts to maximize profits through efficient and economic power production. In this paper, a combined industrial-scale gas turbine (GT) - absorption chiller (AC) system is considered to cool the GT air intake for reducing the plant’s heat rate (HR). This GT-AC system is optimized while considering power output limitations imposed by the PPA. In addition, the proposed formulation accounts for uncertainties in the ambient temperature using Type-2 fuzzy programming. Using the enhanced chaotic differential evolution (CEDE), the Pareto frontier was constructed and the optimization results are analyzed in detail.

Keywords: absorption chillers (AC), turbine inlet air cooling (TIC), power purchase agreement (PPA), multiobjective optimization, type-2 fuzzy programming, chaotic differential evolution (CDDE)

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Authors: Jin Liang, Guanghua Shi

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In this paper, we are concerned with the further refinements of the Heinz operator inequalities in Hilbert spaces. Our purpose is to derive several new Heinz-type operator inequalities. First, with the help of the Taylor series of some hyperbolic functions, we obtain some refinements of the ordering relations among Heinz means defined by Bhatia with different parameters, which would be more suitable in obtaining the corresponding operator inequalities. Second, we present some generalizations of Heinz operator inequalities. Finally, we give a matrix version of the Heinz inequality for the Hilbert-Schmidt norm.

Keywords: Hilbert space, means inequality, norm inequality, positive linear operator

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Authors: Taimur Khan, Zakirullah, Muhammad Shahab

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The SARS-CoV-2 variant BA.2.86 (Omicron) has emerged with unique mutations that may increase its transmission and infectivity. This study investigates how these mutations alter the Omicron receptor-binding domain's interaction network and dynamic properties (RBD) compared to the wild-type virus, focusing on its binding affinity to the human ACE2 (hACE2) receptor. Protein-protein docking and all-atom molecular dynamics simulations were used to analyze structural and dynamic differences. Despite the structural similarity to the wild-type virus, the Omicron variant exhibits a distinct interaction network involving new residues that enhance its binding capacity. The dynamic analysis reveals increased flexibility in the RBD, particularly in loop regions crucial for hACE2 interaction. Mutations significantly alter the secondary structure, leading to greater flexibility and conformational adaptability compared to the wild type. Binding free energy calculations confirm that the Omicron RBD has a higher binding affinity (-70.47 kcal/mol) to hACE2 than the wild-type RBD (-61.38 kcal/mol). These results suggest that the altered interaction network and enhanced dynamics of the Omicron variant contribute to its increased infectivity, providing insights for the development of targeted therapeutics and vaccines.

Keywords: SARS-CoV-2, molecular dynamic simulation, receptor binding domain, vaccine

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Authors: Aya Salama

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

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

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Authors: Adrian-Gabriel Chifu, Sebastien Fournier

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One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document, paragraph, sentence, or aspect. Aspect-based sentiment analysis is a well-studied topic with many available data sets and models. However, there is no clear definition of what makes a sentence difficult for aspect-based sentiment analysis. In this paper, we explore this question by conducting an experiment with three data sets: ”Laptops”, ”Restaurants”, and ”MTSC” (Multi-Target-dependent Sentiment Classification), and a merged version of these three datasets. We study the impact of domain diversity and syntactic diversity on difficulty. We use a combination of classifiers to identify the most difficult sentences and analyze their characteristics. We employ two ways of defining sentence difficulty. The first one is binary and labels a sentence as difficult if the classifiers fail to correctly predict the sentiment polarity. The second one is a six-level scale based on how many of the top five best-performing classifiers can correctly predict the sentiment polarity. We also define 9 linguistic features that, combined, aim at estimating the difficulty at sentence level.

