Search results for: neural regeneration

Authors: Palash Soni, Vivek Kumar Gaba, Shubhankar Bhowmick, Bidyut Mazumdar

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Refrigeration technology is a fast developing field at the present era since it has very wide application in both domestic and industrial areas. It started from the usage of simple ice coolers to store food stuffs to the present sophisticated cold storages along with other air conditioning system. A variety of techniques are used to bring down the temperature below the ambient. Adsorption refrigeration technology is a novel, advanced and promising technique developed in the past few decades. It gained attention due to its attractive property of exploiting unlimited natural sources like solar energy, geothermal energy or even waste heat recovery from plants or from the exhaust of locomotives to fulfill its energy need. This will reduce the exploitation of non-renewable resources and hence reduce pollution too. This work is aimed to develop a model for a solar adsorption refrigeration system and to simulate the same for different operating conditions. In this system, the mechanical compressor is replaced by a thermal compressor. The thermal compressor uses renewable energy such as solar energy and geothermal energy which makes it useful for those areas where electricity is not available. Refrigerants normally in use like chlorofluorocarbon/perfluorocarbon have harmful effects like ozone depletion and greenhouse warming. It is another advantage of adsorption systems that it can replace these refrigerants with less harmful natural refrigerants like water, methanol, ammonia, etc. Thus the double benefit of reduction in energy consumption and pollution can be achieved. A thermodynamic model was developed for the proposed adsorber, and a universal MATLAB code was used to simulate the model. Simulations were carried out for a different operating condition for the silicagel-methanol working pair. Various graphs are plotted between regeneration temperature, adsorption capacities, the coefficient of performance, desorption rate, specific cooling power, adsorption/desorption times and mass. The results proved that adsorption system could be installed successfully for refrigeration purpose as it has saving in terms of power and reduction in carbon emission even though the efficiency is comparatively less as compared to conventional systems. The model was tested for its compliance in a cold storage refrigeration with a cooling load of 12 TR.

Keywords: adsorption, refrigeration, renewable energy, silicagel-methanol

Procedia PDF Downloads 199

Authors: Li Li, Kai-Hsuan Chu

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It is well known that the real estate price depends on a lot of factors. Each house current value is dependent on the location, room number, transportation, living convenience, year and surrounding environments. Although, there are different experienced models for housing agent to estimate the price, it is a case by case study without overall dynamic variation investigation. However, many economic parameters may more or less influence the real estate price variation. Here, the influences of most macroeconomic parameters on real estate price are investigated individually based on least-square scheme and grey correlation strategy. Then those parameters are classified into leading indices, simultaneous indices and laggard indices. In addition, the leading time period is evaluated based on least square method. The important leading and simultaneous indices can be used to establish an artificial intelligent neural network model for real estate price variation prediction. The real estate price variation of Taipei, Taiwan during 2005 ~ 2017 are chosen for this research data analysis and validation. The results show that the proposed method has reasonable prediction function for real estate business reference.

Keywords: real estate price, least-square, grey correlation, macroeconomics

Procedia PDF Downloads 191

Authors: Neziha Jaouedi, Noureddine Boujnah, Mohamed Salim Bouhlel

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In the framework of human machine interaction systems enhancement, we focus throw this paper on human behavior analysis and action recognition. Human behavior is characterized by actions and reactions duality (movements, psychological modification, verbal and emotional expression). It’s worth noting that many information is hidden behind gesture, sudden motion points trajectories and speeds, many research works reconstructed an information retrieval issues. In our work we will focus on motion extraction, tracking and action recognition using wavelet network approaches. Our contribution uses an analysis of human subtraction by Gaussian Mixture Model (GMM) and body movement through trajectory models of motion constructed from kalman filter. These models allow to remove the noise using the extraction of the main motion features and constitute a stable base to identify the evolutions of human activity. Each modality is used to recognize a human action using wavelets of derived beta distributions approach. The proposed approach has been validated successfully on a subset of KTH and UCF sports database.

