Search results for: neural activity

Authors: P. Renjitha, P. Hari Prakash

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Auditory neuropathy spectrum disorder (ANSD) is an auditory disorder with normal cochlear outer hair cell function and disrupted auditory nerve function. It results in unique clinical characteristic with absent auditory brainstem response (ABR), absent acoustic reflex and the presence of otoacoustic emissions (OAE) and cochlear microphonics. The lesion site could be at cochlear inner hair cells, the synapse between the inner hair cells and type I auditory nerve fibers, and/or the auditory nerve itself. But the literatures on synchrony at higher auditory system are sporadic and are less understood. It might be interesting to see if there is a recovery of neural synchrony at higher auditory centers. Also, does the level at which the auditory system recovers with adequate synchrony to the extent of observable evoke response potentials (ERPs) can predict speech perception? In the current study, eight ANSD participants and healthy controls underwent detailed audiological assessment including ABR, auditory middle latency response (AMLR), and auditory late latency response (ALLR). AMLR was recorded for clicks and ALLR was evoked using 500Hz and 2 kHz tone bursts. Analysis revealed that the participant could be categorized into three groups. Group I (2/8) where ALLR was present only for 2kHz tone burst. Group II (4/8), where AMLR was absent and ALLR was seen for both the stimuli. Group III (2/8) consisted individuals with identifiable AMLR and ALLR for all the stimuli. The highest speech identification sore observed in ANSD group was 30% and hence considered having poor speech perception. Overall test result indicates that the site of neural synchrony recovery could be varying across individuals with ANSD. Some individuals show recovery of neural synchrony at the thalamocortical level while others show the same only at the cortical level. Within ALLR itself there could be variation across stimuli again could be related to neural synchrony. Nevertheless, none of these patterns could possible explain the speech perception ability of the individuals. Hence, it could be concluded that neural synchrony as measured by evoked potentials could not be a good clinical predictor speech perception.

Keywords: auditory late latency response, auditory middle latency response, auditory neuropathy spectrum disorder, correlation with speech identification score

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Authors: Kheira A. Bekhechi, Belkacem Khiat

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The goal of our study is to know if there is an influence of the regular sporting physical-activity on the school performances of Algerian children. An experimental group composed of 55 sporting pupils and a reference group of 55 non-sporting pupils between 6 to10 years old (boys and girls) of the primary schools in Oran (Algeria) were followed during 15 months (Five terms). The socio-demographic data was collected from a survey given to pupils of the two groups and the school results from the administration at the end of each term. The sporting pupils have a general school average significantly higher than those of the non- sporting pupils (p < 0.05). The practice of physical activity and regular sporting by the children would deserve to be largely encouraged based on the beneficial effects not only on health but also on the academic performance. The parents, teachers and health professionals should be strongly aware.

Keywords: cognitive capacities, physical activity and sport, school children, school performances

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

Procedia PDF Downloads 388

Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva

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Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.

Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining

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Authors: Dina Helmy El-Ghonemy, Thanaa Hamed Ali

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The aim of this study was to purify and characterize a keratinolytic enzyme produced by Aspergillus sp. DHE7 cultured in basal medium containing chicken feather as substrate. The enzyme was purified through ammonium sulfate saturation of 60%, followed by gel filtration chromatography in Sephadex G-100, with a 16.4-purification fold and recovery yield of 52.2%. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis revealed that the purified enzyme is a monomeric enzyme with an apparent molecular mass of 30 kDa — the purified keratinase of Aspergillus sp. DHE7 exhibited activity in a broad range of pH (7- 9) and temperature (40℃-60℃) profiles with an optimal activity at pH eight and 50℃. The keratinolytic activity was inhibited by protease inhibitors such as phenylmethylsulfonyl fluoride and ethylenediaminetetraacetate, while no reduction of activity was detected by the addition of dimethyl sulfoxide (DMSO). Bivalent cations, Ca²⁺ and Mn²⁺, were able to greatly enhance the activity of keratinase by 125.7% and 194.8%, respectively, when used at one mM final concentration. On the other hand, Cu²⁺ and Hg²⁺ inhibited the enzyme activity, which might be indicative of essential vicinal sulfhydryl groups of the enzyme for productive catalysis. Furthermore, the purified keratinase showed significant stability and compatibility against the tested commercial detergents at 37ºC. Therefore, these results suggested that the purified keratinase from Aspergillus sp. DHE7 may have potential use in the detergent industry and should be of interest in the processing of poultry feather waste.

