Search results for: histological features

Authors: Ben G. Weber, Michael Mateas

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We present a case-based reasoning technique for selecting build orders in a real-time strategy game. The case retrieval process generalizes features of the game state and selects cases using domain-specific recall methods, which perform exact matching on a subset of the case features. We demonstrate the performance of the technique by implementing it as a component of the integrated agent framework of McCoy and Mateas. Our results demonstrate that the technique outperforms nearest-neighbor retrieval when imperfect information is enforced in a real-time strategy game.

Keywords: case based reasoning, real time strategy systems, requirements elicitation, requirement analyst, artificial intelligence

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Authors: Eman A. Al-Hilo, Hawraa H. Al-Waelly

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In this work, fractal compression (FIC) technique is introduced based on using moment features to block indexing the zero-mean range-domain blocks. The moment features have been used to speed up the IFS-matching stage. Its moments ratio descriptor is used to filter the domain blocks and keep only the blocks that are suitable to be IFS matched with tested range block. The results of tests conducted on Lena picture and Cat picture (256 pixels, resolution 24 bits/pixel) image showed a minimum encoding time (0.89 sec for Lena image and 0.78 of Cat image) with appropriate PSNR (30.01dB for Lena image and 29.8 of Cat image). The reduction in ET is about 12% for Lena and 67% for Cat image.

Keywords: fractal gray level image, fractal compression technique, iterated function system, moments feature, zero-mean range-domain block

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Musaab Hasan, Farkhund Iqbal, Patrick C. K. Hung, Benjamin C. M. Fung, Laura Rafferty

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In modern societies, the smart cities concept raised simultaneously with the projection towards adopting smart devices. A smart grid is an essential part of any smart city as both consumers and power utility companies benefit from the features provided by the power grid. In addition to advanced features presented by smart grids, there may also be a risk when the grids are exposed to malicious acts such as security attacks performed by terrorists. Considering advanced security measures in the design of smart meters could reduce these risks. This paper presents a security study for smart metering systems with a prototype implementation of the user interfaces for future works.

Keywords: security design, smart city, smart meter, smart grid, smart metering system

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Authors: Sanchali Das, Swapan Debbarma

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Music mood classification is developing in both the areas of music information retrieval (MIR) and natural language processing (NLP). Some of the Indian languages like Hindi English etc. have considerable exposure in MIR. But research in mood classification in regional language is very less. In this paper, powerful audio based feature for Kokborok Christian song is identified and mood classification task has been performed. Kokborok is an Indo-Burman language especially spoken in the northeastern part of India and also some other countries like Bangladesh, Myanmar etc. For performing audio-based classification task, useful audio features are taken out by jMIR software. There are some standard audio parameters are there for the audio-based task but as known to all that every language has its unique characteristics. So here, the most significant features which are the best fit for the database of Kokborok song is analysed. The regression-based model is used to find out the independent parameters that act as a predictor and predicts the dependencies of parameters and shows how it will impact on overall classification result. For classification WEKA 3.5 is used, and selected parameters create a classification model. And another model is developed by using all the standard audio features that are used by most of the researcher. In this experiment, the essential parameters that are responsible for effective audio based mood classification and parameters that do not significantly change for each of the Christian Kokborok songs are analysed, and a comparison is also shown between the two above model.

Keywords: Christian Kokborok song, mood classification, music information retrieval, regression

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Authors: Punnoose A. K.

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This paper presents an approach for identifying noisy speech recording using a multi-layer perception (MLP) trained to predict phonemes from acoustic features. Characteristics of the MLP posteriors are explored for clean speech and noisy speech at the frame level. Appropriate density functions are used to fit the softmax probability of the clean and noisy speech. A function that takes into account the ratio of the softmax probability density of noisy speech to clean speech is formulated. These phoneme independent scoring is weighted using a phoneme-specific weightage to make the scoring more robust. Simple thresholding is used to identify the noisy speech recording from the clean speech recordings. The approach is benchmarked on standard databases, with a focus on precision.