Keywords: feautures extraction, human action classifier, wavelet neural network, beta wavelet

Procedia PDF Downloads 405

Authors: Anastasiya Paltarzhitskaya

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Subjective time plays an important role in consciousness processes and self-awareness at the moment. The concept of intrinsic neural timescales (INT) explains the difference in perceiving various time intervals. The capacity to experience the present builds on the fundamental properties of temporal cognition. The challenge that both philosophy and neuroscience try to answer is how the brain differentiates the present from the past and future. In our work, we analyze papers which describe mechanisms involved in the perception of ‘present’ and ‘non-present’, i.e., future and past moments. Taking into account that we perceive time intervals even during rest or relaxation, we suppose that the default-mode network activity can code time features, including the present moment. We can compare some results of time perceptual studies, where brain activity was shown in states with different flows of time, including resting states and during “mental time travel”. According to the concept of mental traveling, we employ a range of scenarios which demand episodic memory. However, some papers show that the hippocampal region does not activate during time traveling. It is a controversial result that is further complicated by the phenomenological aspect that includes a holistic set of information about the individual’s past and future.

Keywords: temporal consciousness, time perception, memory, present

Procedia PDF Downloads 65

Authors: Qazi Umer Jamil, Abubakr Siddique, Mubeen Ur Rehman, Nida Aziz, Mohsin I. Tiwana

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This paper discusses a brain controlled robotic gait trainer for neurorehabilitation of Spinal Cord Injury (SCI) patients. Patients suffering from Spinal Cord Injuries (SCI) become unable to execute motion control of their lower proximities due to degeneration of spinal cord neurons. The presented approach can help SCI patients in neuro-rehabilitation training by directly translating patient motor imagery into walkers motion commands and thus bypassing spinal cord neurons completely. A non-invasive EEG based brain-computer interface is used for capturing patient neural activity. For signal processing and classification, an open source software (OpenVibe) is used. Classifiers categorize the patient motor imagery (MI) into a specific set of commands that are further translated into walker motion commands. The robotic walker also employs fall detection for ensuring safety of patient during gait training and can act as a support for SCI patients. The gait trainer is tested with subjects, and satisfactory results were achieved.

Keywords: brain computer interface (BCI), gait trainer, spinal cord injury (SCI), neurorehabilitation

Procedia PDF Downloads 152

Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

Procedia PDF Downloads 287

Authors: Mayuresh Thombre, Prajyot Borkar, Mangirish Bhobe

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New advancement of technology and never satisfying demands of the civilization are putting huge pressure on the natural fuel resources and these resources are at a constant threat to its sustainability. To get the best out of the automobile, the optimum balance between performance and fuel economy is important. In the present state of art, either of the above two aspects are taken into mind while designing and development process which puts the other in the loss as increase in fuel economy leads to decrement in performance and vice-versa. In-depth observation of the vehicle dynamics apparently shows that large amount of energy is lost during braking and likewise large amount of fuel is consumed to reclaim the initial state, this leads to lower fuel efficiency to gain the same performance. Current use of Kinetic Energy Recovery System is only limited to sports vehicles only because of the higher cost of this system. They are also temporary in nature as power can be squeezed only during a small time duration and use of superior parts leads to high cost, which results on concentration on performance only and neglecting the fuel economy. In this paper Kinetic Energy Recovery System for storing the power and then using the same while accelerating has been discussed. The major storing element in this system is a Flat Spiral Spring that will store energy by compression and torsion. The use of spring ensure the permanent storage of energy until used by the driver unlike present mechanical regeneration system in which the energy stored decreases with time and is eventually lost. A combination of internal gears and spur gears will be used in order to make the energy release uniform which will lead to safe usage. The system can be used to improve the fuel efficiency by assisting in overcoming the vehicle’s inertia after braking or to provide instant acceleration whenever required by the driver. The performance characteristics of the system including response time, mechanical efficiency and overall increase in efficiency are demonstrated. This technology makes the KERS (Kinetic Energy Recovery System) more flexible and economical allowing specific application while at the same time increasing the time frame and ease of usage.

