Search results for: GLCM texture features

Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

Abstract:

Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Soumya Sajjan

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Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

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Authors: Kaoutar Ben Azzou, Hanaa Talei

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Human resources is an important department in each organization as it manages the life cycle of employees from recruitment training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best-fit candidates from all applicants. Matching the best profile for a job position requires a manual way of looking at many CVs, which requires hours of work that can sometimes lead to choosing not the best profile. The work presented in this paper aims at reducing the workload of HR personnel by automating the preliminary stages of the candidate screening process, thereby fostering a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide the most fitting job profile for each candidate. Our work employs natural language processing (NLP) techniques to identify and extract key features from unstructured text extracted from a CV, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.

Keywords: automated recruitment, candidate screening, machine learning, human resources management

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Authors: Lucie Speyer, Vincent Lecocq, Séverine Humbert, Antoine Hugon

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Mesoporous alumina is commonly used as a catalyst support for the hydrotreating of heavy petroleum cuts. The process of fabrication usually involves: the synthesis of the boehmite AlOOH precursor, a kneading-extrusion step, and a calcination in order to obtain the final alumina extrudates. Alumina is described as a complex porous medium, generally agglomerates constituted of aggregated nanocrystallites. Its porous texture directly influences the active phase deposition and mass transfer, and the catalytic properties. Then, it is easy to figure out that each step of the fabrication of the supports has a role on the building of their porous network, and has to be well understood to optimize the process. The synthesis of boehmite by precipitation of aluminum salts was extensively studied in the literature and the effect of various parameters, such as temperature or pH, are known to influence the size and shape of the crystallites and the specific surface area of the support. The calcination step, through the topotactic transition from boehmite to alumina, determines the final properties of the support and can tune the surface area, pore volume and pore diameters from those of boehmite. However, the kneading extrusion step has been subject to a very few studies. It generally consists in two steps: an acid, then a basic kneading, where the boehmite powder is introduced in a mixer and successively added with an acid and a base solution to form an extrudable paste. During the acid kneading, the induced positive charges on the hydroxyl surface groups of boehmite create an electrostatic repulsion which tends to separate the aggregates and even, following the conditions, the crystallites. The basic kneading, by reducing the surface charges, leads to a flocculation phenomenon and can control the reforming of the overall structure. The separation and reassembling of the particles constituting the boehmite paste have a quite obvious influence on the textural properties of the material. In this work, we are focused on the influence of the kneading step on the alumina catalysts supports. Starting from an industrial boehmite, extrudates are prepared using various kneading conditions. The samples are studied by nitrogen physisorption in order to analyze the evolution of the textural properties, and by synchrotron small-angle X-ray scattering (SAXS), a more original method which brings information about agglomeration and aggregation of the samples. The coupling of physisorption and SAXS enables a precise description of the samples, as same as an accurate monitoring of their evolution as a function of the kneading conditions. These ones are found to have a strong influence of the pore volume and pore size distribution of the supports. A mechanism of evolution of the texture during the kneading step is proposed and could be attractive in order to optimize the texture of the supports and then, their catalytic performances.

Keywords: alumina catalyst support, kneading, nitrogen physisorption, small-angle X-ray scattering

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Authors: Yaya Gao, Jifeng Liu, Yafeng Liu