Keywords: sentiment analysis, difficulty, classification, machine learning

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Authors: Asiri Wijesinghe, N. D. Kodikara, Damitha Sandaruwan

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The Diabetic Retinopathy (DR) is a rapidly growing interrogation around the world which can be annotated by abortive metabolism of glucose that causes long-term infection in human retina. This is one of the preliminary reason of visual impairment and blindness of adults. Information on retinal pathological mutation can be recognized using ocular fundus images. In this research, we are mainly focused on resurrecting an automated diagnosis system to detect DR anomalies such as severity level classification of DR patient (Non-proliferative Diabetic Retinopathy approach) and vessel tortuosity measurement of untwisted vessels to assessment of vessel anomalies (Proliferative Diabetic Retinopathy approach). Severity classification method is obtained better results according to the precision, recall, F-measure and accuracy (exceeds 94%) in all formats of cross validation. In ROC (Receiver Operating Characteristic) curves also visualized the higher AUC (Area Under Curve) percentage (exceeds 95%). User level evaluation of severity capturing is obtained higher accuracy (85%) result and fairly better values for each evaluation measurements. Untwisted vessel detection for tortuosity measurement also carried out the good results with respect to the sensitivity (85%), specificity (89%) and accuracy (87%).

Keywords: fundus image, exudates, microaneurisms, hemorrhages, tortuosity, diabetic retinopathy, optic disc, fovea

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Authors: Xiaoyan Jiang, Huizhu Wang, Lili Gui, Dandan Shen, Xiao Wei, Xi Yu, Hailiang Liu, Min Hong

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Objective: Applicate FITC to establish Th2-type allergic contact dermatitis model, and study the effect and mechanism of Cimifugin on Th2-type allergic contact dermatitis. Methods: The Balb/c mice were sensitized with painting 80 ul of 1.5% FITC onto the shaved abdomen skin at DAY1 and DAY2. The animals were challenged on their right ears with 20 ul of 0.6% FITC, and the left ears were painted with solvent alone at day 6, mice were administered cimifugin for 7 days. 24h later, ear swelling was noted, and the infiltration of eosinophils was investigated by hematoxylin and eosin (H&E) staining. while part of the ear tissue homogenates prepared for detecting interleukin-4 levels by ELISA .Mice were administered cimifugin In the initial stage of the above model for 5 days(-1DAY—DAY3), ear tissue were homogenized to detect IL-33 levels by ELISA. Results: Cimifugin 25mg/kg, 50mg/kg inhibited mouse ear swelling, ear histopathology showed that mice given Cimifugin has significantly reduced levels of local tissue fluid exudation, congestion, infiltration of lymphocytes, and other inflammatory conditions compared with the model group. At the same time, it has significantly reduce of Th2 cytokines IL-4 in the mouse ear tissue homogenate. Data of the initial stage shows that 12.5mg/kg, 50mg/kg Cimifugin significantly inhibited IL-33 levels. Conclusion: Cimifugin inhibit FITC-induced Th2-type allergic contact dermatitis, and its mechanism may be related to inhibition of IL-33.

Keywords: cimifugin, allergic contact dermatitis, Th1/Th2, IL-33

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Osa D Egonwa

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In the 21^st century, the disciplinary boundaries of past centuries that we often create through mainstream art historical classification, techniques and sources may have been eroded by visual culture, which seems to provide a more inclusive umbrella for the new ways artists go about the creative process and its resultant commodities. Over the past four decades, artists in Africa have resorted to new materials, techniques and themes which have affected our ways of research on these artists and their art. Frontline artists such as El Anatsui, Yinka Shonibare, Erasmus Onyishi are demonstrating that any material is just suitable for artistic expression. Most of times, these materials come with their own techniques/effects and visual syntax: a combination of materials compounds techniques, formal aesthetic indexes, halo effects, and iconography. This tends to challenge the categories and we lean on to view, think and talk about them. This renders our main stream art historical research methods inadequate, thus suggesting new discursive concepts, terms and theories. This paper proposed the Africanist eclectic methods derived from the dual framework of Masquerade Theory and What Comes Behind Six is More Than Seven. This paper shares thoughts/research on art historical methods, terminological re-alignments on classification/source data, presentational format and interpretation arising from the emergent trends in our subject. The outcome provides useful tools to mediate new thoughts and experiences in recent African art and visual culture.

