Search results for: feature extraction method for tremor classification

Authors: Ahmed Machmoum, Malika El Kyal

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We consider iterative parallel multi-splitting method for differential algebraic equations. The main feature of the proposed idea is to use the asynchronous form. We prove that the multi-splitting technique can effectively accelerate the convergent performance of the iterative process. The main characteristic of an asynchronous mode is that the local algorithm not have to wait at predetermined messages to become available. We allow some processors to communicate more frequently than others, and we allow the communication delays tobe substantial and unpredictable. Note that synchronous algorithms in the computer science sense are particular cases of our formulation of asynchronous one.

Keywords: computer, multi-splitting methods, asynchronous mode, differential algebraic systems

Procedia PDF Downloads 549

Authors: K. M. K. D Weerasekara, R. M. K. M Rathnayake, R. U. Halwatura, G. Y. Jayasinghe

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Wood apple (Limonia acidissima L.) trees are native to Sri Lanka and India. Wood apple gum is widely used in the food, coating, and pharmaceutical industries. Wood apple gum was a major component in ancient Sri Lankan coating technology as well. It is also used as a suspending agent in liquid syrups and food ingredients such as sauces, emulsifiers, and stabilizers. Industrial applications include adhesives for labeling and packaging, as well as paint binder. It is also used in the production of paper and cosmetics. Extraction of wood apple gum is an important step in ensuring maximum benefits for various uses. It is apparent that an abundance of untapped potential lies in wood apple gum if people are able to mass produce them. Hence, the current study uses a two-factor factorial design with two major variables and four replications to investigate the best gum-extracting tapping system for Wood apple gum. This study's findings will be useful to Wood apple cultivators, researchers, and gum-based industries alike.

Keywords: wood apple gum, limonia acidissima l., tapping, tapping cuts

Procedia PDF Downloads 75

Authors: Gheida J. Shahrour, Martin J. Russell

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The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: person recognition, topic recognition, culture recognition, 3D body movement signals, variability compensation

Procedia PDF Downloads 541

Authors: Mona Hejazi, Ali Motie Nasrabadi

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Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Keywords: effective connectivity, Granger causality, directed transfer function, epilepsy seizure prediction, EEG

Procedia PDF Downloads 469

Authors: Muzaffar Iqbal, Essam Ezzeldin, Khalid A. Al-Rashood

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Pomalidomide is a second generation oral immunomodulatory agent, being used for the treatment of multiple myeloma in patients with disease refractory to lenalidomide and bortezomib. In this study, a sensitive UPLC-MS/MS assay was developed and validated for high-throughput determination of pomalidomide in rat plasma using celecoxib as an internal standard (IS). Liquid liquid extraction using dichloromethane as extracting agent was employed to extract pomalidomide and IS from 200 µL of plasma. Chromatographic separation was carried on Acquity BEHTM C18 column (50 × 2.1 mm, 1.7 µm) using an isocratic mobile phase of acetonitrile:10 mM ammonium acetate (80:20, v/v), at a flow rate of 0.250 mL/min. Both pomalidomide and IS were eluted at 0.66 ± 0.03 and 0.80 ± 0.03 min, respectively with a total run time of 1.5 min only. Detection was performed on a triple quadrupole tandem mass spectrometer using electrospray ionization in negative mode. The precursor to product ion transitions of m/z 272.01 → 160.89 for pomalidomide and m/z 380.08 → 316.01 for IS were used to quantify them respectively, using multiple reaction monitoring mode. The developed method was validated according to regulatory guideline for bioanalytical method validation. The linearity in plasma sample was achieved in the concentration range of 0.47–400 ng/mL (r2 ≥ 0.997). The intra and inter-day precision values were ≤ 11.1% (RSD, %) whereas accuracy values ranged from - 6.8 – 8.5% (RE, %). In addition, other validation results were within the acceptance criteria and the method was successfully applied in a pharmacokinetic study of pomalidomide in rats.

