Search results for: inference extraction

Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

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In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

Procedia PDF Downloads 513

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

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

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

Procedia PDF Downloads 265

Authors: Ting Shen, Spyros Konstantopoulos

Abstract:

During the past three decades, class size effects have been a focal debate in education. The idea of having smaller class is enormously popular among parents, teachers and policy makers. The rationale of its popularity is that small classroom could provide a better learning environment in which there would be more teacher-pupil interaction and more individualized instruction. This early stage benefits would also have a long-term positive effect. It is a common belief that reducing class size may result in increases in student achievement. However, the empirical evidence about class-size effects from experimental or quasi-experimental studies has been mixed overall. This study sheds more light on whether class size reduction impacts reading achievement in eight European countries: Bulgaria, Germany, Hungary, Italy, Lithuania, Romania, Slovakia, and Slovenia. We examine class size effects on reading achievement using national probability samples of fourth graders. All eight European countries had participated in the Progress in International Reading Literacy Study (PIRLS) in 2001, 2006 and 2011. Methodologically, the quasi-experimental method of instrumental variables (IV) has been utilized to facilitate causal inference of class size effects. Overall, the results indicate that class size effects on reading achievement are not significant across countries and years. However, class size effects are evident in Romania where reducing class size increases reading achievement. In contrast, in Germany, increasing class size seems to increase reading achievement. In future work, it would be valuable to evaluate differential class size effects for minority or economically disadvantaged student groups or low- and high-achievers. Replication studies with different samples and in various settings would also be informative. Future research should continue examining class size effects in different age groups and countries using rich international databases.

Keywords: class size, reading achievement, instrumental variables, PIRLS

Procedia PDF Downloads 291

Authors: Boukari Nassim

Abstract:

This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

Procedia PDF Downloads 346

Authors: Rajkumar Ghosh

Abstract:

The industrialization of coal mining and related activities has a significant impact on groundwater in the surrounding areas of the Damodar River. The Damodar River basin, located in eastern India, is known as the "Ruhr of India" due to its abundant coal reserves and extensive coal mining and industrial operations. One of the major consequences of collieries on groundwater is the contamination of water sources. Coal mining activities often involve the excavation and extraction of coal through underground or open-pit mining methods. These processes can release various pollutants and chemicals into the groundwater, including heavy metals, acid mine drainage, and other toxic substances. As a result, the quality of groundwater in the Damodar River region has deteriorated, making it unsuitable for drinking, irrigation, and other purposes. The high concentration of heavy metals, such as arsenic, lead, and mercury, in the groundwater has posed severe health risks to the local population. Prolonged exposure to contaminated water can lead to various health problems, including skin diseases, respiratory issues, and even long-term ailments like cancer. The contamination has also affected the aquatic ecosystem, harming fish populations and other organisms dependent on the river's water. Moreover, the excessive extraction of groundwater for industrial processes, including coal washing and cooling systems, has resulted in a decline in the water table and depletion of aquifers. This has led to water scarcity and reduced availability of water for agricultural activities, impacting the livelihoods of farmers in the region. Efforts have been made to mitigate these issues through the implementation of regulations and improved industrial practices. However, the historical legacy of coal industrialization continues to impact the groundwater in the Damodar River area. Remediation measures, such as the installation of water treatment plants and the promotion of sustainable mining practices, are essential to restore the quality of groundwater and ensure the well-being of the affected communities. In conclusion, the coal industrialization in the Damodar River surrounding has had a detrimental impact on groundwater. This research focuses on soil subsidence induced by the over-exploitation of ground water for dewatering open pit coal mines. Soil degradation happens in arid and semi-arid regions as a result of land subsidence in coal mining region, which reduces soil fertility. Depletion of aquifers, contamination, and water scarcity are some of the key challenges resulting from these activities. It is crucial to prioritize sustainable mining practices, environmental conservation, and the provision of clean drinking water to mitigate the long-lasting effects of collieries on the groundwater resources in the region.

