Search results for: histological features

Authors: Uma Krishnamoorthy, Vivienne Beavers, Janet Marshall

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Introduction: Cervical cytology showing features raising the suspicion of non cervical glandular neoplasia are reported as code 0 under the United Kingdom National Health Service Cervical screening programme ( NHSCSP). As the suspicion is regarding non cervical neoplasia, smear is reported as normal and patient informed that cervical screening result is normal. GP receives copy of results where it states further referral is indicated in small font within text of report. Background: There were several incidents of delayed diagnosis of endometrial cancer in Lancashire which prompted this Northwest Regional review to enable an understanding of underlying pathology outcome of code zero smears to raise awareness and also to review whether further action on wording of smear results was indicated to prevent such delay. Methodology: All Smears reported at the Manchester cytology centre who process cytology for Lancashire population from March 2013 to March 2014 were reviewed and histological diagnosis outcome of women in whom smear was reported as code zero was reviewed retrospectively . Results: Total smears reported by the cytology centre during this period was approximately 109400. Reports issued with result code 0 among this during this time period was 49.Results revealed that among three fourth (37) of women with code zero smear (N=49), evidence of underlying pathology of non cervical origin was confirmed. Of this, 73 % (36) were due to endometrial pathology with 49 % (24) endometrial carcinoma, 12 % (6)polyp, 4 % atypical endometrial hyperplasia (2), 6 % endometrial hyperplasia without atypia (3), and 2 % adenomyosis (1 case) and 2 % ( 1 case) due to ovarian adenocarcinoma. Conclusion: This review demonstrated that more than half (51 %) of women with a code 0 smear report were diagnosed with underlying carcinoma and 75 % had a confirmed underlying pathology contributory to code 0 smear findings. Recommendations and Action Plan: A local rapid access referral and management pathway for this group of women was implemented as a result of this in our unit. The findings and Pathway were shared with other regional units served by the cytology centre through the Pan Lancashire cervical screening board and through the Cytology centre. Locally, the smear report wording was updated to include a rubber stamp/ print in "Red Bold letters" stating that " URGENT REFERRAL TO GYNAECOLOGY IS INDICATED". Findings were also shared through the Pan Lancashire board with National cervical screening programme board, and revisions to wording of code zero smear reports to highlight the need for Urgent referral has now been agreed at National level to be implemented.

Keywords: code zero smears, endometrial cancer, non cervical glandular neoplasia, ovarian cancer

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Authors: Mohammad Arabi

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The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: Mona Nada, Fahmy Fahmy

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Background: Pleomorphic dermal sarcoma is a rare form of skin cancer affecting cutaneous layer and, in some cases associated with recurrence and metastasis, very commonly to seen in elderly patient affecting the area of head and neck. Pleomorphic dermal sarcoma rises in ultraviolet light exposed areas. The symptoms and severity of this kind of skin cancer varies according to histological factors. The differentiation of Pleomorphic dermal sarcoma needs extensive immunohistochemistry, as the diagnosis depends mainly on exclusion to rule out other malignancy like poorly differentiated squamous cell carcinoma, melanoma, angiosarcoma and leiomyosarcoma. Objective: assessing the management of Pleomorphic dermal sarcoma in our unit and compared to the updated guidelines. Design: Retrospective study Collection of patient data from medical records at countess of Chester plastic surgery unit of the last 5 years, all histologically confirmed Pleomorphic dermal sarcoma (2017-2023). Data were collected confirmed to be Pleomorphic dermal sarcoma were included in the study. The data collected: clinical description of the lesions at first presentation, operation time, multidisciplinary team discussion, plan, referral as well as second operation and investigation done. With comparison of histological examination, immunohistochemistry staining, the excision and rate of recurrence. Results: data collected N19 from (2017-2023) showed the disease predominantly affecting males and the lesion mainly in head and neck, the diagnosis needed extensive immunohistochemistry to differentiate between other malignancy. recurrence present in numbers of the cases which managed after multidisciplinary team discussion either by excision or radiotherapy. Conclusion: Pleomorphic dermal sarcoma is a rare malignancy which needs more understanding and avoid missing as it is aggressive form of skin cancer, there is a chance of metastasis and recurrence which makes it very important to understand the process of development of the cancer and frequent review of the management guidelines.