Keywords: noisy speech identification, speech pre-processing, noise robustness, feature engineering

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Authors: Tusar Kanti Dash, Ganapati Panda

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The evaluation of speech quality and intelligence is critical to the overall effectiveness of the Speech Enhancement Algorithms. Several intrusive and non-intrusive measures are employed to calculate these parameters. Non-Intrusive Evaluation is most challenging as, very often, the reference clean speech data is not available. In this paper, a novel non-intrusive speech evaluation measure is proposed using audio features derived from the Stockwell transform. These features are used with the Light Gradient Boosting Machine for the effective prediction of speech quality and intelligibility. The proposed model is analyzed using noisy and reverberant speech from four databases, and the results are compared with the standard Intrusive Evaluation Measures. It is observed from the comparative analysis that the proposed model is performing better than the standard Non-Intrusive models.

Keywords: non-Intrusive speech evaluation, S-transform, light GBM, speech quality, and intelligibility

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Authors: Sanjit Kumar Paul, A. A. Mamun, M. R. Amin

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The Linear propagation of the dust-acoustic wave in a dusty plasma consisting of Boltzmann distributed electrons and ions and mobile charge fluctuating positive dust grains has been investigated by employing the reductive perturbation method. It has been shown that the dust charge fluctuation is a source of dissipation and its responsible for the formation of the dust-acoustic waves in such a dusty plasma. The basic features of such dust-acoustic waves have been identified. It has been proposed to design a new laboratory experiment which will be able to identify the basic features of the dust-acoustic waves predicted in this theoretical investigation.

Keywords: dust acoustic waves, dusty plasma, Boltzmann distributed electrons, charge fluctuation

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Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

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Authors: Y. Xu, L. Xiong, Z. Xu

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In order to protect data privacy, image with sensitive or private information needs to be encrypted before being outsourced to the cloud. However, this causes difficulties in image retrieval and data management. A secure image retrieval method based on orthogonal decomposition is proposed in the paper. The image is divided into two different components, for which encryption and feature extraction are executed separately. As a result, cloud server can extract features from an encrypted image directly and compare them with the features of the queried images, so that the user can thus obtain the image. Different from other methods, the proposed method has no special requirements to encryption algorithms. Experimental results prove that the proposed method can achieve better security and better retrieval precision.

Keywords: secure image retrieval, secure search, orthogonal decomposition, secure cloud computing

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Authors: Natalia Efremova, Sergey Tarasenko

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We propose a novel neural network architecture for visual saliency detections, which utilizes neuro physiologically plausible mechanisms for extraction of salient regions. The model has been significantly inspired by recent findings from neuro physiology and aimed to simulate the bottom-up processes of human selective attention. Two types of features were analyzed: color and direction of maximum variance. The mechanism we employ for processing those features is PCA, implemented by means of normalized Hebbian learning and the waves of spikes. To evaluate performance of our model we have conducted psychological experiment. Comparison of simulation results with those of experiment indicates good performance of our model.

Keywords: neural network models, visual saliency detection, normalized Hebbian learning, Oja's rule, psychological experiment

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Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

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Authors: O. Nkwonta, B. Dzwairo, F. Otieno, J. Adeyemo

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The effective management of water resources is of great importance to ensure the supply of water resources to support changing water requirements over a selected planning horizon and in a sustainable and cost-effective way. Essentially, the purpose of the water resources planning process is to balance the available water resources in a system with the water requirements and losses to which the system is subjected. In such situations, water resources yield and planning model can be used to solve those difficulties. It has an advantage over other models by managing model runs, developing a representative system network, modelling incremental sub-catchments, creating a variety of standard system features, special modelling features, and run result output options.

Keywords: complex, water resources, planning, cost effective, management

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Authors: Nurgül Şenol, Melda Azman

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In this study, gastrointestinal immunohistochemistry in the Vimba vimba and the localization of CCK/gastrin were determined. Although there are a number of studies which relate to the gastrointestinal histochemistry and the localization of the peptides, a literature research in this field revealed that no histochemical or immunohistochemical study covering also the species had been found in our country. In this research, species will be provided from Vimba vimba located in Eğirdir lake. Stomach samples and intestinal samples of these fish will be exposed to routine histological tissue process, embedded in paraffin blocks, and 5-6 μ -thick sections will be taken. Using the PAP (Peroxidase anti-peroxidase) method, localization of the peptides CCK/gastrin was to be found. The densities of peptides of this species were compared, and then the findings obtained were to be evaluated through the statistical analysis methods (SPSS). Endocrine cells reactive to gastrin/CCK antiserum were demonstrated in the stomach and intestinal mucosa. There is a significant difference between gastrin and CCK when compared to regions.