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Objectives: This study aimed to improve clinicians' understanding and diagnosis of the Mammary analogue secretory carcinoma of the salivary gland（MASC）. Methods: The clinical features of a MASC patient who was admitted to WestChina Hospital of Sichuan University in July 2020 were reviewed and analyzed. A 49-year-old woman with left upper neck pain for three months was admitted to the hospital. She underwent adenoma resection of the left submandibular gland 14 years ago and mucoepidermoid carcinoma resection surgery five years ago. Three months before admission, the patient developed pain in the left mandibular angle after "fatigue" and gradually developed radiation pain in the left ear, which could be relieved after rest. A mass of 1cm could be touched at the mandibular, with tenderness, poor mobility, and hard texture. No swelling, heat, pain, rupture, or pus was found on the surrounding skin. Color doppler ultrasonography of the salivary gland indicated a weak echo mass of 23*14*17mm in the left parotid gland. Results: Surgical excision was completed. Immunohistochemistry of the tumor samples after operation showed that P63（a few，+）, CK7（+）, S100（+）, DOG1（-）, Ki67（MIB-1）（+，5%），pan-TRK（+）, PAS(+) . ETV-6 gene translocation was detected in FISH in postoperative pathology, which indicated MASC. After this diagnosis, the patient sent the postoperative specimen of the second submandibular tumor to our hospital for consultation. The morphology of the two was similar. FISH detected ETV-6 gene translocation, so the second pathological diagnosis was revised to MASC. Conclusion: MASC of the salivary gland is a rare salivary gland tumor whose diagnosis depends on the result of the ETV6-NTRK3 fusion gene.

Keywords: mammary analogue secretory carcinoma, ETV6-NTRK3, salivary gland, misdiagnosed

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Authors: Snehal G. Teli, R. J. Shelke

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CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance.

Keywords: convolutional neural network, image enhancement, machine learning, multiunet, underwater images

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Authors: Mai Ashraf Talaat

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Bullying is an aggressive behavior and a serious issue that should be addressed by everyone and should be avoided at all costs. It is very common among adolescents and schoolchildren and the effects can be devastating and long-lasting. Students are most commonly bullied about physical appearance, race, gender, disability, ethnicity, religion, and sexual orientation. Appearance-targeted bullying is a form of bullying that targets an aspect of a person's appearance, which includes facial and dental features. Deviation from accepted dentofacial aesthetics leads to elevated incidences of bullying in schoolchildren. The aim of this review article is to assess the prevalence of bullying due to dentofacial characteristics and evaluate the importance of dentofacial appearance on perceived social attractiveness based on multiple studies.

Keywords: dentofacial features, orthodontics, malocclusion, adolescents, bullying

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Authors: Reza Gheisari, Paulo J. Bartolo, Nicholas Goddard

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Polymeric micro-cantilevers (Cs) are rapidly becoming popular for MEMS applications such as chemo- and bio-sensing as well as purely electromechanical applications such as microrelays. Polymer materials present suitable physical and chemical properties combined with low-cost mass production. Hence, micro-cantilevers made of polymers indicate much more biocompatibility and adaptability of rapid prototyping along with mechanical properties. This research studies the effects of three process and one size factors on the filling behaviour in micro cavity, and the role of each in the replication of micro parts using different polymer materials i.e. polypropylene (PP) SABIC 56M10 and acrylonitrile butadiene styrene (ABS) Magnum 8434. In particular, the following factors are considered: barrel temperature, mould temperature, injection speed and the thickness of micro features. The study revealed that the barrel temperature and the injection speed are the key factors affecting the flow length of micro features replicated in PP and ABS. For both materials, an increase of feature sizes improves the melt flow. However, the melt fill of micro features does not increase linearly with the increase of their thickness.

Keywords: flow length, micro cantilevers, micro injection moulding, microfabrication

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Authors: Dennis Infante

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This paper examined the reflections of teachers on curriculum reforms, the implementation of the K-12 Basic Education Program in the Philippines. The results revealed that problems and concerns raised by teachers could be classified into curriculum materials and design; competence, readiness and motivation of the teachers; the learning environment, and support systems; readiness, competence and motivation of students; and other relevant factors. The best features of the K-12 curriculum reforms included (1) the components, curriculum materials; (2) the design, structure and delivery of the lessons; (3) the framework and theoretical approach; (3) the qualities of the teaching-learning activities; (4) and other relevant features. With the demanding task of implementing the new curriculum, the teachers expressed their needs which included (1) making the curriculum materials available to achieve the goals of the curriculum reforms; (2) enrichment of the learning environments; (3) motivating and encouraging the teachers to embrace change; (4) providing appropriate support systems; (5) re-tooling, and empowering teachers to implement the curriculum reforms; and (6) other relevant factors. The research concluded with a synthesis that provided a paradigm for implementing curriculum reforms which recognizes the needs of the teachers and the features of the new curriculum.