Keywords: pomalidomide, pharmacokinetics, LC-MS/MS, celecoxib

Procedia PDF Downloads 391

Authors: Naghmeh Moradpoor Sheykhkanloo

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Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into a given SQL database by simply using a web browser. Losing data, disclosing confidential information or even changing the value of data are the severe damages that SQLI attack can cause on a given database. SQLI attack has also been rated as the number-one attack among top ten web application threats on Open Web Application Security Project (OWASP). OWASP is an open community dedicated to enabling organisations to consider, develop, obtain, function, and preserve applications that can be trusted. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLI attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLI attack categories, and an NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLI attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: neural networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection

Procedia PDF Downloads 469

Authors: Kam Wen Hang, Tan Kee Teng, Huang Poh Ching, Chia Kai Xiang, H. V. Annegowda, H. S. Naveen Kumar

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Gynura procumbens (G. procumbens) leaves, commonly known as ‘sambung nyawa’ in Malaysia is a well-known medicinal plant commonly used as folk medicines in controlling blood glucose, cholesterol level as well as treating cancer. These medicinal properties were believed to be related to the polyphenolic content present in G. procumbens extract, therefore optimization of its extraction process is vital to obtain highest possible antioxidant activities. The current study was conducted to investigate the effect of different concentrations of extracting solvent (ethanol) on the amount of polyphenolic content and antioxidant activities of G. procumbens leaf extract. The concentrations of ethanol used were 30-70%, with the temperature and time kept constant at 50°C and 30 minutes, respectively using ultrasound-assisted extraction. The polyphenolic content of these extracts were quantified by Folin-Ciocalteu colorimetric method and results were expressed as milligram gallic acid equivalent (mg GAE)/g. Phosphomolybdenum method and 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging assays were used to investigate the antioxidant properties of the extract and the results were expressed as milligram ascorbic acid equivalent (mg AAE)/g and effective concentration (EC50) respectively. Among the three different (30%, 50% and 70%) concentrations of ethanol studied, the 50% ethanolic extract showed total phenolic content of 31.565 ± 0.344 mg GAE/g and total antioxidant activity of 78.839 ± 0.199 mg AAE/g while 30% ethanolic extract showed 29.214 ± 0.645 mg GAE/g and 70.701 ± 1.394 mg AAE/g, respectively. With respect to DPPH radical scavenging assay, 50% ethanolic extract had exhibited slightly lower EC50 (314.3 ± 4.0 μg/ml) values compared to 30% ethanol extract (340.4 ± 5.3 μg/ml). Out of all the tested extracts, 70% ethanolic extract exhibited significantly (p< 0.05) highest total phenolic content (38.000 ± 1.009 mg GAE/g), total antioxidant capacity (95.874 ± 2.422 mg AAE/g) and demonstrated the lowest EC50 in DPPH assay (244.2 ± 5.9 μg/ml). An excellent correlations were drawn between total phenolic content, total antioxidant capacity and DPPH radical scavenging activity (R2 = 0.949 and R2 = 0.978, respectively). It was concluded from this study that, 70% ethanol should be used as the optimal polarity solvent to obtain G. procumbens leaf extract with maximum polyphenolic content with antioxidant properties.

Keywords: antioxidant activity, DPPH assay, Gynura procumbens, phenolic compounds

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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

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

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Authors: Helena Macedo, Larissa Tomaz, Levi Guimarães, Luís Cerqueira-Pinto, José Dinis-Carvalho

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In the context of retail logistics, the pursuit of operational efficiency and cost optimization involves a rigorous distinction between value-added and non-value-added activities. In today's competitive market, optimizing efficiency and reducing operational costs are paramount for retail businesses. This research paper focuses on the development of a classification model adapted to the retail sector, specifically examining internal logistics processes. Based on a comprehensive analysis conducted in a retail supermarket located in the north of Portugal, which covered various aspects of internal retail logistics, this study questions the concept of value and the definition of wastes traditionally applied in a manufacturing context and proposes a new way to assess activities in the context of internal logistics. This study combines quantitative data analysis with qualitative evaluations. The proposed classification model offers a systematic approach to categorize operations within the retail logistics chain, providing actionable insights for decision-makers to streamline processes, enhance productivity, and allocate resources more effectively. This model contributes not only to academic discourse but also serves as a practical tool for retail businesses, aiding in the enhancement of their internal logistics dynamics.