Keywords: electric control unit, energy, mechanical KERS, planetary gear system, power, smart braking, spiral spring

Procedia PDF Downloads 194

Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from McRae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-worls, resilience to damage

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Authors: Kuei-Ling Sun, Emily Chia-Yu Su

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Allergy is a hypersensitive overreaction of the immune system to environmental stimuli, and a major health problem. These overreactions include rashes, sneezing, fever, food allergies, anaphylaxis, asthmatic, shock, or other abnormal conditions. Allergies can be caused by food, insect stings, pollen, animal wool, and other allergens. Their development of allergies is due to both genetic and environmental factors. Allergies involve immunoglobulin E antibodies, a part of the body’s immune system. Immunoglobulin E antibodies will bind to an allergen and then transfer to a receptor on mast cells or basophils triggering the release of inflammatory chemicals such as histamine. Based on the increasingly serious problem of environmental change, changes in lifestyle, air pollution problem, and other factors, in this study, we both collect allergens and non-allergens from several databases and use several machine learning methods for classification, including logistic regression (LR), stepwise regression, decision tree (DT) and neural networks (NN) to do the model comparison and determine the permutations of allergen amino acid’s sequence.

Keywords: allergy, classification, decision tree, logistic regression, machine learning

Procedia PDF Downloads 293

Authors: Belayneh Matebie, Michael Melese

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The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

Procedia PDF Downloads 37

Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

Procedia PDF Downloads 286

Authors: Neha Singh, Ananya Arora

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Facial expression measurement plays a crucial role in the identification of emotion. Facial expression plays a key role in psychophysiology, neural bases, and emotional disorder, to name a few. The Facial Action Coding System (FACS) has proven to be the most efficient and widely used of the various systems used to describe facial expressions. Coders can manually code facial expressions with FACS and, by viewing video-recorded facial behaviour at a specified frame rate and slow motion, can decompose into action units (AUs). Action units are the most minor visually discriminable facial movements. FACS explicitly differentiates between facial actions and inferences about what the actions mean. Action units are the fundamental unit of FACS methodology. It is regarded as the standard measure for facial behaviour and finds its application in various fields of study beyond emotion science. These include facial neuromuscular disorders, neuroscience, computer vision, computer graphics and animation, and face encoding for digital processing. This paper discusses the conceptual basis for FACS, a numerical listing of discrete facial movements identified by the system, the system's psychometric evaluation, and the software's recommended training requirements.

Keywords: facial action, action units, coding, machine learning

Procedia PDF Downloads 100

Authors: Naira Sahakyan, Margarit Petrosyan, Armen Trchounian

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One of the tasks in contemporary biotechnology, pharmacology and other fields of human activities is to obtain biologically active substances from plants. They are very essential in the treatment of many diseases due to their actually high therapeutic value without visible side effects. However, sometimes the possibility of obtaining the metabolites is limited due to the reduction of wild-growing plants. That is why the plant cell cultures are of great interest as alternative sources of biologically active substances. Besides, during the monitored cultivation, it is possible to obtain substances that are not synthesized by plants in nature. Isolated culture of Ajuga genevensis with high growth activity and ability of regeneration was obtained using MS nutrient medium. The agar-diffusion method showed that aqueous extracts of callus culture revealed high antimicrobial activity towards various gram-positive (Bacillus subtilis A1WT; B. mesentericus WDCM 1873; Staphylococcus aureus WDCM 5233; Staph. citreus WT) and gram-negative (Escherichia coli WKPM M-17; Salmonella typhimurium TA 100) microorganisms. The broth dilution method revealed that the minimal and half maximal inhibitory concentration values against E. coli corresponded to the 70 μg/mL and 140 μg/mL concentration of the extract respectively. According to the photochemiluminescent analysis, callus tissue extracts of leaf and root origin showed higher antioxidant activity than the same quantity of A. genevensis intact plant extract. A. genevensis intact plant and callus culture extracts showed no cytotoxic effect on K-562 suspension cell line of human chronic myeloid leukemia. The GC-MS analysis showed deep differences between the qualitative and quantitative composition of callus culture and intact plant extracts. Hexacosane (11.17%); n-hexadecanoic acid (9.33%); and 2-methoxy-4-vinylphenol (4.28%) were the main components of intact plant extracts. 10-Methylnonadecane (57.0%); methoxyacetic acid, 2-tetradecyl ester (17.75%) and 1-Bromopentadecane (14.55%) were the main components of A. genevensis callus culture extracts. Obtained data indicate that callus culture of A. genevensis can be used as an alternative source of biologically active substances.