Keywords: coal mining, groundwater, soil subsidence, water table, damodar river

Procedia PDF Downloads 80

Authors: Farahnaz Karami

Abstract:

Routing is an important, challenging task in mobile ad hoc networks due to node mobility, lack of central control, unstable links, and limited resources. An ant colony has been found to be an attractive technique for routing in Mobile Ad Hoc Networks (MANETs). However, existing swarm intelligence based routing protocols find an optimal path by considering only one or two route selection metrics without considering correlations among such parameters making them unsuitable lonely for routing real time applications. Fuzzy logic combines multiple route selection parameters containing uncertain information or imprecise data in nature, but does not have multipath routing property naturally in order to provide load balancing. The objective of this paper is to design a routing algorithm using fuzzy logic and ant colony that can solve some of routing problems in mobile ad hoc networks, such as nodes energy consumption optimization to increase network lifetime, link failures rate reduction to increase packet delivery reliability and providing load balancing to optimize available bandwidth. In proposed algorithm, the path information will be given to fuzzy inference system by ants. Based on the available path information and considering the parameters required for quality of service (QoS), the fuzzy cost of each path is calculated and the optimal paths will be selected. NS2.35 simulation tools are used for simulation and the results are compared and evaluated with the newest QoS based algorithms in MANETs according to packet delivery ratio, end-to-end delay and routing overhead ratio criterions. The simulation results show significant improvement in the performance of these networks in terms of decreasing end-to-end delay, and routing overhead ratio, and also increasing packet delivery ratio.

Keywords: mobile ad hoc networks, routing, quality of service, ant colony, fuzzy logic

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Authors: Jorge Anibal Restrepo Morales, Sonia Martín Gómez

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A model based on a logical framework was developed to quantify SMEs' internationalization capacity. To do so, linguistic variables, such as human talent, infrastructure, innovation strategies, FTAs, marketing strategies, finance, etc. were integrated. It is argued that a company’s management of international markets depends on internal factors, especially capabilities and resources available. This study considers internal factors as the biggest business challenge because they force companies to develop an adequate set of capabilities. At this stage, importance and strategic relevance have to be defined in order to build competitive advantages. A fuzzy inference system is proposed to model the resources, skills, and capabilities that determine the success of internationalization. Data: 157 linguistic variables were used. These variables were defined by international trade entrepreneurs, experts, consultants, and researchers. Using expert judgment, the variables were condensed into18 factors that explain SMEs’ export capacity. The proposed model is applied by means of a case study of the textile and clothing cluster in Medellin, Colombia. In the model implementation, a general index of 28.2 was obtained for internationalization capabilities. The result confirms that the sector’s current capabilities and resources are not sufficient for a successful integration into the international market. The model specifies the factors and variables, which need to be worked on in order to improve export capability. In the case of textile companies, the lack of a continuous recording of information stands out. Likewise, there are very few studies directed towards developing long-term plans, and., there is little consistency in exports criteria. This method emerges as an innovative management tool linked to internal organizational spheres and their different abilities.

Keywords: business strategy, exports, internationalization, fuzzy set methods

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Authors: Chris Miskelly, Eoin Cunningham, Beatrice Smyth, John. D. Holbrey, Gosia Swadzba-Kwasny, Emily L. Byrne, Yoan Delavoux, Mantian Li.

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Recently, the use of ionic liquids (ILs) for the preparation of lignocellulose derived cellulosic materials as alternatives to petrochemical feedstocks has been the focus of considerable research interest. While the technical viability of IL-based lignocellulose treatment methodologies has been well established, the high cost of reagents inhibits commercial feasibility. This work aimed to assess the technoeconomic viability of the preparation of cellulose rich materials (CRMs) using protic ionic liquids (PILs) synthesized from low cost alkylamines and sulphuric acid. For this purpose, the tertiary alkylamines, triethylamine, and dimethylbutylamine were selected. Bulk scale production cost of the synthesized PILs, triethylammonium hydrogen sulphate and dimetheylbutylammonium hydrogen sulphate, was reported as $0.78 kg-1 to $1.24 kg-1. CRMs were prepared through the treatment of common wheat (Triticum aestivum) straw with these PILs. By controlling treatment parameters, CRMs with a cellulose content of ≥ 80 wt% were prepared. This was achieved using a T. aestivum straw to PIL loading ratio of 1:15 w/w, a treatment duration of 180 minutes, and ethanol as a cellulose antisolvent. Infrared spectra data and decreased onset degradation temperature of CRMs (ΔTONSET ~ 70 °C) suggested the formation of cellulose sulphate esters during treatment. Chemical derivatisation can aid the dispersion of prepared CRMs in non-polar polymer/ composite matrices, but act as a barrier to thermal processing at temperatures above 150 °C. It was also shown that treatment increased the crystallinity of CRMs (ΔCrI ~ 40 %) without altering the native crystalline structure or crystallite size (~ 2.6 nm) of cellulose; peaks associated with the cellulose I crystalline planes (110), (200), and (004) were observed at Bragg angles 16.0 °, 22.5 ° and 35.0 ° respectively. This highlighted the inability of assessed PILs to dissolve crystalline cellulose and was attributed to the high acidity (pKa ~ - 1.92 to - 6.42) of sulphuric acid derived anions. Electron micrographs revealed that the stratified multilayer tissue structure of untreated T. aestivum straw was significantly modified during treatment. T. aestivum straw particles were disassembled during treatment, with prepared CRMs adopting a golden-brown film-like appearance. This work demonstrated the degradation of non-cellulosic fractions of lignocellulose without dissolution of cellulose. It is the first to report on the derivatisation of cellulose during treatment with protic hydrogen sulphate ionic liquids, and the potential implications of this with reference to biopolymer feedstock preparation.