Keywords: lean retail, lean logisitcs, retail logistics, value-added and non-value-added

Procedia PDF Downloads 66

Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

Procedia PDF Downloads 107

Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

Procedia PDF Downloads 89

Authors: Xudong He, Jian Wang, Jiqiang Liu, Lei Han, Yang Yu, Shaohua Lv

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Nowadays, the threats of the internet are enormous and increasing; however, the classification of huge alert messages generated in this environment is relatively monotonous. It affects the accuracy of the network situation assessment, and also brings inconvenience to the security managers to deal with the emergency. In order to deal with potential network threats effectively and provide more effective data to improve the network situation awareness. It is essential to build a hierarchical filtering method to prevent the threats. In this paper, it establishes a model for data monitoring, which can filter systematically from the original data to get the grade of threats and be stored for using again. Firstly, it filters the vulnerable resources, open ports of host devices and services. Then use the entropy theory to calculate the performance changes of the host devices at the time of the threat occurring and filter again. At last, sort the changes of the performance value at the time of threat occurring. Use the alerts and performance data collected in the real network environment to evaluate and analyze. The comparative experimental analysis shows that the threat filtering method can effectively filter the threat alerts effectively.

Keywords: correlation analysis, hierarchical filtering, multisource data, network security

Procedia PDF Downloads 201

Authors: Krishna Prasad Regmi, Jun-Bo Tu, Cheng-Qun Hou, Li-Feng Li

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Maxillofacial trauma in a pediatric group of patients is particularly challenging, as these patients have significant differences from adults as far as the facial skeleton is concerned. Mandibular condylar fractures are common presentations to hospitals across the globe and remain the most important cause of temporomandibular joint (TMJ) ankylosis. The etiology and epidemiology of pediatric trauma involving the diacapitular condylar fractures (DFs) have been reported in a large series of patients. Nevertheless, little is known about treatment protocols for DFs in children. Accordingly, the treatment modalities for the management of pediatric fractures also differ. We suggest following the PDA and intracapsular ABC classification of condylar fracture to increase the overall postoperative satisfaction level that bypasses the change of subjective feelings of patients’ from preoperative to the postoperative condition. At the same time, use of 3-D technology and surgical navigation may also increase treatment accuracy.

Keywords: maxillofacial trauma, diacapitular fracture, condylar fracture, PDA classification

Procedia PDF Downloads 271

Authors: K. Sanjayani, C. Saraswathy, S. Sreenivasan, S. Sudhahar, D. Suganya, K. S. Neelukumari, N. Vijayarangan

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Double Base Number System (DBNS) is an imminent system of representing a number using two bases namely 2 and 3, which has its application in Elliptic Curve Cryptography (ECC) and Digital Signature Algorithm (DSA).The previous binary method representation included only base 2. DBNS uses an approximation algorithm namely, Greedy Algorithm. By using this algorithm, the number of digits required to represent a larger number is less when compared to the standard binary method that uses base 2 algorithms. Hence, the computational speed is increased and time being reduced. The standard binary method uses binary digits 0 and 1 to represent a number whereas the DBNS method uses binary digit 1 alone to represent any number (canonical form). The greedy algorithm uses two ways to represent the number, one is by using only the positive summands and the other is by using both positive and negative summands. In this paper, arithmetic operations are used for elliptic curve cryptography. Elliptic curve discrete logarithm problem is the foundation for most of the day to day elliptic curve cryptography. This appears to be a momentous hard slog compared to digital logarithm problem. In elliptic curve digital signature algorithm, the key generation requires 160 bit of data by usage of standard binary representation. Whereas, the number of bits required generating the key can be reduced with the help of double base number representation. In this paper, a new technique is proposed to generate key during encryption and extraction of key in decryption.

Keywords: cryptography, double base number system, elliptic curve cryptography, elliptic curve digital signature algorithm

Procedia PDF Downloads 396

Authors: H. Nacef, L. Larous

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Salin soils occupy about 7% of land area; they are characterized by unsuitable physical conditions for the growth of living organisms. However, researches showed that some microorganisms especially fungi are able to grow and adapt to such extreme conditions; it is due to their ability to develop different physiological mechanisms in their adaptation. The aim of this study is to identify qualitatively the osmolytes that the biotechnological important fungus A. oryzae accumulated and/or produced in its adaptation, which they were detected by Thin-layer chromatography technique (TLC) using several systems, from different media (Wheat brane, MNM medium and MM medium). The results showed that The moderately halotolerant fungus A. oryzae, accumulates mixture of molecules, containing polyols and sugars , some amino acids in addition to some molecules which were not defined. Wheat bran was the best medium for the extraction of these molecules, where the proportion was 85.71%, followed by MNM medium 64.28%, then the minimum medium MM 14.28%. Properties of osmolytes are becoming increasingly useful in molecular biology, agriculture pharmaceutical, medicinal, and biotechnological interests.