Keywords: art historical methods, classifications, concepts, re-alignment

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Authors: Jai Prakash Verma, Khushboo Verma

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In the present paper, a pair of Mond-Weir type non-differentiable multiobjective second-order programming problems, involving two kernel functions, where each of the objective functions contains support function, is formulated. We prove weak, strong and converse duality theorem for the second-order symmetric dual programs under η-pseudoinvexity conditions.

Keywords: non-differentiable multiobjective programming, second-order symmetric duality, efficiency, support function, eta-pseudoinvexity

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Authors: Hala O. El-Mesallamy, Salwa M. Suwailem, Mae M. Seleem

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Visfatin and apelin are two new adipokines that recently gained a special interest in diabetes research. This study was conducted to study the interplay between these two adipokines and their correlation with other inflammatory and biochemical parameters in type 2 diabetic (T2D) postmenopausal women with CAD. Visfatin and apelin were measured by enzyme-linked immunoassay (ELISA). Visfatin was found to be significantly higher in the following groups: T2D patients without CAD, non-obese and obese T2D patients with CAD when compared to control group. Apelin was found to be significantly lower in non-obese and obese T2D patients with CAD when compared to control group. Visfatin and apelin were found to be significantly associated with each other and with other biochemical parameters. The current study provides evidence for the interplay between visfatin and apelin through the inflammatory milieu characteristic of T2D and their possible role in the pathogenesis of CAD complication of T2D.

Keywords: apelin, coronary artery disease, inflammation, type 2 diabetes, visfatin

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Aleksander V. Zakharov, Marat R. Bogdanov, Ramil F. Malikov, Irina N. Dumchikova

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Classification of persecution movement laws is proposed. Modes of persecution in number of specific cases were researched. Modes of movement control using GLONASS/GPS are discussed.

Keywords: UAV Management, mathematical algorithms of targeting and persecution, GLONASS, GPS

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Authors: Taylor Kolody, Farkhund Iqbal, Rabia Batool, Benjamin Fung, Mohammed Hussaeni, Saiqa Aleem

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The rising threat of climate change has led to an increase in public awareness and care about our collective and individual environmental impact. A key component of this impact is our use of cars and other polluting forms of transportation, but it is often difficult for an individual to know how severe this impact is. While there are applications that offer this feedback, they require manual entry of what transportation mode was used for a given trip, which can be burdensome. In order to alleviate this shortcoming, a data from the 2016 TRIPlab datasets has been used to train a variety of machine learning models to automatically recognize the mode of transportation. The accuracy of 89.6% is achieved using single deep neural network model with Gated Recurrent Unit (GRU) architecture applied directly to trip data points over 4 primary classes, namely walking, public transit, car, and bike. These results are comparable in accuracy to results achieved by others using ensemble methods and require far less computation when classifying new trips. The lack of trip context data, e.g., bus routes, bike paths, etc., and the need for only a single set of weights make this an appropriate methodology for applications hoping to reach a broad demographic and have responsive feedback.

Keywords: classification, gated recurrent unit, recurrent neural network, transportation

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Authors: Olena Kavats, Dmitry Khramov, Kateryna Sergieieva, Vladimir Vasyliev, Iurii Kavats