Keywords: cellulose, extraction, protic ionic liquids, esterification, thermal stability, waste valorisation, biopolymer feedstock

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Authors: Piyush Samant, Ravinder Agarwal

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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: complementary and alternative medicine, classification, iridology, iris, feature extraction, disease prediction

Procedia PDF Downloads 407

Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

Procedia PDF Downloads 320

Authors: Kilari Nikhil, Ankur Tibrewal, Srinivas Kruthiventi S. S.

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Conventional speaker identification systems often fall short of capturing the diverse variations present in speech data due to fixed-scale architectures. In this research, we propose a CNN-based architecture, USENet, designed to overcome these limitations. Leveraging two key techniques, our approach achieves superior performance on the VoxCeleb 1 Dataset without any pre-training. Firstly, we adopt a U-net-inspired design to extract features at multiple scales, empowering our model to capture speech characteristics effectively. Secondly, we introduce the squeeze and excitation block to enhance spatial feature learning. The proposed architecture showcases significant advancements in speaker identification, outperforming existing methods, and holds promise for future research in this domain.

Keywords: multi-scale feature extraction, squeeze and excitation, VoxCeleb1 speaker identification, mel-spectrograms, USENet

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Authors: S. Ebadzadsahraei, H. Kazemian

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The main objective of this research was to measure particulate matter (PM₂.₅) and Volatile Organic Compound (VOCs) as two classes of air pollutants, at Prince George (PG) neighborhood in warm and cold seasons. To fulfill this objective, analytical protocols were developed for accurate sampling and measurement of the targeted air pollutants. PM₂.₅ samples were analyzed for their chemical composition (i.e., toxic trace elements) in order to assess their potential source of emission. The City of Prince George, widely known as the capital of northern British Columbia (BC), Canada, has been dealing with air pollution challenges for a long time. The city has several local industries including pulp mills, a refinery, and a couple of asphalt plants that are the primary contributors of industrial VOCs. In this research project, which is the first study of this kind in this region it measures physical and chemical properties of particulate air pollutants (PM₂.₅) at the city neighborhood. Furthermore, this study quantifies the percentage of VOCs at the city air samples. One of the outcomes of this project is updated data about PM₂.₅ and VOCs inventory in the selected neighborhoods. For examining PM₂.₅ chemical composition, an elemental analysis methodology was developed to measure major trace elements including but not limited to mercury and lead. The toxicity of inhaled particulates depends on both their physical and chemical properties; thus, an understanding of aerosol properties is essential for the evaluation of such hazards, and the treatment of such respiratory and other related diseases. Mixed cellulose ester (MCE) filters were selected for this research as a suitable filter for PM₂.₅ air sampling. Chemical analyses were conducted using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for elemental analysis. VOCs measurement of the air samples was performed using a Gas Chromatography-Flame Ionization Detector (GC-FID) and Gas Chromatography-Mass Spectrometry (GC-MS) allowing for quantitative measurement of VOC molecules in sub-ppb levels. In this study, sorbent tube (Anasorb CSC, Coconut Charcoal), 6 x 70-mm size, 2 sections, 50/100 mg sorbent, 20/40 mesh was used for VOCs air sampling followed by using solvent extraction and solid-phase micro extraction (SPME) techniques to prepare samples for measuring by a GC-MS/FID instrument. Air sampling for both PM₂.₅ and VOC were conducted in summer and winter seasons for comparison. Average concentrations of PM₂.₅ are very different between wildfire and daily samples. At wildfire time average of concentration is 83.0 μg/m³ and daily samples are 23.7 μg/m³. Also, higher concentrations of iron, nickel and manganese found at all samples and mercury element is found in some samples. It is able to stay too high doses negative effects.