Keywords: salinity, aspergillus oryzae, halo tolerance, osmolytes, compatible solutes

Procedia PDF Downloads 415

Authors: Bei Shan Lin, Tzu Yu Huang, Ya Ping Chen, Chun Mel Lu

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This research is divided into two parts. The first part is to adopt the method of supercritical carbon dioxide fluid extraction to extract sage essential oil (Salvia officinalis) and to find out the differences when the procedure is under different pressure conditions. Meanwhile, this research is going to probe into the composition of the extracted sage essential oil. The second part will talk about the effect of the aromatherapy with extracted sage essential oil to improve the sleeping quality for women in menopause. The extracted sage substance is tested by inhibiting DPPH radical to identify its antioxidant capacity, and the extracted component was analyzed by gas chromatography-mass spectrometer. Under two different pressure conditions, the extracted experiment gets different results. By 3000 psi, the extracted substance is IC50 180.94mg/L, which is higher than IC50 657.43mg/L by 1800 psi. By 3000 psi, the extracted yield is 1.05%, which is higher than 0.68% by 1800 psi. Through the experimental data, the researcher also can conclude that the extracted substance with 3000psi contains more materials than the one with 1800 psi. The main overlapped materials are the compounds of cyclic ether, flavonoid, and terpenes. Cyclic ether and flavonoids have the function of soothing and calming. They can be applied to relieve cramps and to eliminate menopause disorders. The second part of the research is to apply extracted sage essential oil to aromatherapy for women who are in menopause and to discuss the effect of the improvement for the sleeping quality. This research adopts the approaching of Swedish upper back massage, evaluates the sleeping quality with the Pittsburgh Sleep Quality Index, and detects the changes with heart rate variability apparatus. The experimental group intervenes with extracted sage essential oil to the aromatherapy. The average heart beats detected by the apparatus has a better result in SDNN, low frequency, and high frequency. The performance is better than the control group. According to the statistical analysis of the Pittsburgh Sleep Quality Index, this research has reached the effect of sleep quality improvement. It proves that extracted sage essential oil has a significant effect on increasing the activities of parasympathetic nerves. It is able to improve the sleeping quality for women in menopause

Keywords: supercritical carbon dioxide fluid extraction, Salvia officinalis, aromatherapy, Swedish massage, Pittsburgh sleep quality index, heart rate variability, parasympathetic nerves

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Authors: B.-Y.-S. Lau, Y.-C.-T. Jonathan, M.-S. Alias

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Heritage trees are natural large, individual trees with exceptionally value due to association with age or event or distinguished people. In Malaysia, there is an abundance of tropical heritage trees throughout the country. It is essential to set up a repository of heritage trees to prevent valuable trees from being cut down. In this cross domain study, a web-based online expert system namely the Heritage Tree Expert Assessment and Classification (HTEAC) is developed and deployed for public to nominate potential heritage trees. Based on the nomination, tree care experts or arborists would evaluate and verify the nominated trees as heritage trees. The expert system automatically rates the approved heritage trees according to pre-defined grades via Delphi technique. Features and usability test of the expert system are presented. Preliminary result is promising for the system to be used as a full scale public system.

Keywords: arboriculture, Delphi, expert system, heritage tree, urban forestry

Procedia PDF Downloads 313

Authors: H. Yousefnia, S. Zolghadri, S. Shanesazzadeh, A.Lahooti, A. R. Jalilian

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In this study, 68Ga-ECC and 67Ga-ECC were both prepared with the radiochemical purity of higher than 97% in less than 30 min. The biodistribution data for 68Ga-ECC showed the extraction of the most of the activity from the urinary tract. The absorbed dose was estimated based on biodistribution data in mice by the medical internal radiation dose (MIRD) method. Comparison between human absorbed dose estimation for these two agents indicated the values of approximately ten-fold higher after injection of 67Ga-ECC than 68Ga-ECC in the most organs. The results showed that 68Ga-ECC can be considered as a more potential agent for renal imaging compared to 67Ga-ECC.