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Geoinformation technologies of space agromonitoring are a means of operative decision making support in the tasks of managing the agricultural sector of the economy. Existing technologies use satellite images in the optical range of electromagnetic spectrum. Time series of optical images often contain gaps due to the presence of clouds and haze. A geoinformation technology is created. It allows to fill gaps in time series of optical images (Sentinel-2, Landsat-8, PROBA-V, MODIS) with radar survey data (Sentinel-1) and use information about agrometeorological conditions of the growing season for individual monitoring years. The technology allows to perform crop classification and mapping for spring-summer (winter and spring crops) and autumn-winter (winter crops) periods of vegetation, monitoring the dynamics of crop state seasonal changes, crop yield forecasting. Crop classification is based on supervised classification algorithms, takes into account the peculiarities of crop growth at different vegetation stages (dates of sowing, emergence, active vegetation, and harvesting) and agriculture land state characteristics (row spacing, seedling density, etc.). A catalog of samples of the main agricultural crops (Ukraine) is created and crop spectral signatures are calculated with the preliminary removal of row spacing, cloud cover, and cloud shadows in order to construct time series of crop growth characteristics. The obtained data is used in grain crop growth tracking and in timely detection of growth trends deviations from reference samples of a given crop for a selected date. Statistical models of crop yield forecast are created in the forms of linear and nonlinear interconnections between crop yield indicators and crop state characteristics (temperature, precipitation, vegetation indices, etc.). Predicted values of grain crop yield are evaluated with an accuracy up to 95%. The developed technology was used for agricultural areas monitoring in a number of Great Britain and Ukraine regions using EOS Crop Monitoring Platform (https://crop-monitoring.eos.com). The obtained results allow to conclude that joint use of Sentinel-1 and Sentinel-2 images improve separation of winter crops (rapeseed, wheat, barley) in the early stages of vegetation (October-December). It allows to separate successfully the soybean, corn, and sunflower sowing areas that are quite similar in their spectral characteristics.

Keywords: geoinformation technology, crop classification, crop yield prediction, agricultural monitoring, EOS Crop Monitoring Platform

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Authors: S. alireza Mirghasemi, Shervin Rashidinia, Mohammadsaleh Sadeghi, Mohsen Talebizadeh, Narges Rahimi Gabaran, S. Shahin Eftekhari, Sara Shahmoradi

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Purpose: There are various pin-in-plaster methods for treating distal radius fractures. This study is meant to introduce a modified technique of pin-in-plaster. Materials and methods: Fifty-four patients with distal radius fractures were followed up for one year. Patients were excluded if they had type B fractures according to AO classification, multiple injuries or pathological fractures, and were treated more than 7 days after injury. Range of motion and functional results were evaluated. Radiographic parameters including radial inclination, tilt, and height, were measured preoperatively and postoperatively. Results: The average radial tilt was 10.6° and radial height was 10.2 mm at the sixth month postoperatively. Three cases of pin tract infection were recorded, who were treated totally with oral antibiotics. There was no case of pin loosening. Of total 73 patients underwent surgery, three cases of radial nerve irritation were recorded at the time of cast removal. All of them resolved at the 6th month follow up. No median nerve compression and carpal tunnel syndrome have found. We also had no case of tendon injury. Conclusion: Our modified technique is effective to restore anatomic congruity and maintain reduction.

Keywords: distal radius fracture, percutaneous pinning, pin-in-plaster, modified method of pin-in-plaster, operative treatment

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Authors: Sandesh Achar

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Personalized advertising holds greater potential for higher conversion rates compared to generic advertisements. However, its widespread application in the retail industry faces challenges due to complex implementation processes. These complexities impede the swift adoption of personalized advertisement on a large scale. Personalized advertisement, being a data-driven approach, necessitates consumer-related data, adding to its complexity. This paper introduces an innovative data-driven decision-making framework, D3Advert, which personalizes advertisements by analyzing personalities using a BiLSTM network. The framework utilizes the Myers–Briggs Type Indicator (MBTI) dataset for development. The employed BiLSTM network, specifically designed and optimized for D3Advert, classifies user personalities into one of the sixteen MBTI categories based on their social media posts. The classification accuracy is 86.42%, with precision, recall, and F1-Score values of 85.11%, 84.14%, and 83.89%, respectively. The D3Advert framework personalizes advertisements based on these personality classifications. Experimental implementation and performance analysis of D3Advert demonstrate a 40% improvement in impressions. D3Advert’s innovative and straightforward approach has the potential to transform personalized advertising and foster widespread personalized advertisement adoption in marketing.