Keywords: CCK, gastrin, immunoreactivity, vimba vimba

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

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Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

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Authors: Daleesha M. Viswanathan, Sumam Mary Idicula

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Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively.

Keywords: orientation features, discrete feature vector, HMM., Indian sign language

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Hussah Alotaishan

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In today’s globally-driven modern cities diverse groups of new visitors face various challenges when attempting to find their desired location if culture and language are barriers. The most common way-showing tools such as directional and identificational signs are the most problematic and their usefulness can be limited or even non-existent. It is argued new methods should be implemented that could support or replace such conventional literacy and language dependent way-finding aids. It has been concluded in recent research studies that local urban features in complex pedestrian spaces are worthy of further study in order to reveal if they do function as way-showing clues. Some researchers propose a more comprehensive approach to the complex perception of buildings, façade design and surface patterns, while some have been questioning whether we necessarily need directional signs or can other methods deliver the same message but in a clearer manner for a wider range of users. This study aimed to test to what extent do existent environmental and urban features through the city center area of Swansea in the UK facilitate the way-finding process of a first time visitor. The three-hour experiment was set to attempt to find 11 visitor attractions ranging from recreational, historical, educational and religious locations. The challenge was attempting to find as many as possible when no prior geographical knowledge of their whereabouts was established. The only clues were 11 pictures representing each of the locations that had been acquired from the city of Swansea official website. An iPhone and a heart-rate tracker wristwatch were used to record the route was taken and stress levels, and take record photographs of destinations or decision-making points throughout the journey. This paper addresses: current limitations in understanding the ways that the physical environment can be intentionally deployed to facilitate pedestrians while finding their way around, without or with a reduction in language dependent signage; investigates visitor perceptions of their surroundings by indicating what urban elements manifested an impact on the way-finding process. The initial findings support the view that building facades and street features, such as width, could facilitate the decision-making process if strategically employed. However, more importantly, the anticipated features of a specific place construed from a promotional picture can also be misleading and create confusion that may lead to getting lost.

Keywords: pedestrian way-finding, environmental features, urban way-showing, environmental affordance

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Authors: Faruk Ortes, Derya Karabulut, Yunus Ziya Arslan

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Myoelectric features gathering from musculature environment are considered on a preferential basis to perceive muscle activation and control human arm prostheses according to recent experimental researches. EMG (electromyography) signal based human arm prostheses have shown a promising performance in terms of providing basic functional requirements of motions for the amputated people in recent years. However, these assistive devices for neurorehabilitation still have important limitations in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyogram (EMG) is used as the control signal to command such devices. This kind of control consists of activating a motion in prosthetic arm using muscle activation for the same particular motion. Extraction of clear and certain neural information from EMG signals plays a major role especially in fine control of hand prosthesis movements. Many signal processing methods have been utilized for feature extraction from EMG signals. The specific objective of this study was to compare widely used time domain features of EMG signal including integrated EMG(IEMG), root mean square (RMS) and waveform length(WL) for prediction of externally applied forces to human hands. Obtained features were classified using artificial neural networks (ANN) to predict the forces. EMG signals supplied to process were recorded during only type of muscle contraction which is isometric and isotonic one. Experiments were performed by three healthy subjects who are right-handed and in a range of 25-35 year-old aging. EMG signals were collected from muscles of the proximal part of the upper body consisting of: biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically analyzed and merits and pitfalls of the extracted features were discussed with detail. The obtained results are anticipated to contribute classification process of EMG signal and motion control of powered human arm prosthetics control.

Keywords: assistive devices for neurorehabilitation, electromyography, feature extraction, force estimation, human arm prosthesis

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Authors: A. N. Paithane, D. S. Bormane, S. D. Shirbahadurkar

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It has been seen that emotion recognition is an important research topic in the field of Human and computer interface. A novel technique for Feature Extraction (FE) has been presented here, further a new method has been used for human emotion recognition which is based on HHT method. This method is feasible for analyzing the nonlinear and non-stationary signals. Each signal has been decomposed into the IMF using the EMD. These functions are used to extract the features using fission and fusion process. The decomposition technique which we adopt is a new technique for adaptively decomposing signals. In this perspective, we have reported here potential usefulness of EMD based techniques.We evaluated the algorithm on Augsburg University Database; the manually annotated database.