Keywords: effective absorbed dose, ethylenecysteamine cysteine, Ga-67, Ga-68

Procedia PDF Downloads 469

Authors: Thanh Van Hoang, Tien Yin Chou, Yao Min Fang, Yi Min Huang, Xuan Linh Nguyen

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Reliable information on the spatial distribution of annual rainfall and temperature is essential in research projects relating to urban and regional planning. This research presents results of a classification of temperature and rainfall in the Red River Delta of northern Vietnam based on measurements from seven meteorological stations (Ha Nam, Hung Yen, Lang, Nam Dinh, Ninh Binh, Phu Lien, Thai Binh) in the river basin over a thirty-years period from 1982-2011. The average accumulated rainfall trends in the delta are analysed and form the basis of research essential to weather and climate forecasting. This study employs interpolation based on the Kriging Method for daily rainfall (min and max) and daily temperature (min and max) in order to improve the understanding of sources of variation and uncertainly in these important meteorological parameters. To the Kriging method, the results will show the different models and the different parameters based on the various precipitation series. The results provide a useful reference to assist decision makers in developing smart agriculture strategies for the Red River Delta in Vietnam.

Keywords: spatial interpolation method, ArcGIS, temperature variability, rainfall variability, Red River Delta, Vietnam

Procedia PDF Downloads 330

Authors: Djallel Bouamama, Yasser R. Haddadi

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Accurate brain tumor segmentation is crucial for diagnosis and treatment planning, yet it remains a challenging task due to the variability in tumor shapes and intensities. This paper introduces a distinct approach to brain tumor image segmentation by leveraging an advanced architecture known as Mamba-Unet. Building on the well-established U-Net framework, Mamba-Unet incorporates distinct design enhancements to improve segmentation performance. Our proposed method integrates a multi-scale attention mechanism and a hybrid loss function to effectively capture fine-grained details and contextual information in brain MRI scans. We demonstrate that Mamba-Unet significantly enhances segmentation accuracy compared to conventional U-Net models by utilizing a comprehensive dataset of annotated brain MRI scans. Quantitative evaluations reveal that Mamba-Unet surpasses traditional U-Net architectures and other contemporary segmentation models regarding Dice coefficient, sensitivity, and specificity. The improvements are attributed to the method's ability to manage class imbalance better and resolve complex tumor boundaries. This work advances the state-of-the-art in brain tumor segmentation and holds promise for improving clinical workflows and patient outcomes through more precise and reliable tumor detection.

Keywords: brain tumor classification, image segmentation, CNN, U-NET

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: data mining, knowledge discovery in databases, prediction models, student success

Procedia PDF Downloads 407

Authors: Bairam Khan, Premlata Vaishnava

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The ability to conceptualize one entity in terms of another allows us to communicate through metaphors. This central feature of human cognition has evolved with the development of language, and the processing of metaphors is without any conscious appraisal and is quite effortless. South Asians, like other speech communities, have been using the kitchenary [culinary] metaphor in a very simple yet interesting way and are known for bringing into new and unique constellations wherever they are. This composite feature of our language is used to communicate in a precise and compact manner and maneuvers the expression. The present study explores the role of kitchenary metaphors in the making and shaping of idioms by applying Cognitive Metaphor Theories. Drawing on examples from a corpus of adverts, print, and electronic media, the study looks at the metaphorical language used by real people in real situations. The overarching theme throughout the course is that kitchenary metaphors are powerful tools of expression in Hindi-Urdu.

Keywords: cognitive metaphor theories, kitchenary metaphors, hindi-urdu print, and electronic media, grammatical structure of kitchenary metaphors of hindi-urdu

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Authors: F. Bellali, M. Kharroubi, M. Loutfi, N.Bourhim