Keywords: pleomorphic dermal sarcoma, recurrence, radiotherapy, surgical

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Authors: Muhammet Baldan, Emel Timuçin

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Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

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Authors: Samir El-Gendy, Doaa Zaghloul

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The current study dealt with the gross and microscopic anatomy of the integument of male ostrich in addition to the histological features of different areas of skin by light and SEM. The ostrich skin is characterized by prominent feather follicles and bristles. The number of feather follicles was determined per cm2 in different regions. The integument of ostrich had many modifications which appeared as callosities and scales, nail and toe pads. They were sternal, pubic and Achilles tendon callosities. The vacuolated epidermal cells were seen mainly in the skin of legs and to a lesser extent in the skin of back and Achilles areas. Higher lipogenic potential was expressed by epidermis from glabrous areas of ostrich skin. The dermal papillae were found in the skin of feathered area of neck and back and this was not a common finding in bird's skin which may give resistance against shearing forces in these regions of ostrich skin. The thickness of the keratin layer of ostrich varied, being thick and characteristically loose in the skin at legs, very thin and wavy at neck, while at Achilles skin area, scale and toe pad were thick and more compact, with the thickest very dense and wavy keratin layer at the nail. The dermis consisted of superficial layer of dense irregular connective tissue characterized by presence of many vacuoles of different sizes just under the basal lamina of the epithelium of epidermis and deep layer of dense regular connective tissue. This result suggested presence of fat droplets in this layer which may be to overcome the lack of good barrier of cutaneous water loss in epidermis.

Keywords: ostrich, light microscopy, scanning electron microscopy, integument, skin modifications

Procedia PDF Downloads 244

Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

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Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: So-Young Lee, Myoung-Won Oh, Soon-Cheol Eom, Yeon-Won Suh

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Buildings produce more than one thirds of the total energy consumption and CO₂ emissions. Korean government agency pronounced and initiated Zero Energy Buildings policy for construction as of 2025. The net zero energy design features include passive (daylight, layout, materials, insulation, finishes, etc.) and active (renewable energy sources) elements. The Zero Energy House recently built in Nowon-gu, Korea is provided for 121 households as a public rental housing complex. However most of public rental housing did not include sustainable features which can reduce housing maintaining cost significantly including energy cost. It is necessary to implement net zero design features to the obsolete public rental housing during the remodeling procedure since it can reduce housing cost in long term. The purpose of this study is to investigate sustainable design elements implemented in Net Zero Energy House in Korea and passive and active housing design features in order to apply the sustainable features to the case public rental apartment for remodeling. Housing complex cases in this study are Nowan zero Energy house, Gangnam Bogemjari House, and public rental housings built in more than 20 years in Seoul areas. As results, energy consumption in public rental housing built in 5-years can be improved by exterior surfaces. Energy optimizing in case housing built in more than 20 years can be enhanced by renovated materials, insulation, replacement of windows, exterior finishes, lightings, gardening, water, renewable energy installation, Green IT except for sunlight and layout of buildings. Further life costing analysis is needed for energy optimizing for case housing alternatives.

Keywords: affordable housing, remodeling, sustainable design, zero-energy house

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: clusters of microcalcifications, ductal carcinoma in situ, least-square support vector machine, particle swarm optimization

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

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Authors: Dalia Atallah, Lamiaa Ahmed, Hala Zaki, Mahmoud Khattab