Keywords: Aspergillus sp. DHE7, biochemical characterization, keratinase, purification, waste management

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Authors: K. Biswas, U. H. Armin, S. M. J. Prodhan, J. A. Prithul, S. Sarker, F. Afrin

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Alzheimer’s disease (AD) (a progressive neurodegenerative disorder) is mostly predominant cause of dementia in the elderly. Prolonging the function of acetylcholine by inhibiting both acetylcholinesterase and butyrylcholinesterase is most effective treatment therapy of AD. Traditionally Pterocarpus santalinus L. is widely known for its medicinal use. In this study, in vitro acetylcholinesterase inhibitory activity was investigated and methanolic extract of the plant showed significant activity. To confirm this activity (in vivo), learning and memory enhancing effects were tested in mice. For the test, memory impairment was induced by scopolamine (cholinergic muscarinic receptor antagonist). Anti-amnesic effect of the extract was investigated by the passive avoidance task in mice. The study also includes brain acetylcholinesterase activity. Results proved that scopolamine induced cognitive dysfunction was significantly decreased by administration of the extract solution, in the passive avoidance task and inhibited brain acetylcholinesterase activity. These results suggest that bark extract of Pterocarpus santalinus can be better option for further studies on AD via their acetylcholinesterase inhibitory actions.

Keywords: Pterocarpus santalinus, cholinesterase inhibitor, passive avoidance, Alzheimer’s disease

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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The aim of this study was to develop an Artificial Neural Network0 s recommendation model for an online process using the complexity of load, performance, and average servicing time of the resources. Here, the proposed model investigates the resource performance using stochastic gradient decent method for learning ranking function. A probabilistic cost function is implemented to identify the optimal θ values (load) on each resource. Based on this result the recommendation of resource suitable for performing the currently executing task is made. The test result of CoSeLoG project is presented with an accuracy of 72.856%.

Keywords: ADALINE, neural network, gradient decent, process mining, resource behaviour, polynomial regression model

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Authors: Kefaya Qaddoum

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The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

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Authors: Chin-Yuan Hsu, Yu-Lung Chuang

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The expression, concentration, and activity of mitochondrial energy-utilized molecules and cellular energy-regulated molecules decreased with age in the trophocytes and oenocytes of honeybees (Apis mellifera), but those of cellular energy-metabolized molecules is unknown. In this study, the expression, concentration, and activity of cellular energy-metabolized molecules were assayed in the trophocytes and fat cells of young and old worker bees by using the techniques of cell and biochemistry. The results showed that (i) the •-hydroxylacyl-coenzyme A dehydrogenase (HOAD) activity/citrate synthase (CS) activity ratio, non-esterified fatty acids concentrations, the expression of eukaryotic initiation factor 4E, and the expression of phosphorylated eIF4E binding protein 1 decreased with age; (ii) fat and glycogen accumulation increased with age; and (iii) the pyruvate dehydrogenase (PDH) activity/citrate synthase (CS) activity ratio was not correlated with age. These finding indicated that •-oxidation (HOAD/CS) and protein synthsis decreased with age. Glycolysis (PDH/CS) was unchanged with age. The most likely reason is that sugars are the vital food of worker bees. Taken together these data reveal that young workers have higher cellular energy metabolism than old workers and that aging results in a decline in the cellular energy metabolism in worker honeybees.

Keywords: aging, energy, honeybee, metabolism

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: S. Raeis Farshid, B. Raeis Farshid

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Ag-TiO₂ nanocomposites were prepared by the sol-gel method with and without additives such as carboxy methyl cellulose (CMC), polyethylene glycol (PEG), polyvinyl pyrrolidone (PVP), and hydroxyl propyl cellulose (HPC). The characteristics of the prepared Ag-TiO₂ nanocomposites were identified by Fourier Transform Infra-Red spectroscopy (FTIR), X-Ray Diffraction (XRD), and scanning electron microscopy (SEM) methods. The additives have a significant effect on the particle size distribution and photocatalytic activity of Ag-TiO₂ nanocomposites. SEM images have shown that the particle size distribution of Ag-TiO₂ nanocomposite in the presence of HPC was the best in comparison to the other samples. The photocatalytic activity of the synthesized nanocomposites was investigated for decolorization of methyl orange (MO) in water under UV-irradiation in a batch reactor, and the results showed that the photocatalytic activity of the nanocomposites had been increased by CMC, PEG, PVP, and HPC, respectively.