Keywords: Ajuga genevensis, antibacterial activity, antioxidant activity, callus cultures

Procedia PDF Downloads 290

Authors: Jiajun Wang, Xiaoge Li

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The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose a new aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree

Procedia PDF Downloads 203

Authors: Qingqing Gai, Junxing Shen, Yu Luo

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Due to the hardware architecture characteristics, some operators tend to acquire better performance if the input/output tensor dimensions are aligned to a certain minimum granularity, such as convolution and deconvolution commonly used in deep learning. Furthermore, if the requirements are not met, the general strategy is to pad with 0 to satisfy the requirements, potentially leading to the under-utilization of the hardware resources. Therefore, for the convolution and deconvolution whose input and output channels do not meet the minimum granularity alignment, we propose to transfer the W-dimensional data to the C-dimension for computation (W2C) to enable the C-dimension to meet the hardware requirements. This scheme also reduces the number of computations in the W-dimension. Although this scheme substantially increases computation, the operator’s speed can improve significantly. It achieves remarkable speedups on multiple hardware accelerators, including Nvidia Tensor cores, Qualcomm digital signal processors (DSPs), and Huawei neural processing units (NPUs). All you need to do is modify the network structure and rearrange the operator weights offline without retraining. At the same time, for some operators, such as the Reducemax, we observe that transferring the Cdimensional data to the W-dimension(C2W) and replacing the Reducemax with the Maxpool can accomplish acceleration under certain circumstances.

Keywords: convolution, deconvolution, W2C, C2W, alignment, hardware accelerator

Procedia PDF Downloads 94

Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

Procedia PDF Downloads 109

Authors: Yosra Mefteh Rekik, Younes Boujelbene

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We introduce in this paper a new conceptual model representing the stock market dynamics. This model is essentially based on cognitive behavior of the intelligence investors. In order to validate our model, we build an artificial stock market simulation based on agent-oriented methodologies. The proposed simulator is composed of market supervisor agent essentially responsible for executing transactions via an order book and various kinds of investor agents depending to their profile. The purpose of this simulation is to understand the influence of psychological character of an investor and its neighborhood on its decision-making and their impact on the market in terms of price fluctuations. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Keywords: artificial intelligence methods, artificial stock market, behavioral modeling, multi-agent based simulation

Procedia PDF Downloads 436

Authors: Sang-Myung Jung, Jeong Hyun Ju, Gwang Heum Yoon, Hwa Sung Shin

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Central nervous system (CNS) consists of neuronal cell and supporting cells. Astrocytes are the most common supporting cells and play roles in metabolism between neurons and blood vessel. For this function, engineered astrocytes have been studied as a therapeutic source for CNS injury. In neural tissue engineering, nanofiber has been suggested as an effective scaffold for providing structure and mechanical properties influencing physiology. Cellulose acetate (CA) has been investigated for material to fabricate scaffold because of its biocompatibility, biodegradability and fine thermal stability. In this research, CA nanofiber was modified via heat treatment and its effect on astrocyte activity was evaluated. Adhesion and viability of astrocyte were increased in proportion to stiffness. Additionally, expression of GFAP, a marker of astrocyte activation, was increased via stiffness of scaffold. This research suggests a simple modification method to change stiffness of CA nanofiber and shows cellular behavior affecting stiffness of three-dimensional scaffold independently. For the results, we highlight that the stiffness is a factor to regulate astrocyte activity.