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Background: Isoprenaline (ISO) administration induces myocardial damage via oxidative stress and endothelial dysfunction. Atorvastatin (ATV) treatment improves both oxidative stress and endothelial dysfunction yet recent studies have reported a pro-oxidant effect upon ATV administration on both clinical and experimental studies. The present study was directed to investigate the effect of ATV pre-treatment and treatment on ISO-induced myocardial damage. Methods: Male rats were divided into five groups (n = 10). Rats were given ISO (5mg/kg/day, i.p.) for one week with or without ATV (10mg/kg/day, p.o.). ATV was given either as pre-treatment for one week before its co-administration with ISO for another week or as a treatment for two weeks at the end of the ISO administration. At the end of the experiment, the electrocardiographic examination was done and blood was isolated for the estimation of plasma creatine kinase MB (CK-MB) activity. Rats were then sacrificed and the whole ventricles were isolated for histological examination and the estimation of lipid peroxides as malondialdehyde (MDA) level, reduced glutathione (GSH) level, catalase activity, total nitrate-nitrite (NOx), as well as the estimation of both endothelial nitric oxide synthase (eNOS) and inducible nitric oxide synthase (iNOS) protein expression. Results: ISO-induced myocardial damage showed a significant elevation in ST segment, an increase in CK-MB activity, as well as increased oxidative stress biomarkers. Also, ISO-treated rats showed a significant decrease in myocardial NOx level and eNOS as well as degeneration in the myocardium. ATV pre-treatment didn’t show any protection to ISO-treated rats. On the other hand, ATV treatment showed a significant decrease in both the elevated ST wave and CK-MB activity. Moreover, ATV Treatment succeeded to improve oxidative stress biomarkers, tissue NOx, and eNOS protein expression, as well as amelioration of the histological alterations. Conclusion: Pre-treatment with ATV failed to protect against ISO-induced damage. This might suggest a synergistic pro-oxidant effect upon administration of the pro-oxidant ISO along with ATV as demonstrated by the increased oxidative stress and endothelial dysfunction. On the other side, ATV treatment succeeded to significantly improve oxidative stress biomarkers, endothelial dysfunction and myocardial degeneration.

Keywords: atorvastatin, endothelial dysfunction, isoprenaline, oxidative stress

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Authors: Ramy Hammady, Sylvester Arnab

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Significant attention has been directed to adopt game interventions practically to change certain behaviours in many disciplines such as health, education, psychology through many years. That’s due to the intrinsic motivation that games can cause and the substantial impact the games can leave on the player. Many review papers were induced to highlight and measure the effectiveness of the game’s interventions on changing behaviours; however, most of these studies neglected the game design process itself and the game features and elements that can stimuli changing behaviours. Therefore, this paper aims to identify the most game design mechanics and features that are the most influencing on changing behaviour during or after games interventions. This paper also sheds light on the theories of changing behaviours that clearly can led the game design process. This study gives directions to game designers to spot the most influential game features and mechanics for changing behaviour games in order to exploit it on the same manner.

Keywords: behaviour change, game design, serious gaming, gamification, review

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Authors: Amina Aloui, Jalila Ben Salah-Abbès, Abdellah Zinedine, Amar Riba, Noel Durand, Catherine Brabet, Didier Montet, Samir Abbès

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Mycotoxin contamination of food and feed-isa globaconcern, both economically and for public health. Aflatoxin M1 (AFM1) is the principal hydroxylated metabolite of aflatoxin B1. It is frequently found in milk and other dairy products. It is responsible for the development of hepatocellular carcinoma and immunotoxic in humans and animals. The reduction of its bioavailabilitybecomesa great demand in order to protect human and animal health. The use of probiotic bacteria and clay are demonstrated to be able to bind AFM1 in vitro. This study aimed to investigate, in vivo, the activity of two-component mixture: L. rhamnosusGAF06 (LR) and bentonite for reducing the oxidative stress and the histological alterationsinduced by AFM1 in the liver andkidneys. For the experiment, male mice were divided into 7 groups (6 mice/group) and treated, orally, by AFM1, alone or in combination with LR and/or bentonite, for 10 days as follows: group 1 control, group 2 treated with LR alone (2.108 CFU/ml), group 3 treated with bentonite alone (1g/kg), group 4 treated with AFM1 alone (100μg/kg), group 5 co-treated with LR+AFM1, group 6 co-treated with bentonite+AFM1, group 7 co-treated with bentonite+LR+AFM1. At the end of the treatment, the mice were sacrificed, and the livers and kidneys were collected for histological assays. Intracellular antioxidant activities and lipid peroxidation were also studied. The results showed that AFM1causeddamage in liver and kidney tissues, being evidence of hepatotoxicity and nephrotoxicity marked by necrotic cells. It increased the MDA level and decreased the antioxidant enzyme activities (SOD) in both organs. In contrast, the co-treatment with AFM1 plus LR and/or bentonitesignificantly improved the hepatic and renal tissues, regulated kidney, and liver antioxidant enzyme activities. This improvement was more remarkable with the administration of LR-bentonite mixture with AFM1.LR and bentonite alone showed to be safe during the treatment. This mixture can be a promising candidate for future applications in biotechnological processes that aimed to detoxify AFM1in food and feed.