Keywords: intrinsic mode function (IMF), Hilbert-Huang transform (HHT), empirical mode decomposition (EMD), emotion detection, electrocardiogram (ECG)

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Authors: Xiao-Jing Zhu, Liang Zhou, Shi-Zhong Zheng

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Various studies have shown that diallyl trisulfide (DATS) can protect the liver injury, and DATS has a strong antioxidant property. The aim of this study is to evaluate the in vivo role of DATS in protecting the liver against injury and fibrogenesis and further explores the underlying mechanisms. Our results demonstrated that DATS protected the liver from CCl4-caused injury by suppressing the elevation of ALT and AST activities, and by improving the histological architecture of the liver. Treatment with DATS or colchicine improved the liver fibrosis by sirius red staining and immunofluorescence. In addition, immunohistochemistry, western blot, and RT-PCR analyses indicated that DATS inhibited HSC activation. Furthermore, DATS attenuated oxidative stress by increasing glutathione and reducing lipid peroxides and malondialdehyde. These findings suggest that the protective effect of DATS on CCl4-caused liver injury and liver fibrogenesis was, at least partially, attributed to its antioxidant activity.

Keywords: liver fibrogenesis, liver injury, oxidative stress, DATS

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Authors: McKenzie S. Braley, Lesley Jessiman

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If language reveals internal psychological processing, then it is also likely that language use in autobiographical memory transcripts may be used as a window into attachment style and related personality features. The current study, therefore, examined the possible associations between attachment style, negative affectivity, social inhibition, and linguistic features extracted from autobiographical memory transcripts. Young adult participants (n = 61) filled out attachment and personality questionnaires, and orally reported a relationship-related memory. Memories were audio-recorded and later transcribed verbatim. Using a computerized linguistic extraction tool, positive affect words, negative affect words, and cognition words were extracted. Spearman’s rank correlation coefficients revealed that attachment anxiety was negatively correlated with cognition words (r2 = -0.26, p = 0.047) and that negative affectivity was negatively correlated with positive affect words (r2 = -0.32, p = 0.012). The findings suggest that attachment style and personality are associated with speech styles indicative of both emotionality and depth of processing. Because attachment styles, negative affectivity, and social inhibition are associated with poor mental health outcomes, analyses of key linguistics features in autobiographical memory narratives may provide reliable screening tools for mental wellbeing.

Keywords: attachment style, autobiographical memory, language, negative affectivity, social inhibition

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Authors: Ruma Rano, Firoza Sultana, Bishal Bhuyan, Nurul Alam Mazumder

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Fly ash collected from Cachar Paper Mill, Assam, India has been thoroughly characterized in respect of its physico-chemical, morphological and mineralogical features were concerned by using density, LOI, FTIR, XRD, SEM-EDS etc. The results reveal that there is a striking difference in the features and properties of the coarser and finer fractions .The high carbon ash consists of large unburnt carbon (chars), irregular carbonaceous particles in the coarser fraction, which appear to be porous and may be used as domestic fuel. The percentage of char albeit the carbon content decreases with decrease in size of particles. The various fractions essentially contain quartz and mullite as the main mineral phases. For suggesting the potential utilization channels, number of experiments were performed correlating the total characteristic features. Water holding capacities of different size classified fractions were determined, the coarser fractions have unexpectedly higher water holding capacities than the finer ones. An attempt has been made to correlate the results obtained with potential use in agriculture. Another potential application of coarser particles is used as adsorbent for effluents containing waste organic materials. Thus thorough characterization leads to not only a definite direction about the uses of the value added components but also gives useful information regarding the prevailing combustion process.