Keywords: astrocyte, cellulose acetate, cell therapy, stiffness of scaffold

Procedia PDF Downloads 472

Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

Procedia PDF Downloads 136

Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

Procedia PDF Downloads 152

Authors: Marizia Roberta Rasoanandrasana, Hery Lisy Tiana. Ranarijaona, Herintsitohaina Razakamanarivo, Eric Delaitre, Nandrianina Ramifehiarivo

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Madagascar is one of the producer’s countries of world's fine cocoa. Cocoa-based agroforestry systems (CBAS) plays a very important economic role for over 75% of the population in the north of Madagascar, the island's main cocoa-producing area. It is also viewed as a key factor in the deforestation of local protected areas. It is therefore urgent to establish a compromise between cocoa production and forest conservation in this region which is difficult due to a lack of accurate cocoa agro-systems data. In order to fill these gaps and to response to these socio-economic and environmental concerns, this study aims to describe CBAS by providing precise data on their characteristics and to establish a typology. To achieve this, 150 farms were surveyed and observed to characterize CBAS based on 11 agronomic and 6 socio-economic data. Also, 30 representative plots of CBAS among the 150 farms were inventoried for providing accurate ecological data (6 variables) as an additional data for the typology determination. The results showed that Madagascar’s CBAS systems are generally extensive and practiced by smallholders. Four types of cocoa-based agroforestry system were identified, with significant differences between the following variables: yield, planting age, cocoa density, density of associated trees, preceding crop, associated crops, Shannon-Wiener indices and species richness in the upper stratum. Type 1 is characterized by old systems (>45 years) with low crop density (425 cocoa trees/ha), installed after conversion of crops other than coffee (> 50%) and giving low yields (427 kg/ha/year). Type 2 consists of simple agroforestry systems (no associated crop 0%), fairly young (20 years) with low density of associated trees (77 trees/ha) and low species diversity (H'=1.17). Type 3 is characterized by high crop density (778 trees/ha and 175 trees/ha for cocoa and associated trees respectively) and a medium level of species diversity (H'=1.74, 8 species). Type 4 is particularly characterized by orchard regeneration method involving replanting and tree lopping (100%). Analysis of the potential of these four types has identified Type 4 as a promising practice for sustainable agriculture.

Keywords: conservation, practices, productivity, protect areas, smallholder, trade-off, typology

Procedia PDF Downloads 95

Authors: Utkarsh A. Mishra, Ankit Bansal

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At high temperature, radiative heat transfer is the dominant mode of heat transfer. It is governed by various phenomena such as photon emission, absorption, and scattering. The solution of the governing integrodifferential equation of radiative transfer is a complex process, more when the effect of participating medium and wavelength properties are taken into consideration. Although a generic formulation of such radiative transport problem can be modeled for a wide variety of problems with non-gray, non-diffusive surfaces, there is always a trade-off between simplicity and accuracy of the problem. Recently, solutions of complicated mathematical problems with statistical methods based on randomization of naturally occurring phenomena have gained significant importance. Photon bundles with discrete energy can be replicated with random numbers describing the emission, absorption, and scattering processes. Photon Monte Carlo (PMC) is a simple, yet powerful technique, to solve radiative transfer problems in complicated geometries with arbitrary participating medium. The method, on the one hand, increases the accuracy of estimation, and on the other hand, increases the computational cost. The participating media -generally a gas, such as CO₂, CO, and H₂O- present complex emission and absorption spectra. To model the emission/absorption accurately with random numbers requires a weighted sampling as different sections of the spectrum carries different importance. Importance sampling (IS) was implemented to sample random photon of arbitrary wavelength, and the sampled data provided unbiased training of MC estimators for better results. A better replacement to uniform random numbers is using deterministic, quasi-random sequences. Halton, Sobol, and Faure Low-Discrepancy Sequences are used in this study. They possess better space-filling performance than the uniform random number generator and gives rise to a low variance, stable Quasi-Monte Carlo (QMC) estimators with faster convergence. An optimal supervised learning scheme was further considered to reduce the computation costs of the PMC simulation. A one-dimensional plane-parallel slab problem with participating media was formulated. The history of some randomly sampled photon bundles is recorded to train an Artificial Neural Network (ANN), back-propagation model. The flux was calculated using the standard quasi PMC and was considered to be the training target. Results obtained with the proposed model for the one-dimensional problem are compared with the exact analytical and PMC model with the Line by Line (LBL) spectral model. The approximate variance obtained was around 3.14%. Results were analyzed with respect to time and the total flux in both cases. A significant reduction in variance as well a faster rate of convergence was observed in the case of the QMC method over the standard PMC method. However, the results obtained with the ANN method resulted in greater variance (around 25-28%) as compared to the other cases. There is a great scope of machine learning models to help in further reduction of computation cost once trained successfully. Multiple ways of selecting the input data as well as various architectures will be tried such that the concerned environment can be fully addressed to the ANN model. Better results can be achieved in this unexplored domain.