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In Morocco, fish processing industry is an important source income for a large amount of byproducts including skins, bones, heads, guts and scales. Those underutilized resources particularly scales contain a large amount of proteins and calcium. Scales from Sardina plichardus resulting from the transformation operation have the potential to be used as raw material for the collagen production. Taking into account this strong expectation of the regional fish industry, scales sardine upgrading is well justified. In addition, political and societal demands for sustainability and environment-friendly industrial production systems, coupled with the depletion of fish resources, drive this trend forward. Therefore, fish scale used as a potential source to isolate collagen has a wide large of applications in food, cosmetic and bio medical industry. The main aim of this study is to isolate and characterize the acid solubilize collagen from sardine fish scale, Sardina pilchardus. Experimental design methodology was adopted in collagen processing for extracting optimization. The first stage of this work is to investigate the optimization conditions of the sardine scale deproteinization on using response surface methodology (RSM). The second part focus on the demineralization with HCl solution or EDTA. Moreover, the last one is to establish the optimum condition for the isolation of collagen from fish scale by solvent extraction. The basic principle of RSM is to determinate model equations that describe interrelations between the independent variables and the dependent variables.

Keywords: Sardina pilchardus, scales, valorization, collagen extraction, response surface methodology

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Authors: Solomon Tilahun Desisa, Muktar Seid Hussen

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Hibiscus macranthus is one of family Malvaceae and genus Hibiscus plant which grows mainly in western part of Ethiopia. Hibiscus macranthus is the most adaptable and abundant plant in the nation, which are used as an ornamental plant often a hedge or fence plant, and used as a firewood after harvesting the stem together with the bark, and used also as a fiber for trying different kinds of things by forming the rope. However, Hibiscus macranthus plant fibre has not been commercially exploited and extracted properly. This study of work describes the possibility of mechanical and retting methods of Hibiscus macranthus fibre extraction and characterization. Hibiscus macranthus fibre is a bast fibre which obtained naturally from the stem or stalks of the dicotyledonous plant since it is a natural cellulose plant fiber. And the fibre characterized by studying its physical and chemical properties. The physical characteristics were investigated as follows, including the length of 100-190mm, fineness of 1.0-1.2Tex, diameter under X100 microscopic view 16-21 microns, the moisture content of 12.46% and dry tenacity of 48-57cN/Tex along with breaking extension of 0.9-1.6%. Hibiscus macranthus fiber productivity was observed that 12-18% of the stem out of which more than 65% is primary long fibers. The fiber separation methods prove to decrease of non-cellulose ingredients in the order of mechanical, water and chemical methods. The color measurement also shows the raw Hibiscus macranthus fiber has a natural golden color according to YID1925 and paler look under both retting methods than mechanical separation. Finally, it is suggested that Hibiscus macranthus fibre can be used for manufacturing of natural and organic crop and coffee packages as well as super absorbent, fine and high tenacity textile products.

Keywords: Hibiscus macranthus, bast fiber, extraction, characterization

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Authors: Gayathri Sadanala, Shibam Pokhrel, Owen Murphy

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In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments.

Keywords: interaction, machine learning, predictive modeling, virtual reality

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Authors: Payal Abichandani, Rishi Prakash, Paras Nath Barwal, B. K. Murthy

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Information generated from various computerization processes is a potential rich source of knowledge for its designated community. To pass this information from generation to generation without modifying the meaning is a challenging activity. To preserve and archive the data for future generations it’s very essential to prove the authenticity of the data. It can be achieved by extracting the metadata from the data which can prove the authenticity and create trust on the archived data. Subsequent challenge is the technology obsolescence. Metadata extraction and standardization can be effectively used to resolve and tackle this problem. Metadata can be categorized at two levels i.e. Technical and Domain level broadly. Technical metadata will provide the information that can be used to understand and interpret the data record, but only this level of metadata isn’t sufficient to create trustworthiness. We have developed a tool which will extract and standardize the technical as well as domain level metadata. This paper is about the different features of the tool and how we have developed this.

Keywords: digital preservation, metadata, OAIS, PDI, XML

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Authors: Prajwal Prakash Vasisht, Sharath Rajamurthy, Nishanth Dara

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In a supervised machine learning approach, metrics such as precision, accuracy, and coverage can be calculated using ground truth labels to help in model tuning, evaluation, and selection. In an unsupervised setting, however, where the data has no ground truth, there are few interpretable metrics that can guide us to do the same. Our approach creates a framework to adapt the Intersection over Union metric, referred to as Adapted IoU, usually used to evaluate supervised learning models, into the unsupervised domain, which solves the problem by factoring in subject matter expertise and intuition about the ideal output from the model. This metric essentially provides a scale that allows us to compare the performance across numerous unsupervised models or tune hyper-parameters and compare different versions of the same model.