Keywords: chars, porous, water holding capacity, combustion process

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Authors: Diego F. Carrillo-González, Milena Osorio, Natalia M. Cerro, Yasser Y. Lenis

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Introduction: mortality during the implantation and early embryonic development periods reach around 30% in different mammalian species. It has been described that progesterone (P4) and Arginine (Arg) play a beneficial role in establishing and maintaining early pregnancy in mammals. The combined effect between Arg and P4 on reproductive parameters in the rabbit species is not yet elucidated, to our best knowledge. Objective: to assess the effect of L-arginine and progesterone during the post-breeding period in female rabbits on the composition of the amniotic fluid, the placental structure, and the bone growth in their fetuses. Methods: crossbred female rabbits (n=16) were randomly distributed into four experimental groups (Ctrl, Arg, P4, and Arg+P4). In the control group, 0.9% saline solution was administered as a placebo, the Arg group was administered arginine (50 mg/kg BW) from day 4.5 to day 19 post-breeding, the P4 group was administered progesterone (Gestavec®, 1.5 mg/kg BW) from 24 hours to day 4 post-breeding and for the Arg+P4 group, an administration was performed under the same time and dose guidelines as the Arg and P4 treatments. Four females were sacrificed, and the amniotic fluid was collected and analyzed with rapid urine test strips, while the placenta and fetuses were processed in the laboratory to obtain histological plates. The percentage of deciduous, labyrinthine, and junctional zones was determined, and the length of the femur for each fetus was measured as an indicator of growth. Descriptive statistics were applied to identify the success rates for each of the tests. Afterwards, A one-way analysis of variance (ANOVA) was performed, and a comparison of means was conducted by Tukey's test. Results: a higher density (p<0.05) was observed in the amniotic fluid for fetuses in the control group (1022±2.5g/mL) compared to the P4 (1015±5.3g/mL) and Arg+P4 (1016±4,9g/mL) groups. Additionally, the density of amniotic fluid in the Arg group (1021±2.5g/mL) was higher (p<0.05) than in the P4 group. The concentration of protein, glucose, and ascorbic acid had no statistical difference between treatments (p>0.05). The histological analysis of the uteroplacental regions, a statistical difference (p<0,05) in the proportion of deciduous zone was found between the P4 group (9.6±2.6%) when compared with the Ctrl (28.15±12.3%), and Arg+P4 (26.3±4.9) groups. In the analysis of the fetuses, the weight was higher for the Arg group (2.69±0.18), compared to the other groups (p<0.05), while a shorter length was observed (p<0.05) in the fetuses for the Arg+P4 group (25.97±1.17). However, no difference (p>0.05) was found when comparing the length of the developing femurs between the experimental groups. Conclusion: the combination of L-arginine and progesterone allows a reduction in the density of amniotic fluid, without affecting the protein, energy, and antioxidant components. However, the use of L-arginine stimulates weight gain in fetuses, without affecting size, which could be used to improve production parameters in rabbit production systems. In addition, the modification in the deciduous zone could show a placental adaptation based on the fetal growth process, however more specific studies on the placentation process are required.

Keywords: arginine, progesterone, rabbits, reproduction

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Authors: Shahrokh Barati, Reza Ramezani

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Due to the countries growing attention to the renewable energy producing, the demand for energy from renewable energy has gone up among the renewable energy sources; wind energy is the fastest growth in recent years. In this regard, in order to increase the availability of wind turbines, using of Fault Detection and Isolation (FDI) system is necessary. Wind turbines include of various faults such as sensors fault, actuator faults, network connection fault, mechanical faults and faults in the generator subsystem. Although, sensors and actuators have a large number of faults in wind turbine but have discussed fewer in the literature. Therefore, in this work, we focus our attention to design a sensor and actuator fault detection and isolation algorithm and Fault-tolerant control systems (FTCS) for Wind Turbine. The aim of this research is to propose a comprehensive fault detection and isolation system for sensors and actuators of wind turbine based on data-driven approaches. To achieve this goal, the features of measurable signals in real wind turbine extract in any condition. The next step is the feature selection among the extract in any condition. The next step is the feature selection among the extracted features. Features are selected that led to maximum separation networks that implemented in parallel and results of classifiers fused together. In order to maximize the reliability of decision on fault, the property of fault repeatability is used.

Keywords: FDI, wind turbines, sensors and actuators faults, renewable energy

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Authors: Maassoumeh Bemani Naeini

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Studies on interlanguage have long been engaged in describing the phenomenon of variation in SLA. Pursuing the same goal and particularly addressing the role of linguistic features, this study describes the use of Persian morphology in the interlanguage of two adult English-speaking learners of Persian L2. Taking the general approach of a combination of contrastive analysis, error analysis and interlanguage analysis, this study focuses on the identification and prediction of some possible instances of transfer from English L1 to Persian L2 across six elicitation tasks aiming to investigate whether any of contextual features may variably influence the learners’ order of morpheme accuracy in the areas of copula, possessives, articles, demonstratives, plural form, personal pronouns, and genitive cases.  Results describe the existence of task variation in the interlanguage system of Persian L2 learners.