Keywords: air pollutants, chemical analysis, particulate matter (PM₂.₅), volatile organic compound, VOCs

Procedia PDF Downloads 142

Authors: Allal Bedlaoui, Hamid Boutoutaou

Abstract:

Due to its superior economics, shrink-fit assembly is one of the best mechanical assembly methods. There are simply two components, the axis and hub. It is used in many different industries, including the production of trains, cars, and airplanes. The outer radius of the inner cylinder must be greater than the inner radius of the outer cylinder for this operation; this difference is referred to as the "interference" between the two cylinders. There are three ways to accomplish this: heating the outer cylinder to cause it to expand; cooling the cylinder's inside to cause it to contract; and third, finishing the fitting under a press. At the intersection of the two matched parts, a contact pressure and friction force are generated. We consider interference and form defects in this article because they prevent the connection between the axis and the hub from having a perfect form surface and because we will be looking at how they affect the assembly. Numerical simulation is used to ascertain if interference and form defects have a beneficial or negative influence in the distribution of stresses, assembly resistance, and plasticity.

Keywords: shrink-fit, interference, form defect, plasticity, extraction force

Procedia PDF Downloads 78

Authors: Reut Lanyado, David Mendlovic

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Most biological methods for authentication require special equipment and, some of them are easy to fake. We proposed a method for authentication based on hand movement while typing a sentence with a regular camera. This technique uses the full video of the hand, which is harder to fake. In the first phase, we tracked the hand joints in each frame. Next, we represented a single frame for each individual using our Pose Agnostic Rotation and Movement (PARM) dimensional space. Then, we indicated a full video of hand movement in a fixed low dimensional space using this method: Fixed Dimension Video by Interpolation Statistics (FDVIS). Finally, we identified each individual in the FDVIS representation using unsupervised clustering and supervised methods. Accuracy exceeds 96% for 80 individuals by using supervised KNN.

Keywords: authentication, feature extraction, hand recognition, security, signal processing

Procedia PDF Downloads 127

Authors: Thaís T. Valério Caetano, João Máximo De Siqueira, Carlos Alexandre Carollo, Arthur Ladeira Macedo, Vanessa C. Stein

Abstract:

Nicotiana glauca, commonly known as wild tobacco or tobacco bush, belongs to the Solanaceae family. It is native to South America but has become naturalized in various regions, including Australia, California, Africa, and the Mediterranean. N. glauca is listed in the Global Invasive Species Database (GISD) and the Invasive Species Compendium (CABI). It is known for producing pyridine alkaloids, including anabasine, which is highly toxic. Anabasine is predominantly found in the leaves and can cause severe health issues such as neuromuscular blockade, respiratory arrest, and cardiovascular problems when ingested. Mistaken identity with edible plants like spinach has resulted in food poisoning cases in Israel and Brazil. Anabasine, a minor alkaloid constituent of tobacco, may contribute to tobacco addiction by mimicking or enhancing the effects of nicotine. Therefore, it is essential to investigate the production pattern of anabasine and its relationship to the developmental stages of the plant. This study aimed to establish the relationship between the phenological plant age, cultivation place, and the increase in anabasine concentration, which can lead to human intoxication cases. In this study, N. glauca plants were collected from three different rural areas in Brazil for a year to examine leaves at various stages of development. Samples were also obtained from cultivated plants in Marilândia, Minas Gerais, Brazil, as well as from Divinópolis, Minas Gerais, Brazil, and Arraial do Cabo, Rio de Janeiro, Brazil. In vitro cultivated plants on MS medium were included in the study. The collected leaves were dried, powdered, and stored. Alkaloid extraction was performed using a methanol and water mixture, followed by liquid-liquid extraction with chloroform. The anabasine content was determined using HPLC-DAD analysis with nicotine as a standard. The results indicated that anabasine production increases with the plant's development, peaking in adult leaves during the reproduction phase and declining afterward. In vitro, plants showed similar anabasine production to young leaves. The successful adaptation of N. glauca in new environments poses a global problem, and the correlation between anabasine production and the plant's developmental stages has been understudied. The presence of substances produced by the plant can pose a risk to other species, especially when mistaken for edible plants. The findings from this study shed light on the pattern of anabasine production and its association with plant development, contributing to a better understanding of the potential risks associated with N. glauca and the importance of accurate identification.