Keywords: sol-gel method, Ag-TiO₂, decolorization, photocatalyst, nanocomposite

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Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Khalida Boutemak, Nasssima Benali, Nadji Moulai-Mostefa

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Effect of enzyme on the yield and chemical composition of Artemisia campestris essential oil is reported in the present study. It was demonstrated that enzyme facilitated the extraction of essential oil with increase in oil yield and did not affect any noticeable change in flavour profile of the volatile oil. Essential oil was tested for antibacterial activity using Escherichia coli; which was extremely sensitive against control with the largest inhibition (29mm), whereas Staphylococcus aureus was the most sensitive against essential oil obtained from enzymatic pre-treatment with the largest inhibition zone (25mm). The antioxidant activity of the essential oil with hemicellulase pre-treatment (EO2) and control sample (EO1) was determined through reducing power. It was significantly lower than the standard drug (vitamin C) in this order: vitamin C˃EO2˃EO1.

Keywords: Artemisia campestris, enzyme pre-treatment, hemicellulase, antibacterial activity, antioxidant activity

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Ahmet Faruk Akyuz, Hasan Sakir Bilge

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Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea.

Keywords: thermal stereo matching, deep neural networks, CNN, Depth estimation

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Authors: Ana Tsokilauri

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The uppermost factor in the regulation of lignin-cellulose activity of decaying white rot or free rot are the substances serving as carbon and nitrogen nutrition of microorganisms and are considered as the most important factor of generative activity of white rot. The research object was Basidiomycete Fungi, peculiar and common in Georgia, and the separation of 10 of them as pure crops. The unidentified pure crops have tasted in order to be determined the potential of synthesis of lignin-degrading enzymes and the substrate of optimal lignocellulose growth. One of the most important aspects of the research conducted on Basidiomycetes was the use of specific lignocellulosic residues for selecting Fungi as a substrate of their growth. In order to increase lignocellulose with the help of substrate, crops were selected from the screening stage that showed good laccase activity. (Dusheti 1; Dusheti 10; Fshavi 5; Fshavi1; Fshavi 8; Fshavi 32; Manglisi 26; Sabaduri20; Dusheti 7; Sabaduri 1 ), Among the selected cultures, the crops with good laccase activity against the following substances, in particular: Dusheti 1- in this case, the rate of enzymatic activity on bran substrate was -105,6 u/ml, mandarin-96,4 u/ml. In case of corn - 102,9 u/ml. In case of Dusheti 7- the indicators were as follows: bananas-121,7 u/ml, mandarin-125,4 u/ml, corn - 117,1 u/ml. In the case of Sanaduri 32, the laccase activity was as follows: pomegranate- 101,2 u/ml. As a result of conducted experiments, the synthesis and activity rates of enzymes depending on plant substrates varied within a fairly wide range, which is still being under research.

Keywords: Lignocellulosic substrate, Basidiomycetes, white-rot basidiomycetes, Laccase

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Authors: Omaimah Algohary

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Kaolin is one of the principle intestinal adsorbents, has traditionally been used internally in the treatment of various enteric disorders, colitis, enteritis, dysentery, and diarrhea associated with food and alkaloidal poisoning and in traveler’s diarrhea. It binds to and traps bacteria and its toxins and gases in the gut. It also binds to water in the gut, which helps to make the stools firmer, hence giving symptomatic relief. Metronidazole is a synthetic antibacterial agent that is used primarily in the treatment of various anaerobic infections such as intra-abdominal infections, antiprotozoal, and as amebicidal. The need for safe, therapeutically effective antidiarrheal combination continuously lead to effective treatment. Metronidazol used for treatment of anaerobic bacteria and kaolin , when administered simultaneously, Metronidazole–Kaolin interactions have been reported by FDA but not studied. This project is the first to study the effect of Metronidazole–Kaolin interactions on the antimicrobial activity of metronidazole. Agar diffusion method performed to test the antimicrobial activity of metronidazole–kaolin antidiarrheal combination from aqueous solutions at an in-vivo simulated pHs conditions that obtained at 37+0.5 °C on Helicobacter pylori as anaerobic bacteria and E.coli as aerobic bacteria and used as a control for the technique. The antimicrobial activity of metronidazole combination as 1:1 and 1:2 with kaolin was abolished in acidic media as no zones of inhibition shown compared to only metronidazole that used as a control. In alkaline media metronidazole combination as 1:1 and 1:2 with kaolin showed diminutive activity compared to the control. These results proved that the kaolin adsorb metronidazole and abolish its antimicrobial activity and such combination should be avoided.