Keywords: English L1, Interlanguage Analysis, Persian L2, SLA variation

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Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta

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Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.

Keywords: parkinson, gait, feature selection, bat algorithm

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Authors: Lemac Tin

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This paper deals with the lyrical subject of the confessional poetry which is the main part of her stylistic strucuture. We concluded two types of this subject in the classical confessional poetic discourse; reflexive and authentic subject. We offer the model of their genesis, textual features and appeareance realisations. Genesis is related to the theories of deriving poetry from emotion and magic and their similar position in the primitive lyrics and lyrics of the ancient civilizations. Textual features are related to the emotive and semiotic analysis of each type. Appearance realisations of these two types are I-subject, We-subject, transvocal and objectified subject. We check this approaches on some of the poems from World literature.

Keywords: confessional poetry, confessional lyrical subject, magic, emotion, emotive analysis, semiotic analysis

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Authors: Sweta Agrawal, Sachin Chaugule, Gargi Rane, Shashank More, Madhavi Indap

Abstract:

Angiogenesis and metastasis are two of the most important hallmarks of cancer. Hence, most of the cancer therapies nowadays are multi-targeted so as to reduce resistance and have better efficacy. As synthetic molecules arise with a burden of their toxicities and side-effects, more and more research is being focussed on exploiting the vast natural resources of drugs, in the form of plants and animals. Although, the idea of using marine organisms as a source of pharmaceuticals is not new, the pace at which marine drugs are being discovered, has definitely up surged! In the present study, we have assessed the anti-angiogenic and in vitro anti-metastatic activity of aqueous fraction from the extract of marine gastropod Euchelus asper. The soft body of Euchelus Asper was extracted with methanol and named EAME. Partition chromatography of EAME gave three fractions EAME I, II and III. Biochemical analysis revealed the presence of proteins in EAME III. Preliminary analysis had revealed the anti-angiogenic activity was exhibited by EAME III out of the three fractions. Hereafter, EAME III (concentration 25µg/ml-400µg/ml) was tested on chick chorioallantoic membrane (CAM) model for the detailed analysis of its potential anti-angiogenic effect. In vitro testing of the fraction (concentration 0.25µg/ml - 1µg/ml), involved cytotoxicity by SRB assay, cell cycle analysis by flow cytometry and anti-proliferative effect by scratch wound healing assay on A549 lung carcinoma cells. Apart from this, a portion of treated CAM as well as conditioned medium from treated A549 were subjected to gelatin zymography for assessment of matrix metalloproteinases MMP-2 and MMP-9 levels. Our results revealed that EAME III exhibited significant anti-angiogenic activity on CAM which was also supported by histological observations. During histological studies of CAM, it was found that EAME III caused reduction in angiogenesis by altering the extracellular matrix of the CAM membrane. In vitro analysis disclosed that EAME III exhibited moderate cytotoxic effect on A549 cells and its effect was not dose-dependent. The results of flow cytometry confirmed that EAME III caused cell cycle arrest in A549 cell line as almost all of the treated cells were found in G1 phase. Further, the migration and proliferation of A549 was significantly reduced by EAME III as observed from the scratch wound assay. Moreover, Gelatin zymography analysis revealed that EAME III caused suppression of MMP-2 in CAM membrane and reduced MMP-9 and MMP-2 expression in A549 cells. This verified that the anti-angiogenic and anti-metastatic effects of EAME III were correlated with the suppression of MMP-2 and -9. To conclude, EAME III shows dual anti-tumour action by reducing angiogenesis and exerting anti-metastatic effect on lung cancer cells, thus it has the potential to be used as an anti-cancer agent against lung carcinoma.

Keywords: angiogenesis, anti-cancer, marine drugs, matrix metalloproteinases

Procedia PDF Downloads 231

Authors: S. Kherchaoui, A. Houacine

Abstract:

This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

Procedia PDF Downloads 232