Keywords: nicotiana glauca graham, global invasive species database, alkaloids, toxic

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Authors: M. Sari Yilmaz, N. Karamahmut Mermer

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Coal fly ash is formed as a solid waste product from the combustion of coal in coal fired power stations. Huge amounts of fly ash are produced globally every year and are predicted to increase. Nowadays, less than half of the fly ash is used as a raw material for cement manufacturing, construction and the rest of it is disposed as a waste causing yet another environmental concern. For this reason, the recycling of this kind of slurries into useful materials is quite important in terms of economical and environmental aspects. The purpose of this study is to evaluate the Orhaneli and Tuncbilek coal fly ashes for utilization in some industrial applications. Therefore the mineralogical and chemical compositions of these fly ashes were analyzed by X-ray fluorescence (XRF) spectroscopy and X-ray diffraction (XRD). The silicon (Si) and aluminum (Al) in the fly ashes were activated by alkali fusion technique with sodium hydroxide. The obtained extracts were analyzed for Si and Al content by inductively coupled plasma optical emission spectrometry (ICP-OES).

Keywords: extraction, fly ash, fusion, XRD

Procedia PDF Downloads 322

Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

Procedia PDF Downloads 154

Authors: Thaís T. Valério Caetano, Lívia de Carvalho Ferreira, João Máximo De Siqueira, Carlos Alexandre Carollo, Arthur Ladeira Macedo, Vanessa C. Stein

Abstract:

Nicotiana glauca, commonly known as wild tobacco or tobacco bush, belongs to the Solanaceae family. It is native to South America but has become naturalized in various regions, including Australia, California, Africa, and the Mediterranean. N. glauca is listed in the Global Invasive Species Database (GISD) and the Invasive Species Compendium (CABI). It is known for producing pyridine alkaloids, including anabasine, which is highly toxic. Anabasine is predominantly found in the leaves and can cause severe health issues such as neuromuscular blockade, respiratory arrest, and cardiovascular problems when ingested. Mistaken identity with edible plants like spinach has resulted in food poisoning cases in Israel and Brazil. Anabasine, a minor alkaloid constituent of tobacco, may contribute to tobacco addiction by mimicking or enhancing the effects of nicotine. Therefore, it is essential to investigate the production pattern of anabasine and its relationship to the developmental stages of the plant. This study aimed to establish the relationship between the phenological plant age, cultivation place, and the increase in anabasine concentration, which can lead to human intoxication cases. In this study, N. glauca plants were collected from three different rural areas in Brazil during a year to examine leaves at various stages of development. Samples were also obtained from cultivated plants in Marilândia, Minas Gerais, Brazil, as well as from Divinópolis, Minas Gerais, Brazil, and Arraial do Cabo, Rio de Janeiro, Brazil. In vitro cultivated plants on MS medium were included in the study. The collected leaves were dried, powdered, and stored. Alkaloid extraction was performed using a methanol and water mixture, followed by liquid-liquid extraction with chloroform. The anabasine content was determined using HPLC-DAD analysis with nicotine as a standard. The results indicated that anabasine production increases with the plant's development, peaking in adult leaves during the reproduction phase and declining afterward. In vitro, plants showed similar anabasine production to young leaves. The successful adaptation of N. glauca in new environments poses a global problem, and the correlation between anabasine production and the plant's developmental stages has been understudied. The presence of substances produced by the plant can pose a risk to other species, especially when mistaken for edible plants. The findings from this study shed light on the pattern of anabasine production and its association with plant development, contributing to a better understanding of the potential risks associated with N. glauca and the importance of accurate identification.

Keywords: alkaloid production, invasive species, nicotiana glauca, plant phenology

Procedia PDF Downloads 83

Authors: Niharika Kaushal, Minni Singh

Abstract:

Fruit crops are widely cultivated throughout the World, with citrus being one of the most common. Mandarins, oranges, grapefruits, lemons, and limes are among the most frequently grown varieties. Citrus cultivars are industrially processed into juice, resulting in approx. 25-40% by wt. of biomass in the form of peels and seeds, generally considered as waste. In consequence, a significant amount of this nutraceutical-enriched biomass goes to waste, which, if utilized wisely, could revolutionize the functional food industry, as this biomass possesses a wide range of bioactive compounds, mainly within the class of polyphenols and terpenoids, making them an abundant source of functional bioactive. Mandarin is a potential source of bioflavonoids with putative antioxidative properties, and its potential application for developing value-added products is obvious. In this study, ‘kinnow’ mandarin (Citrus nobilis X Citrus deliciosa) biomass was studied for its flavonoid profile. For this, dried and pulverized peels were subjected to green and sustainable extraction techniques, namely, supercritical fluid extraction carried out under conditions pressure: 330 bar, temperature: 40 ̊ C and co-solvent: 10% ethanol. The obtained extract was observed to contain 47.3±1.06 mg/ml rutin equivalents as total flavonoids. Mass spectral analysis revealed the prevalence of polymethoxyflavones (PMFs), chiefly tangeretin and nobiletin. Furthermore, the antioxidant potential was analyzed by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method, which was estimated to be at an IC₅₀ of 0.55μg/ml. The pre-systemic metabolism of flavonoids limits their functionality, as was observed in this study through in vitro gastrointestinal studies where nearly 50.0% of the flavonoids were degraded within 2 hours of gastric exposure. We proposed nanoencapsulation as a means to overcome this problem, and flavonoids-laden polylactic-co-glycolic acid (PLGA) nano encapsulates were bioengineered using solvent evaporation method, and these were furnished to a particle size between 200-250nm, which exhibited protection of flavonoids in the gastric environment, allowing only 20% to be released in 2h. A further step involved impregnating the nano encapsulates within alginate hydrogels which were fabricated by ionic cross-linking, which would act as delivery vehicles within the gastrointestinal (GI) tract. As a result, 100% protection was achieved from the pre-systemic release of bioflavonoids. These alginate hydrogels had key significant features, i.e., less porosity of nearly 20.0%, and Cryo-SEM (Cryo-scanning electron microscopy) images of the composite corroborate the packing ability of the alginate hydrogel. As a result of this work, it is concluded that the waste can be used to develop functional biomaterials while retaining the functionality of the bioactive itself.

Keywords: bioflavonoids, gastrointestinal, hydrogels, mandarins

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Authors: G. C. Santini, C. Potrich, L. Lunelli, L. Vanzetti, S. Marasso, M. Cocuzza, C. Pederzolli

Abstract:

The relevance of circulating miRNAs as non-invasive biomarkers for several pathologies is nowadays undoubtedly clear, as they have been found to have both diagnostic and prognostic value able to add fundamental information to patients’ clinical picture. The availability of these data, however, relies on a time-consuming process spanning from the sample collection and processing to the data analysis. In light of this, strategies which are able to ease this procedure are in high demand and considerable effort have been made in developing Lab-on-a-chip (LOC) devices able to speed up and standardise the bench work. In this context, a very promising polydimethylsiloxane (PDMS)-based microdevice which integrates the processing of the biological sample, i.e. purification of extracellular miRNAs, and reverse transcription was previously developed in our lab. In this study, we aimed at the improvement of the miRNA extraction performances of this micro device by increasing the ability of its surface to absorb extracellular miRNAs from biological samples. For this purpose, we focused on the modulation of two properties of the material: roughness and charge. PDMS surface roughness was modulated by casting with several templates (terminated with silicon oxide coated by a thin anti-adhesion aluminum layer), followed by a panel of curing conditions. Atomic force microscopy (AFM) was employed to estimate changes at the nanometric scale. To introduce modifications in surface charge we functionalized PDMS with different mixes of positively charged 3-aminopropyltrimethoxysilanes (APTMS) and neutral poly(ethylene glycol) silane (PEG). The surface chemical composition was characterized by X-ray photoelectron spectroscopy (XPS) and the number of exposed primary amines was quantified with the reagent sulfosuccinimidyl-4-o-(4,4-dimethoxytrityl) butyrate (s-SDTB). As our final end point, the adsorption rate of all these different conditions was assessed by fluorescence microscopy by incubating a synthetic fluorescently-labeled miRNA. Our preliminary analysis identified casting on thermally grown silicon oxide, followed by a curing step at 85°C for 1 hour, as the most efficient technique to obtain a PDMS surface roughness in the nanometric scaleable to trap miRNA. In addition, functionalisation with 0.1% APTMS and 0.9% PEG was found to be a necessary step to significantly increase the amount of microRNA adsorbed on the surface, therefore, available for further steps as on-chip reverse transcription. These findings show a substantial improvement in the extraction efficiency of our PDMS microdevice, ultimately leading to an important step forward in the development of an innovative, easy-to-use and integrated system for the direct purification of less abundant circulating microRNAs.