Keywords: curriculum reforms, K-12, teachers' reflections, implementing curriculum change

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Authors: Tingting Chen, Shouliang Zhao

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Compared with other regions, the construction for villages and towns in regions inhabited by minority nationality shall be deeply rooted in natural and cultural endowment in locality, and more importance shall be attached to building of characteristics. In this kind of area, landscape design is very important for its character and tradition. By empirical study in Shennongjia Area, some findings could be summarized as below. There are unique natural and cultural resources in Shennongjia Forestry District; during transformation on style and features of Tujia Village, Xiaguping, special style and features have been successfully shaped through 4 strategies: (1) highlighting Tujia Culture and architectural style in west region of Hubei Province; (2) merging with local natural environment; (3) introducing system of rural coordination architect; and (4) making great efforts to design and construct environmental embellishments with village and town symbols.

Keywords: rural coordination architect, special style and features, characteristic landscape, villages and towns in regions inhabited by minority nationality

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Authors: Seham El Kareh, Miramar Etman

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Recently, detecting liars and extracting features which distinguish them from truth-tellers have been the focus of a wide range of disciplines. To the author’s best knowledge, most of the work has been done on facial expressions and body gestures but only few works have been done on the language used by both liars and truth-tellers. This paper sheds light on four axes. The first axis copes with building an audio corpus for deceptive and truthful speech for Egyptian Arabic speakers. The second axis focuses on examining the human perception of lies and proving our need for computational linguistic-based methods to extract features which characterize truthful and deceptive speech. The third axis is concerned with building a linguistic analysis program that could extract from the corpus the inter- and intra-linguistic cues for deceptive and truthful speech. The program built here is based on selected categories from the Linguistic Inquiry and Word Count program. Our results demonstrated that Egyptian Arabic speakers on one hand preferred to use first-person pronouns and present tense compared to the past tense when lying and their lies lacked of second-person pronouns, and on the other hand, when telling the truth, they preferred to use the verbs related to motion and the nouns related to time. The results also showed that there is a need for bigger data to prove the significance of words related to emotions and numbers.

Keywords: Egyptian Arabic corpus, computational analysis, deceptive features, forensic linguistics, human perception, truthful features

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Authors: Mounir Azzam, Valerie Graw, Andreas Rienow

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The Syrian war, which commenced in 2011, has resulted in significant changes in the real estate market in the Damascus metropolitan area, with rising levels of insecurity and disputes over tenure rights. The quest for spatial justice is, therefore, imperative, and this study performs a spatiotemporal analysis to investigate the impact of the war on real estate differentiation. Using the hedonic price models including 2,411 housing transactions over the period 2010-2022, this study aims to understand the spatial dynamics of the real estate market in wartime. Our findings indicate that war variables have had a significant impact on the differentiation and depreciation of property prices. Notably, property attributes have a more substantial impact on real estate values than district location, with severely damaged buildings in Damascus city resulting in an 89% decline in prices, while prices in Rural Damascus districts have decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google Earth Engine. We monitored 250 samples from hedonic datasets within three different years of the Syrian war (2015, 2019, and 2022). Our findings show that correlation values were highly differentiated, particularly among Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall between 2015 and 2019, they improved slightly after 2019. The findings have valuable implications, not only for investment prospects in setting up a successful reconstruction strategy but also for spatial justice of property rights in strongly encouraging sustainable real estate development.