Keywords: general metric, unsupervised learning, classification, intersection over union

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Authors: Lila Boulekbache-Makhlouf, Fatiha Brahmi

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This study was carried out with the aim of enriching fresh cheese with the food by-products, which are the leaves of shallots and the peels of potatoes. Firstly, the conditions for extracting the total polyphenols using ultrasound are optimized. Then, the contents of total polyphenols PPT , flavonoids and antioxidant activity were evaluated for the extracts obtained by adopting the optimal parameter. On the other hand, we have carried out some physicochemical, microbiological and sensory analyzes of the cheese produced. The maximum total polyphenols value of 70.44 mg GAE gallic acid equivalent / g of dry matter DM of shallot leaves was reached with 40% (v/v) ethanol, an extraction time of 90 min and a temperature of 10 °C. While, the maximum TPP total polyphenols content of potato peels of 45.03 ± 4.16 mg gallic acid equivalent / g of dry matter DM was obtained using an ethanol /water mixture (40%, v/v), a time of 30 min and a temperature of 60 °C and the flavonoid contents were 13.99 and 7.52 QE quercetin equivalent/g dry matter DM, respectively. From the antioxidant tests, we deduced that the potato peels present a higher antioxidant power with the concentration of extracts causing a 50% inhibition IC50s of 125.42 ± 2.78 μg/mL for 2,2-diphényl 1-picrylhydrazyle DPPH, of 87.21 ± 7.72 μg/mL for phosphomolybdate and 200.77 ± 13.38 μg/mL for iron chelation, compared with the results obtained for shallot leaves which were 204.29 ± 0.09, 45.85 ± 3,46 and 1004.10 ± 145.73 μg/mL, respectively. The results of the physicochemical analyzes have shown that the formulated cheese was compliant with standards. Microbiological analyzes show that the hygienic quality of the cheese produced was satisfactory. According to the sensory analysis, the experts liked the cheese enriched with the powder and pieces of the leaves of the shallots.

Keywords: shallots leaves, potato peels, ultrasound extraction, phenolics, cheese

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Authors: K. Fellah, H. Benmehdi, A. Amrouche, H. Malainine, F. Memmou, H. Dalile, W. Siata

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This work aims to evaluate the antioxidant of flavonoids extracted from the leaves of Prunus persica L. A phytochemical screening allowed us to highlight the different phytochemicals present in the leaves of the studied plant. The selective extraction of flavonoids gave yields of 0.71, 1.5, and 4.8% for the fractions ethyl ether, ethyl acetate and n- butanol, respectively. The reading of the antioxidant activity of different extracts of flavonoids by HPLTC method revealed positive reaction (yellow spots) on the TLC plates sprayed with DPPH. Using the DPPH method, the fractions of flavonoids (bunanol, ethyl acetate and Diethyl ether) showed a potent scavenging activity with IC50 = 0.22; 0.27 and 0.76 mg / ml, respectively. Furthermore, our findings revealed the extracts under study exhibited higher reducing potential which depends upon extract concentration. These results obtained from this investigation confirm that the Prunus persica remains a major resource of bioactive molecules.

Keywords: Prunus persica L., phytochemical study, flavonoids, antioxidant activity, TLC bioautographic, FRAP, DPPH

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Authors: Bairam Khan, Premlata Vaishnava

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The ability to conceptualize one entity in terms of another allows us to communicate through metaphors. This central feature of human cognition has evolved with the development of language, and the processing of metaphors is without any conscious appraisal and is quite effortless. South Asians, like other speech communities, have been using the kitchenary [culinary] metaphor in a very simple yet interesting way and are known for bringing into new and unique constellations wherever they are. This composite feature of our language is used to communicate in a precise and compact manner and maneuvers the expression. The present study explores the role of kitchenary metaphors in the making and shaping of idioms by applying Cognitive Metaphor Theories. Drawing on examples from a corpus of adverts, print, and electronic media, the study looks at the metaphorical language used by real people in real situations. The overarching theme throughout the course is that kitchenary metaphors are powerful tools of expression in Hindi-Urdu.

Keywords: cognitive metaphor theory, source domain, target domain, signifier- signified, kitchenary, ethnocultural elements of south asia and hindi- urdu language

Procedia PDF Downloads 77