Keywords: circulating miRNAs, diagnostics, Lab-on-a-chip, polydimethylsiloxane (PDMS)

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Authors: Ching-Ju Hsiao, Mao-Chen Huang, Pei-Fan Chen, Ruo-Yun Chung, Jiun-Hua Chou, Chih-Hui Yang, Keng-Shiang Huang, Jei-Fu Shaw

Abstract:

Chlorophyll has many benefits for human body. It is known to improve the health of the circulatory, digestive, immune and detoxification systems of the body. However, Chl can’t be preserved at the environment of high temperature and light exposure for a long time due to it is chemical structure is easily degradable. This characteristic causes that human body is difficult to absorb Chl effective components. In order to solve this problem, we utilize polycaprolactone (PCL) polymer encapsulation technology to increase the stability of Chl. In particular, we also established a microfluidic platform provide the control of composite beads diameter. The new composite beads is potential to be a health food. Result show that Chl effective components via the microfludic platform can be encapsulated effectively and still preserve its effective components.

Keywords: chlorophyll, PCL, PVA, microfluidic

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Authors: Emmanuel Chinaguh, Kehinde Adeosun

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Nigerians die untimely, with 55.75 years of life expectancy, which is 17.45 below the world average of 73.2 (Worldometer, 2020). This is due, among other factors, to the country's limited access to high-quality healthcare. To increase access to good and affordable healthcare services, the National Health Insurance Authority (NHIA) Bill 2022 – which repealed the National Health Insurance Scheme Act 2004 – was passed into law. Applying Jacob Mey’s (2001) pragmatics act (pract) theory, this study explores how NHIA seeks to actualise these healthcare goals by characterising the general situational prototype or pragmemes and pragmatic acts in institutional communications. Data was sourced from the NHIA operational guidelines, which has 147 pages and four sections, and shared posters on NHIA Nigeria Twitter Handle with 14,200 followers. Digital humanities tools, like AntConc and Voyant, were engaged in the data analysis for text encoding and data visualisation. This study identifies these discourse tokens in the data: advertisement and programmes, standards and accreditation, records and information, and offences and penalties. Advertisement and programmes pract facilitating, propagating, prospecting, advising and informing; standards and accreditation, and records and information pract stating, informing and instructing; and offences and penalties pract stating and sanctioning. These practs combined to advance the goals of affordable care and universal accessibility to quality healthcare services. The pragmatic acts were marked by these pragmatic tools: shared situational knowledge (SSK), relevance (REL), reference (REF) and inference (INF). This paper adds to the understanding of health insurance discourse in Nigeria as a mediated social practice that promotes the health of Nigerians.

Keywords: affordable care, NHIA, Nigeria’s health insurance discourse, pragmatic acts.

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Authors: Yong Chen

Abstract:

To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: Maad Kamal Al-Anni

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This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Intrusion Detection System (IDS), Mobile Adhoc Networks (MANET), Back Propagation Algorithm (BPA), Neural Networks (NN)

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Authors: Majid Forghani, Michael Khachay

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In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay.

Keywords: antigenic variants, neighbor effect, wavelet packet, wavelet particle decomposition

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Authors: Lumindu Dilumka, Ranaweera I. D., Sudusinghe S. P., Sanduni Kanchana A. M. K.

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This study was motivated by the challenges experienced by parents and guardians in ensuring the safety of children in cyberspace. In the last two or three years, online education has become very popular all over the world due to the Covid 19 pandemic. Therefore, parents, guardians and teachers must ensure the safety of children in cyberspace. Children are more likely to go astray and there are plenty of online programs are waiting to get them on the wrong track and also, children who are engaging in the online education can be distracted at any moment. Therefore, parents should keep a close check on their children's online activity. Apart from that, due to the unawareness of children, they tempt to share their sensitive information, causing a chance of being a victim of phishing attacks from outsiders. These problems can be overcome through the proposed web-based system. We use feature extraction, web tracking and analysis mechanisms, image processing and name entity recognition to implement this web-based system.

Keywords: online education, cyber bullying, social media, face recognition, web tracker, privacy data

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Authors: Saleem Abuleil, Khalid S. Alsamara

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Events represent a significant source of information on the web; they deliver information about events that occurred around the world in all kind of subjects and areas. These events can be collected and organized to provide valuable and useful information for decision makers, researchers, as well as any person seeking knowledge. In this paper, we discuss an ongoing research to target stock markets domain to observe and record changes (events) when they happen, collect them, understand the meaning of each one of them, and organize the information along with meaning in a well-structured format. By using Semantic Role Labeling (SRL) technique, we identified four factors for each event in this paper: verb of action and three roles associated with it, entity name, attribute, and attribute value. We have generated a set of rules and techniques to support our approach to analyze and understand the meaning of the events taking place in stock markets.