Keywords: kaolin, metronidazole, interaction, Helicobacter pylori. E. coli, antimicrobial activity

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Authors: Heidarali Malmir

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The effects of acid rain (AR), smog’s cars (SC), and combined AR+SC on the antioxidants enzymes, lipid-soluble antioxidants, and water-soluble antioxidants were studied in the two cultivars of rice. The results showed that simulated AR significantly increased the total glutathione (TGSH), thiobarbituric acid (TBA), and α-tocopherol, accompanied by decreases in dry weight and leaves area in the two cultivars, and this change was more obvious in Shirudi cultivar than in Aus cultivar (p≤0.05). Under SC stress cultivar shirudi had higher H+-ATPase, glutathione peroxidase (GSH-px), and catalase (CAT) activities than cultivar Aus. The results of superoxide dismutase (SOD) activity, TGSH, and α-tocopherol levels affected by AR treatments were very different to those of SOD activity, TGSH, and α-tocopherol levels, as shown in SC treatment. It seems that SOD activity coupled with the water-soluble antioxidants and α-tocopherol levels correlated with the lipid-soluble antioxidants. It is suggested that α-tocopherol increases H+-ATPase activity.

Keywords: H+-ATPase, membrane permeability, lipid soluble antioxidants, water soluble antioxidants, associated enzyme

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Bahaa Eldin A. Fouda, Khaled N. Yossef, Mohamed Elhosseny, Ahmed Lotfy, Mohamed Salama, Mohamed Sobh

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Developmental neurotoxicity (DNT) entails the toxic effects imparted by various chemicals on the brain during the early childhood period. As human brains are vulnerable during this period, various chemicals would have their maximum effects on brains during early childhood. Some toxicants have been confirmed to induce developmental toxic effects on CNS e.g. lead, however; most of the agents cannot be identified with certainty due the defective nature of predictive toxicology models used. A novel alternative method that can overcome most of the limitations of conventional techniques is the use of 3D neurospheres system. This in-vitro system can recapitulate most of the changes during the period of brain development making it an ideal model for predicting neurotoxic effects. In the present study, we verified the possible DNT of epoxomicin which is a naturally occurring selective proteasome inhibitor with anti-inflammatory activity. Rat neural progenitor cells were isolated from rat embryos (E14) extracted from placental tissue. The cortices were aseptically dissected out from the brains of the fetuses and the tissues were triturated by repeated passage through a fire-polished constricted Pasteur pipette. The dispersed tissues were allowed to settle for 3 min. The supernatant was, then, transferred to a fresh tube and centrifuged at 1,000 g for 5 min. The pellet was placed in Hank’s balanced salt solution cultured as free-floating neurospheres in proliferation medium. Two doses of epoxomicin (1µM and 10µM) were used in cultured neuropsheres for a period of 14 days. For proliferation analysis, spheres were cultured in proliferation medium. After 0, 4, 5, 11, and 14 days, sphere size was determined by software analyses. The diameter of each neurosphere was measured and exported to excel file further to statistical analysis. For viability analysis, trypsin-EDTA solution were added to neurospheres for 3 min to dissociate them into single cells suspension, then viability evaluated by the Trypan Blue exclusion test. Epoxomicin was found to affect proliferation and viability of neuropsheres, these effects were positively correlated to doses and progress of time. This study confirms the DNT effects of epoxomicin on 3D neurospheres model. The effects on proliferation suggest possible gross morphologic changes while the decrease in viability propose possible focal lesion on exposure to epoxomicin during early childhood.

Keywords: neural progentor cells, epoxomicin, neurosphere, medical and health sciences

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Amira Kalifa, Faïek Errouissi

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The seasonal changes and the daily activity of Stomoxys calcitrans (Diptera: Muscidae) were examined, using Vavoua traps, in a dairy cattle farm in Zaouiet Sousse, the Sahel area of Tunisia during May 2014 to October 2014. Over this period, a total of 4366 hematophagous diptera were captured and Stomoxys calcitrans was the most commonly trapped species (96.52%). Analysis of the seasonal activity, showed that S.calcitrans is bivoltine, with two peaks: a significant peak is recorded in May-June, during the dry season, and a second peak at the end of October, which is quite weak. This seasonal pattern would depend on climatic factors, particularly the temperature of the manure and that of the air. The activity pattern of Stomoxys calcitrans was diurnal with seasonal variations. The daily rhythm shows a peak between 11:00 am to 15:00 pm in May and between 11:00 am to 17:00 pm in June. These vector flies are important pests of livestock in Tunisia, where they are known as a mechanical vector of several pathogens and have a considerable economic and health impact on livestock. A better knowledge of their ecology is a prerequisite for more efficient control measures.