Keywords: hedonic price, real estate differentiation, reconstruction strategy, spatial justice, urban texture analysis

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Authors: Komathy Thiagarajan, Mohd. Azizuddin Mohd. Yussof, Hasnoorina Husin, Noor Azreena Abd Aziz, Faezah Shekh Abdullah, Abdul Wahaf Abdul Wahid

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Polycystic Ovary Syndrome (PCOS) presents with a plethora of clinical features owing to the multifaceted underlying pathophysiology. This study was conducted to determine the clinical features unique to the sub fertile women attending the Sub fertility Clinic of the National Population and Family Development Board (NPFDB) so that a more holistic approach can be adopted to further enhance the pregnancy outcome in those women. This was a case-control study conducted over a span of three years (from January 2014 until December 2016), whereby women who fulfilled the Rotterdam Criteria 2004 were classified as PCOS (n=79) and women who did not fulfill the Rotterdam Criteria were classified as controls (n=88). The mean age of the women was 30.1 years and the mean duration of marriage was 3.93 years. The majority of women suffered from primary sub fertility (82.6%). The median age was lower among PCOS women (29.0 years) compared to the controls (30.0 years), p<0.05. The majority of PCOS women (43.0%) were obese (BMI > 30 kg/m2) compared to only 19.3% who were obese in the control group, p<0.05. Hypertension was present in 59.5% of PCOS women and only in 36.4% of the control group, p<0.05. There were significantly more women who presented with hirsutism in PCOS group (27.8%) as compared to the control group (5.7%), p<0.05. The findings of this study elucidate that the clinical features of significance among sub fertile women suffering from PCOS, if detected early, are amenable to lifestyle modifications and timely interventions can potentially improve the fertility outcomes in this group of women.

Keywords: clinical features, fertility, lifestyle modification, PCOS

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process.

Keywords: Quran, speaker recognition, reciter recognition, Gaussian Mixture Model

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Authors: Munaf Rashid

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For content-based human action retrieval systems, search accuracy is often inferior because of the following two reasons 1) global information pertaining to videos is totally ignored, only low level motion descriptors are considered as a significant feature to match the similarity between query and database videos, and 2) the semantic gap between the high level user concept and low level visual features. Hence, in this paper, we propose a method that will address these two issues and in doing so, this paper contributes in two ways. Firstly, we introduce a method that uses both global and local information in one framework for an action retrieval task. Secondly, to minimize the semantic gap, a user concept is involved by incorporating features weight updating (FWU) Relevance Feedback (RF) approach. We use statistical characteristics to dynamically update weights of the feature descriptors so that after every RF iteration feature space is modified accordingly. For testing and validation purpose two human action recognition datasets have been utilized, namely Weizmann and UCF. Results show that even with a number of visual challenges the proposed approach performs well.

Keywords: relevance feedback (RF), action retrieval, semantic gap, feature descriptor, codebook

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Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu

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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.

Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness

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Authors: Radostina Popova

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The paper presents an analysis of the innovation performance of small and medium-sized furniture enterprises in Bulgaria, accounting for over 97% of the companies in the sector. It contains advanced features of innovation in enterprises, specific features of the furniture industry in Bulgaria and analysis of the results of studies on the topic. The results from studies of three successive periods - 2006-2008; 2008-2010; 2010-2012, during which were studied 594 small and medium-sized furniture enterprises. There are commonly used in the EU definitions and indicators (European Commission, OECD, Oslo Manual), which allows for the comparability of results.

Keywords: innovation activity, competitiveness of innovation, furniture enterprises in Bulgaria

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Authors: H. Aripin, I. Made Joni, Edvin Priatna, Nundang Busaeri, Svilen Sabchevski