Keywords: natuaral language processing, Arabic language, event extraction and understanding, sematic role labeling, stock market

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Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

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This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: neural network, self-organizing map, rule extraction, rule insertion

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Authors: Delana Theiventhiran, Wally J. Bartfay

Abstract:

Background and Significance: As the Internet is becoming the epitome of modern communications, there are many pragmatic reasons why the digital divide matters in terms of accessing and using E-health based technologies. With the rise of technology usage globally, those in the older adult generation may not be as familiar and comfortable with technology usage and are thus put at a disadvantage compared to other generations such as millennials when examining and using E-health based platforms and technology. Currently, little is known about how older adults and millennials access and use e-health based technologies. Methods: A systemic review of the literature was undertaken employing the following three databases: (i) PubMed, (ii) ERIC, and (iii) CINAHL; employing the search term 'digital divide and generations' to identify potential articles. To extract required data from the studies, a data abstraction tool was created to obtain the following information: (a) author, (b) year of publication, (c) sample size, (d) country of origin, (e) design/methods, (f) major findings/outcomes obtained. Inclusion criteria included publication dates between the years of Jan 2009 to Aug 2018, written in the English language, target populations of older adults aged 65 and above and millennials, and peer reviewed quantitative studies only. Major Findings: PubMed provided 505 potential articles, where 23 of those articles met the inclusion criteria. Specifically, ERIC provided 53 potential articles, where no articles met criteria following data extraction. CINAHL provided 14 potential articles, where eight articles met criteria following data extraction. Conclusion: Practically speaking, identifying how newer E-health based technologies can be integrated into society and identifying why there is a gap with digital technology will help reduce the impact on generations and individuals who are not as familiar with technology and Internet usage. The largest concern of all is how to prepare older adults for new and emerging E-health technologies. Currently, there is a dearth of literature in this area because it is a newer area of research and little is known about it. The benefits and consequences of technology being integrated into daily living are being investigated as a newer area of research. Several of the articles (N=11) indicated that age is one of the larger factors contributing to the digital divide. Similarly, many of the examined articles (N=5) identify that privacy concerns were one of the main deterrents of technology usage for elderly individuals aged 65 and above. The older adult generation feels that privacy is one of the major concerns, especially in regards to how data is collected, used and possibly sold to third party groups by various websites. Additionally, access to technology, the Internet, and infrastructure also plays a large part in the way that individuals are able to receive and use information. Lastly, a change in the way that healthcare is currently used, received and distributed would also help attribute to the change to ensure that no generation is left behind in a technologically advanced society.

Keywords: digital divide, e-health, millennials, older adults

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Authors: G. Medkour Ishak-Boushaki, A. Taibi, M. Allab

Abstract:

Quantitative environmental radioactivity measurements are needed to determine the level of exposure of a population to ionizing radiations and for the assessment of the associated risks. Gamma spectrometry remains a very powerful tool for the analysis of radionuclides present in an environmental sample but the basic problem in such measurements is the low rate of detected events. Using large environmental samples could help to get around this difficulty but, unfortunately, new issues are raised by gamma rays attenuation and self-absorption. Recently, a new method has been suggested, to detect and identify without quantification, in a short time, a gamma ray of a low count source. This method does not require, as usually adopted in gamma spectrometry measurements, a pulse height spectrum acquisition. It is based on a chronological record of each detected photon by simultaneous measurements of its energy ε and its arrival time τ on the detector, the pair parameters [ε,τ] defining an event mode sequence (EMS). The EMS serials are analyzed sequentially by a Bayesian approach to detect the presence of a given radioactive source. The main object of the present work is to test the applicability of this sequential approach in radioactive environmental materials detection. Moreover, for an appropriate health oversight of the public and of the concerned workers, the analysis has been extended to get a reliable quantification of the radionuclides present in environmental samples. For illustration, we consider as an example, the problem of detection and quantification of 238U. Monte Carlo simulated experience is carried out consisting in the detection, by a Ge(Hp) semiconductor junction, of gamma rays of 63 keV emitted by 234Th (progeny of 238U). The generated EMS serials are analyzed by a Bayesian inference. The application of the sequential Bayesian approach, in environmental radioactivity analysis, offers the possibility of reducing the measurements time without requiring large environmental samples and consequently avoids the attached inconvenient. The work is still in progress.

Keywords: Bayesian approach, event mode sequence, gamma spectrometry, Monte Carlo method

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