Keywords: cattle farm, daily rhythm, Stomoxys calcitrans, seasonal activity

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Authors: M. Pramila Devi, N. V. N. Indra Kiran

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Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point’s exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed.

Keywords: control chart pattern recognition, neural network, backpropagation, generalization, early stopping

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Authors: I. Hua Chu, Hsiang-Chi Yu, Hsuan Su

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Purpose: To examine the associations between self-determined motivation and physical activity in patients with coronary heart disease (CHD) in a longitudinal study. Methods: Patients with CHD were recruited for this study. Their motivations for exercise were measured by the Behavioral Regulation in Exercise Questionnaire-2 (BREQ-2). Physical activity was assessed using the 7-day physical activity recall questionnaire. Duration and energy expenditure of moderate to vigorous physical activity (MVPA) were used in data analysis. All outcome measures were assessed at baseline and 12 months follow up. Data were analyzed using Pearson correlation analysis and regression analysis. Results: The results of the 45 participants (mean age 60.24 yr; 90.2% male) revealed that there were significant negative correlations between amotivation at baseline and duration (r=-.295, p=.049) and energy expenditure (r=-.300, p=.045) of MVPA at 12 months. In contrast, there were significant positive correlations between calculated relative autonomy index (RAI) at baseline and duration (r=.377, p=.011) and energy expenditure (r=.382, p=.010) of MVPA at 12 months. There was no significant correlation between other subscales of the BREQ-2 and duration or energy expenditure of MVPA. Regression analyses revealed that RAI was a significant predictor of duration (p=.011) and energy expenditure (p=.010) of MVPA at 12 months follow-up. Conclusions: These results suggest that the relative degree of self-determined motivation could predict long-term MVPA behaviors in CHD patients. Physical activity interventions are recommended to target enhancing one’s identified and intrinsic motivation to increase the likelihood of physical activity participation in this population.

Keywords: self-determined motivation, physical activity, coronary heart disease, relative autonomy index (RAI)

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Authors: Hiba Hamdi, Thomas Corpetti, Laure Roupioz, Xavier Briottet

Abstract:

Air temperatures are generally higher in cities than in their rural surroundings. The overheating of cities is a direct consequence of increasing urbanization, characterized by the artificial filling of soils, the release of anthropogenic heat, and the complexity of urban geometry. This phenomenon, referred to as urban heat island (UHI), is more prevalent during heat waves, which have increased in frequency and intensity in recent years. In the context of global warming and urban population growth, helping urban planners implement UHI mitigation and adaptation strategies is critical. In practice, the study of UHI requires air temperature information at the street canyon level, which is difficult to obtain. Many urban air temperature simulation models have been proposed (mostly based on physics or statistics), all of which require a variety of input parameters related to urban morphology, land use, material properties, or meteorological conditions. In this paper, we build and evaluate a neural network model based on Urban Weather Generator (UWG) model simulations and data from meteorological stations that simulate air temperature over Toulouse, France, on days favourable to UHI.

Keywords: air temperature, neural network model, urban heat island, urban weather generator

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Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

Abstract:

This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.

Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

Abstract:

One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Ogbonnaya Nwokoro, Florence O. Anya

Abstract:

Nutritional factors relating to the production of linamarase from Lactobacillus delbrueckii NRRL B–763 were investigated. The microorganism was cultivated in a medium containing 1% linamarin. Enzyme was produced using a variety of carbon substrates but the highest enzyme activity was detected in the presence of salicin (522 U/ml) after 48 h while the lowest yield was observed with CM cellulose (38 U/ml) after 72 h. Enzyme was not produced in the presence of cellobiose. Among a variety of nitrogen substrates tested, peptone supported maximum enzyme production (412 U/ml) after 48 h. Lowest enzyme production was observed with urea (40 U/ml). Organic nitrogen substrates generally supported higher enzyme productivity than inorganic nitrogen substrates. Enzyme activity was observed in the presence of Mn2+ (% relative activity = 216) while Hg2+ was inhibitory (% relative activity = 28). Locally-formulated media were comparable to MRS broth in supporting linamarase production by the bacterium. Higher enzyme activity was produced in media with surfactant than in media without surfactant. The enzyme may be useful in enhanced degradation of cassava cyanide.

Keywords: linamarase, locally formulated media, carbon substrates, nitrogen substrates, metal ions

Procedia PDF Downloads 427