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In this investigation, manganese has been produced from medium grade manganese ore from Karangnunggal mine (West Java, Indonesia). The ores were grinded using a jar mill to pass through a 150 mesh sieve. The effects of keeping it at a temperature of 1200 °C in methane gas on the structural properties have been studied. The material’s properties have been characterized on the basis of the experimental data obtained using X-ray fluorescence (XRF), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Fourier transform infrared (FTIR) spectroscopy. It has been found that the ore contains MnO₂ as the main constituents at about 46.80 wt.%. It can be also observed that the ore particles are agglomerated forming dense grains with different texture and morphology. The irregular-shaped grains with dark contrast, the large brighter grains, and smaller grains with bright texture and smooth surfaces are associated with the presence of manganese, calcium, and quartz, respectively. From XRD patterns, MnO₂ is reduced to hausmannite (Mn₃O₄), manganosite (MnO) and manganese carbide (Mn₇C₃). At a temperature of 1200°C the keeping time does not have any effect on the formation of crystals and the crystalline phases remain almost unchanged in the time range from 15 to 90 minutes. An increase of the keeping time up to 45 minutes during the sintering process leads to an increase of the MnO concentration, while at 90 minutes, the concentration decreases. At longer keeping times the excess reaction of the methane gas and manganese oxide in the ore causes an increase of carbon deposition. As a result, it blocks the particle surface and then hinders the reduction process of manganese oxide. From FTIR spectrum allows one to explain that the appearance of C=O stretching mode arises from absorption of atmospheric methane and manganese oxide of the ore. The intensity of this band increases with increasing the keeping time, indicating an increase of carbon deposition on the surface of manganese oxide.

Keywords: manganese, medium grade manganese ore, structural properties, keeping the temperature, carbon deposition

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Baha Hasan, Jan Wikander

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This paper describes a part of the integrating work between assembly design and assembly process planning domains (APP). The work is based, in its first stage, on modelling assembly features to support APP. A multi-layer architecture, based on feature-based modelling, is proposed to establish a dynamic and adaptable link between product design using CAD tools and APP. The proposed approach is based on deriving “specific function” features from the “generic” assembly and form features extracted from the CAD tools. A hierarchal structure from “generic” to “specific” and from “high level geometrical entities” to “low level geometrical entities” is proposed in order to integrate geometrical and assembly data extracted from geometrical and assembly modelers to the required processes and resources in APP. The feature concept, feature-based modelling, and feature recognition techniques are reviewed.

Keywords: assembly feature, assembly process planning, feature, feature-based modelling, form feature, ontology

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Authors: Rady A., Elsheshai A., Elsawy M., Nagui R.

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Background and Aim: Metabolic syndrome affect around 20% of general population , authors have incriminated antipsychotics as serious risk factor that may provoke such derangement. The aim of our study is to assess metabolic syndrome in patients presenting psychotic features (delusions and hallucinations) whether schizophrenia or mood disorder and compare results in terms of drug naïf, on medication and healthy control. Subjects and Methods: The study recruited 40 schizophrenic patients, half of them drug naïf and the other half on antipsychotics, 40 patients with mood disorder with psychotic features, half of them drug naïf and the other half on medication, 20 healthy control. Exclusion criteria were put in order to exclude patients having already endocrine or metabolic disorders that my interfere with results obtain to minimize confusion bias. Metabolic syndrome assessed by measuring parameters including weight, body mass index, waist circumference, triglyceride level, HDL, fasting glucose, fasting insulin and insulin resistance Results: No difference was found when comparing drug naïf to those on medication in both schizophrenic and psychotic mood disorder arms, schizophrenic patients whether on medication or drug naïf should difference with control group for fasting glucose, schizophrenic patients on medication also showed difference in insulin resistance compared to control group. On the other hand, patients with psychotic mood disorder whether drug naïf or on medication showed difference from control group for fasting insulin level. Those on medication also differed from control for insulin resistance Conclusion: Our study didn’t reveal difference in metabolic syndrome among patients with psychotic features whether on medication or drug naïf. Only patients with Psychotic features on medication showed insulin resistance. Schizophrenic patients drug naïf or on medication tend to show higher fasting glucose while psychotic mood disorder whether drug naïf or on medication tend to show higher fasting insulin. This study suggest that presence of psychotic features themselves regardless being on medication or not carries a risk for insulin resistance and metabolic syndrome. Limitation: This study is limited by number of participants and larger numbers in future studies should be included in order to extrapolate results. Cohort longitudinal studies are needed in order to evaluate such hypothesis.

Keywords: schizophrenia, metabolic syndrome, psychosis, insulin, resistance

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Authors: Shao Bo Cheng, Yong-Jin Han, Se Young Park, Seong-Bae Park

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Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like e-bay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C e-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C e-commerce.