Keywords: radiative heat transfer, Monte Carlo Method, pseudo-random numbers, low discrepancy sequences, artificial neural networks

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

Procedia PDF Downloads 117

Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

Procedia PDF Downloads 241

Authors: Ailing Yang, Penghan Song, Aihua Cai

Abstract:

The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods.

Keywords: maneuvering target, state estimation, Kalman filter, LSTM, self-attention

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Authors: Albert Alexander Stonier

Abstract:

Due to the fossil fuel exhaustion and environmental pollution, renewable energy sources especially solar photovoltaic system plays a predominant role in providing energy to the consumers. It has been estimated that by 2050 the renewable energy sources will satisfy 50% of the total energy requirement of the world. In this context, the faults in the conversion process require a special attention which is considered as a major problem. A fault which remains even for a few seconds will cause undesirable effects to the system. The presentation comprises of the analysis, causes, effects and mitigation methods of various faults occurring in the entire solar photovoltaic energy conversion process. In order to overcome the faults in the system, an intelligent based artificial neural networks and fuzzy logic are proposed which can significantly mitigate the faults. Hence the presentation intends to find the problem in renewable energy and provides the possible solution to overcome it with simulation and experimental results. The work performed in a 3kWp solar photovoltaic plant whose results cites the improvement in reliability, availability, power quality and fault tolerant ability.

Keywords: solar photovoltaic, power electronics, power quality, PWM

Procedia PDF Downloads 269

Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

Abstract:

A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

Procedia PDF Downloads 114

Authors: Nathenal Thomas Lambamo

Abstract:

Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Joseph Wolff, Jeffrey Eilbott

Abstract:

Autism Spectrum Disorders (ASD) are a spectrum of social disorders characterized by deficits in social communication, verbal ability, and interaction that can vary in severity. In recent years, researchers have used magnetic resonance imaging (MRI) to help detect how neural patterns in individuals with ASD differ from those of neurotypical (NT) controls for classification purposes. This study analyzed the classification of ASD within males and females using functional MRI data. Functional connectivity (FC) correlations among brain regions were used as feature inputs for machine learning algorithms. Analysis was performed on 558 cases from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. When trained specifically on females, the algorithm underperformed in classifying the ASD subset of our testing population. Although the subject size was relatively smaller in the female group, the manual matching of both male and female training groups helps explain the algorithm’s bias, indicating the altered sex abnormalities in functional brain networks compared to typically developing peers. These results highlight the importance of taking sex into account when considering how generalizations of findings on males with ASD apply to females.

Keywords: autism spectrum disorder, machine learning, neuroimaging, sex differences

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Authors: Skiker Kaoutar, Mounir Maouene

Abstract:

Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from Mc Rae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-world, resilience to damage

Procedia PDF Downloads 534