Keywords: aflatoxin M1, bentonite, L. rhamnosus GAF06, oxidative stress, prevention

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Authors: Lavanya Srinivasan

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Biometric identification is to identify unique features in a person like fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat, and case recorded using longwave infrared, short wave infrared, medium wave infrared, and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using YOLO, background subtraction, silhouettes extraction, and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the principal component analysis and recognised using different classifiers. The comparative results with the different classifier show that linear discriminant analysis outperforms other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.

Keywords: biometric, gait, silhouettes, YOLO

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Authors: Lise P. Labéjof, Krisnayne S. Ribeiro, Nicolle P. dos Santos

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An unrecorded experiment of use of the smartphone as a tool for practical classes of histology is presented in this article. Behavior, learning of the students of three science courses at the University were analyzed and compared as well as the mode of teaching of this discipline and the appreciation of the students, using either digital photographs taken by phone or drawings for record microscopic observations, analyze and interpret histological sections of human or animal tissues.

Keywords: cell phone, digital micrographies, learning of sciences, teaching practices

Procedia PDF Downloads 596

Authors: Zin Mar Lwin

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Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods.

Keywords: BCI, EEG, ICA, SVM

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Hassan Sabo

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Language is a system of conventionally spoken, manual and written symbols by human beings that members of a certain social group and participants in its culture express themselves. Communication, expression of identity and imaginative expression are among the functions of language. Dialect is a form of language, or a regional variety of language that is spoken in a particular geographical setting by a particular group of people. Hausa is one of the major languages in Africa, in terms of large number of people for whom it is the first language. Hausa is one of the western Chadic groups of languages. It constitutes one of the five or six branches of Afro-Asiatic family. The predominant Hausa speakers are in Nigeria and they live in different geographical locations which resulted to variety of dialects within the Hausa language apart of the standard Hausa language, the Hausa language has a variety of dialect that distinguish from one another by such features as phonology, grammar and vocabulary. This study intends to examine such features that serve as means of identification among Hausa speakers who are set off from others, geographically or socially.

Keywords: dialect, features, geographical location, Hausa language

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Authors: Eric Lacoste

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Laboratory animals are currently widely used as a model of human pathologies in dermatology such as atopic dermatitis (AD). These models provide a better understanding of the pathophysiology of this complex and multifactorial disease, the discovery of potential new therapeutic targets and the testing of the efficacy of new therapeutics. However, confirmation of the correct development of AD is mainly based on histology from skin biopsies requiring invasive surgery or euthanasia of the animals, plus slicing and staining protocols. However, there are currently accessible imaging technologies such as Optical Coherence Tomography (OCT), which allows non-invasive visualization of the main histological structures of the skin (like stratum corneum, epidermis, and dermis) and assessment of the dynamics of the pathology or efficacy of new treatments. Briefly, female immunocompetent hairless mice (SKH1 strain) were sensitized and challenged topically on back and ears for about 4 weeks. Back skin and ears thickness were measured using calliper at 3 occasions per week in complement to a macroscopic evaluation of atopic dermatitis lesions on back: erythema, scaling and excoriations scoring. In addition, OCT was performed on the back and ears of animals. OCT allows a virtual in-depth section (tomography) of the imaged organ to be made using a laser, a camera and image processing software allowing fast, non-contact and non-denaturing acquisitions of the explored tissues. To perform the imaging sessions, the animals were anesthetized with isoflurane, placed on a support under the OCT for a total examination time of 5 to 10 minutes. The results show a good correlation of the OCT technique with classical HES histology for skin lesions structures such as hyperkeratosis, epidermal hyperplasia, and dermis thickness. This OCT imaging technique can, therefore, be used in live animals at different times for longitudinal evaluation by repeated measurements of lesions in the same animals, in addition to the classical histological evaluation. Furthermore, this original imaging technique speeds up research protocols, reduces the number of animals and refines the use of the laboratory animal.