Keywords: personalized advertisement, deep Learning, MBTI dataset, BiLSTM network, NLP.

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Authors: Thitaporn Thoopputra, Tipaporn Pongmesa, Shuchuen Li

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Aims: To evaluate the application of non-invasive diabetes risk assessment tool in community pharmacy setting. Methods: Thai diabetes risk score was applied to assess individuals at risk of developing type 2 diabetes. Interactive computer-based risk screening (IT) and paper-based risk screening (PT) tools were applied. Participants aged over 25 years with no known diabetes were recruited in six participating pharmacies. Results: A total of 187 clients, mean aged (+SD) was 48.6 (+10.9) years. 35% were at high risk. The mean value of willingness-to-pay for the service fee in IT group was significantly higher than PT group (p=0.013). No significant difference observed for the satisfaction between groups. Conclusions: Non-invasive risk assessment tool, whether paper-based or computerized-based can be applied in community pharmacy to support the enhancing role of pharmacists in chronic disease management. Long term follow up is needed to determine the impact of its application in clinical, humanistic and economic outcomes.

Keywords: community pharmacy, intervention, prevention, risk assessment, type 2 diabetes

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Authors: Gholam Hossein Javanmard, Roghayeh Mohammadi Garegozlo

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The present study was a randomized three-group double-blind clinical trial with repeated measures designs which aimed to determine the pure effect and combined effect of mindfulness based-relaxation (MBR) technique and Transcranial Electrical Simulation (tCES) on psychological distress decreasing of patients with type-2 diabetes. The sample of the study consisted of 30 patients with type-2 diabetes who were selected from the Diabetes Association of Bonab city in Iran. The participants were matched and then randomly assigned to the three groups of 10 subjects (MBR, CES, MBR+CES). The subjects received interventions related to their group in 10 individual sessions. Pre-test, post-test, and one-month follow-up were conducted using DASS-42. Analysis of variance with repeated measures showed a significant change in psychological distress. Multivariate covariance analysis and the paired interpersonal comparative test of Ben Foruni indicated that both interventions of MBR and CES have a similar effect on psychological distress decreasing in the post-test and follow-up phase. But, the combined therapy of MBR+CES was more efficient, and it had a more stable effect. However, all three interventions, especially combined intervention of MBR+CES, as efficient and stable treatment, are suggested for improving the psychological status of diabetic patients.

Keywords: mindfulness based-relaxation, transcranial electrical simulation, type 2 diabetes, psychological distress

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Authors: WooYoung Jung, V. Sim

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This paper presents numerical analysis in terms of buckling resistance strength of polymer matrix composite (PMC) infill panels system under the influence of temperature on the foam core. Failure mode under in-plane compression is investigated by means of numerical analysis with ABAQUS platform. Parameters considered in this study are contact length and both the type of foam for core and the variation of its Young's Modulus under the thermal influence. Variation of temperature is considered in static cases and only applied to core. Indeed, it is shown that the effect of temperature on the panel system mechanical properties is significance. Moreover, the variations of temperature result in the decrements of the system strength. This is due to the polymeric nature of this material. Additionally, the contact length also displays the effect on performance of infill panel. Their significance factors are based on type of polymer for core. Hence, by comparing difference type of core material, the variation can be reducing.

Keywords: buckling, contact length, foam core, temperature dependent

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Authors: M. Abbasi Layegh, S. Haghipour, K. Athari, R. Khosravi, M. Tafkikialamdari

Abstract:

An approach to find the optimum mel-frequency cepstral coefficients (MFCCs) for the Radif of Mirzâ Ábdollâh, which is the principal emblem and the heart of Persian music, performed by most famous Iranian masters on two Iranian stringed instruments ‘Tar’ and ‘Setar’ is proposed. While investigating the variance of MFCC for each record in themusic database of 1500 gushe of the repertoire belonging to 12 modal systems (dastgâh and âvâz), we have applied the Fuzzy C-Mean clustering algorithm on each of the 12 coefficient and different combinations of those coefficients. We have applied the same experiment while increasing the number of coefficients but the clustering accuracy remained the same. Therefore, we can conclude that the first 7 MFCCs (V-7MFCC) are enough for classification of The Radif of Mirzâ Ábdollâh. Classical machine learning algorithms such as MLP neural networks, K-Nearest Neighbors (KNN), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and Support Vector Machine (SVM) have been employed. Finally, it can be realized that SVM shows a better performance in this study.

Keywords: radif of Mirzâ Ábdollâh, Gushe, mel frequency cepstral coefficients, fuzzy c-mean clustering algorithm, k-nearest neighbors (KNN), gaussian mixture model (GMM), hidden markov model (HMM), support vector machine (SVM)

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Authors: Ouahiba Bechiri, Mostefa Abbessi

Abstract:

The color removal from industrial effluents is a major concern in wastewater treatment. The main objective of this work was to study the decolorization of a mixture of Orange G acid (OG) and naphthol blue black dye (NBB) in aqueous solution by hydrogen peroxide using [H1,5Fe1,5P2W12Mo6O61,23H2O] as catalyst. [H1,5Fe1,5P2 W12Mo6O61,23H2O] is a recyclable DAWSON type heteropolyanion. Effects of various experimental parameters of the oxidation reaction of the dye were investigated. The studied parameters were: the initial pH, H2O2 concentration, the catalyst mass and the temperature. The optimum conditions had been determined, and it was found that efficiency of degradation obtained after 15 minutes of reaction was about 100%. The optimal parameters were: initial pH = 3; [H2O2]0 = 0.08 mM; catalyst mass = 0.05g; for a concentration of dyes = 30mg/L.

Keywords: Dawson type heteropolyanion, naphthol blue-black, dye degradation, orange G acid, oxidation, hydrogen peroxide

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Authors: Htay Htay Yi, Zin May Aye

Abstract:

Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: false negative rate, intrusion detection system, machine learning methods, performance

Procedia PDF Downloads 118

Authors: Nitin Lahkar, Rishiraj Goswami

Abstract:

“Oil Well Logging” or the practice of making a detailed record (a well log) of the geologic formations penetrated by a borehole is an important practice in the Oil and Gas industry. Although a lot of research has been undertaken in this field, some basic limitations still exist. One of the main arenas or venues where plethora of problems arises is in logistically challenged areas. Accessibility and availability of efficient manpower, resources and technology is very time consuming, restricted and often costly in these areas. So, in this regard, the main challenge is to decrease the Non Productive Time (NPT) involved in the conventional logging process. The thought for the solution to this problem has given rise to a revolutionary concept called the “Robotic Logging Technology”. Robotic logging technology promises the advent of successful logging in all kinds of wells and trajectories. It consists of a wireless logging tool controlled from the surface. This eliminates the need for the logging truck to be summoned which in turn saves precious rig time. The robotic logging tool here, is designed such that it can move inside the well by different proposed mechanisms and models listed in the full paper as TYPE A, TYPE B and TYPE C. These types are classified on the basis of their operational technology, movement and conditions/wells in which the tool is to be used. Thus, depending on subsurface conditions, energy sources available and convenience the TYPE of Robotic model will be selected. Advantages over Conventional Logging Techniques: Reduction in Non-Productive time, lesser energy requirements, very fast action as compared to all other forms of logging, can perform well in all kinds of well trajectories (vertical/horizontal/inclined).

Keywords: robotic logging technology, innovation, geology, geophysics

Procedia PDF Downloads 306