Keywords: atopic dermatitis, mouse model, oxzolone model, histology, imaging

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Authors: Rishabh Beri, Sahil Shah

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We have applied a range of filters and processing in order to extract out the various features of the football game, like the field lines of a football field. Another important aspect was the detection of the players in the field and tagging them according to their teams distinguished by their jersey colours. This extracted information combined about the players and field helped us to create a virtual field that consists of the playing field and the players mapped to their locations in it.

Keywords: Detect, Football, Players, Virtual

Procedia PDF Downloads 331

Authors: Diego R. Boiarski, Camila M. Toigo, Thais M. Sobjak, Andrey F. P. Santos, Silvia Romao, Ana T. B. Guimaraes

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Anthropogenic activities promote changes in the community’s structures and decrease the species abundance of amphibians. Biological communities of fluvial systems are assemblies of organisms that have adapted to regional conditions, including the physical environment and food resources, and are further refined through interactions with other species. The aim of this study was to assess neurotoxic alterations and in the antioxidant system on tadpoles of Lithobates catesbeianus exposed to waters from Cascavel River, in the south of Brazil. A total of 420 L of water was collected from the Cascavel River, 140 L from each of the three different locations: Site 1 – headwater; Site 2 – stretch of the stream that runs through an urbanized area; Site 3 – a stretch from the rural area. Twelve tadpoles were acclimated in each aquarium (100 L of water) for seven days. The water from each aquarium was replaced with the ones sampled from the river, except the one from the control aquarium. After seven days, a portion of the liver was removed and conditioned for ChE, SOD, CAT and LPO analysis; other part of the tissue was conditioned for histological analysis. The statistical analysis performed was one-way ANOVA, followed by post-hoc Tukey-HSD test, and the multivariate principal components analysis. It was not observed any neurotoxic effect, but a slight increase in SOD activity and elevation of CAT activity in both urban and rural environment. A decrease in LPO reaction was detected, mainly among the tadpoles exposed to the waters from the rural area. The results of the present study demonstrate the alteration of the antioxidant system, as well as liver histopathologies in tadpoles exposed mainly to waters collected in urban and rural environments. These alterations may cause the reduction in the velocity of the metamorphosis process from the tadpoles. Further, were observed histological alterations, highlighting necrotic areas mainly among the animals exposed to urban waters. Those damages can lead to metabolic dysfunction, interfering with survival capacity, diminishing not only individual fitness but for the whole population. In the interpretation synthesis of all biomarkers, the cellular damage gradient is perceptible, characterized by the variables related to the antioxidant system, due to the flow direction of the stream. This result is indicative that along the course of the creek occurs dumping of organic material, which promoted an acute response upon tadpoles of L. catesbeianus. and it was also observed the difference in tissue damage between the experimental groups and the control group, the latter presenting histological alterations, but to a lesser degree than the animals exposed to the waters of the Cascavel river. These damages, caused by reactive oxygen species possibly resulting from the contamination by organic compounds, can lead the animals to a series of metabolic dysfunctions, interfering with its metamorphosis capacity. Interruption of metamorphosis may affect survival, which may impair its growth, development and reproduction, diminishing not only the fitness of each individual but in a long-term, to the entire population.

Keywords: American bullfrog, histopathology, oxidative stress, urban creeks pollution

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Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

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Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: climate, degradation, HVAC, neighborhood component analysis

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Authors: Bin Liu

Abstract:

Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: Ebenezer Obaloluwa Olaniyi

Abstract:

In this paper, an intelligent system has been designed to grade apple based on either its defective or healthy for production in food processing. This paper is segmented into two different phase. In the first phase, the image processing techniques were employed to extract the necessary features required in the apple. These techniques include grayscale conversion, segmentation where a threshold value is chosen to separate the foreground of the images from the background. Then edge detection was also employed to bring out the features in the images. These extracted features were then fed into the neural network in the second phase of the paper. The second phase is a classification phase where neural network employed to classify the defective apple from the healthy apple. In this phase, the network was trained with back propagation and tested with feed forward network. The recognition rate obtained from our system shows that our system is more accurate and faster as compared with previous work.

Keywords: image processing, neural network, apple, intelligent system

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Authors: Yanjun Chen, Chuanping Hu, Jie Shao, Lin Mei, Chongyang Zhang

Abstract:

Traditional image classification is based on encoding scheme (e.g. Fisher Vector, Vector of Locally Aggregated Descriptor) with low-level image features (e.g. SIFT, HoG). Compared to these low-level local features, deep convolutional features obtained at the mid-level layer of convolutional neural networks (CNN) have richer information but lack of geometric invariance. For scene classification, there are scattered objects with different size, category, layout, number and so on. It is crucial to find the distinctive objects in scene as well as their co-occurrence relationship. In this paper, we propose a method to take advantage of both deep convolutional features and the traditional encoding scheme while taking object-centric and scene-centric information into consideration. First, to exploit the object-centric and scene-centric information, two CNNs that trained on ImageNet and Places dataset separately are used as the pre-trained models to extract deep convolutional features at multiple scales. This produces dense local activations. By analyzing the performance of different CNNs at multiple scales, it is found that each CNN works better in different scale ranges. A scale-wise CNN adaption is reasonable since objects in scene are at its own specific scale. Second, a fisher kernel is applied to aggregate a global representation at each scale and then to merge into a single vector by using a post-processing method called scale-wise normalization. The essence of Fisher Vector lies on the accumulation of the first and second order differences. Hence, the scale-wise normalization followed by average pooling would balance the influence of each scale since different amount of features are extracted. Third, the Fisher vector representation based on the deep convolutional features is followed by a linear Supported Vector Machine, which is a simple yet efficient way to classify the scene categories. Experimental results show that the scale-specific feature extraction and normalization with CNNs trained on object-centric and scene-centric datasets can boost the results from 74.03% up to 79.43% on MIT Indoor67 when only two scales are used (compared to results at single scale). The result is comparable to state-of-art performance which proves that the representation can be applied to other visual recognition tasks.

Keywords: deep convolutional features, Fisher Vector, multiple scales, scale-specific normalization

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Authors: Yong Zhao, Jian He, Cheng Zhang

Abstract:

Cardiovascular diseases caused by hypertension are extremely threatening to human health, and early diagnosis of hypertension can save a large number of lives. Traditional hypertension detection methods require special equipment and are difficult to detect continuous blood pressure changes. In this regard, this paper first analyzes the principle of heart rate variability (HRV) and introduces sliding window and power spectral density (PSD) to analyze the time domain features and frequency domain features of HRV, and secondly, designs an HRV-based hypertension prediction network by combining Resnet, attention mechanism, and multilayer perceptron, which extracts the frequency domain through the improved ResNet18 features through a modified ResNet18, its fusion with time-domain features through an attention mechanism, and the auxiliary prediction of hypertension through a multilayer perceptron. Finally, the network was trained and tested using the publicly available SHAREE dataset on PhysioNet, and the test results showed that this network achieved 92.06% prediction accuracy for hypertension and outperformed K Near Neighbor(KNN), Bayes, Logistic, and traditional Convolutional Neural Network(CNN) models in prediction performance.

Keywords: feature extraction, heart rate variability, hypertension, residual networks

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Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

Abstract:

A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

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Authors: Nengi A. H. Ejiobih

Abstract:

Conflict is one of the defining features of human societies. Often, the use or misuse of language in interaction is the genesis of conflict. As such, it is expected that when people use language they do so in socially determined ways and with almost predictable social effects. The objective of this paper was to examine the interest at work as manifested in language choice and collocations in conflict discourse. It also scrutinized the implications of linguistic features in conflict discourse as it concerns ideology and power relations in political discourse in Nigeria. The methodology used for this paper is an approach from Critical discourse analysis because of its multidisciplinary model of analysis, linguistic features and its implications were analysed. The datum used is a text from the Sunday Sun Newspaper in Nigeria, West Africa titled Akunyili Memo: State of the Nation. Some of the findings include; different ideologies are inherent in conflict discourse, there is the presence of power relations being produced, exercised, maintained and produced throughout the discourse and the use of pronouns in conflict discourse is valuable because it is used to initiate and maintain relationships in social context. This paper has provided evidence that, taking into consideration the nature of the social actions and the way these activities are translated into languages, the meanings people convey by their words are identified by their immediate social, political and historical conditions.

Keywords: conflicts, discourse, language, linguistic features, social context

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