Keywords: spam keyword, e-commerce, keyword features, spam filtering

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Authors: Marie Madeleine Nanga Ndjang, Julie Mathilde Klang, Edwin M. Mmutlane, Derek Tantoh Ndinteh, Eugenie Kayitesi, Francois Ngoufack Zambou

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Gluten intolerance and the unavailability of wheat flour in some parts of the world have led to the development of gluten-free bread. However, gluten-free bread generally results in a low specific volume, and to remedy this, the use of hydrocolloids and bases has proved to be very successful. Thus, the present study aims to determine the optimal proportions of gum extract of Triumffetapentendraand sodium bicarbonate in breadmaking of a composite flour based on sour cassava starch, peanut, and cowpea flour. To achieve this, a BoxBenkhendesign was used, the variable being the amount of extract gums, the amount of bicarbonate, and the amount of water. The responses evaluated were the specific volume and texture properties (Hardness, Cohesiveness, Consistency, Elasticity, and Masticability). The specific volume was done according to standard methods of AACC and the textural properties by a texture analyzer. It appears from this analysis that the specific volume is positively influenced by the incorporation of extract gums, bicarbonate, and water. The hardness, consistency, and plasticity increased with the incorporation rate of extract gums but reduced with the incorporation rate of bicarbonate and water. On the other hand, Cohesion and elasticity increased with the incorporation rate of bicarbonate and water but reduced with the incorporation of extract gum. The optimate proportions of extract gum, bicarbonate, and water are 0.28;1.99, and 112.5, respectively. This results in a specific volume of 1.51; a hardness of 38.51; a cohesiveness of 0.88; a consistency of 32.86; an elasticity of 5.57, and amasticability of 162.35. Thus, this analysis suggests that gum extracts and sodium bicarbonate can be used to improve the quality of gluten-free bread.

Keywords: box benkhen design, bread-making, gums, textures properties, specific volume

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Authors: Ayat Adnan Atwah, Muhammad A. Khan

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Self-cleaning surfaces are getting significant attention in industrial fields. Especially for textile fabrics, it is observed that self-cleaning textile fabric surfaces are created by manipulating the surface features with the help of coatings and nanoparticles, which are considered costly and far more complicated. However, controlling the fabrication parameters of textile fabrics at the microscopic level by exploring the potential for self-cleaning has not been addressed. This study aimed to establish the context of self-cleaning textile fabrics by controlling the fabrication parameters of the textile fabric at the microscopic level. Therefore, 3D-printed textile fabrics were fabricated using the low-cost fused filament fabrication (FFF) technique. The printing parameters, such as orientation angle (O), layer height (LH), and extruder width (EW), were used to control the microscopic features of the printed fabrics. The combination of three printing parameters was created to provide the best self-cleaning textile fabric surface: (LH) (0.15, 0.13, 0.10 mm) and (EW) (0.5, 0.4, 0.3 mm) along with two different (O) of (45º and 90º). Three different thermoplastic flexible filament materials were used: (TPU 98A), (TPE felaflex), and (TPC flex45). The printing parameters were optimised to get the optimum self-cleaning ability of the printed specimens. Furthermore, the impact of these characteristics on mechanical strength at the fabric-woven structure level was investigated. The study revealed that the printing parameters significantly affect the self-cleaning properties after adjusting the selected combination of layer height, extruder width, and printing orientation. A linear regression model was effectively developed to demonstrate the association between 3D printing parameters (layer height, extruder width, and orientation). According to the experimental results, (TPE felaflex) has a better self-cleaning ability than the other two materials.

Keywords: 3D printing, self-cleaning fabric, microscopic features, printing parameters, fabrication

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

Abstract:

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

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

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Authors: Shao-Tzu Huang, Chen-Chien Hsu, Wei-Yen Wang

Abstract:

Matching high dimensional features between images is computationally expensive for exhaustive search approaches in computer vision. Although the dimension of the feature can be degraded by simplifying the prior knowledge of homography, matching accuracy may degrade as a tradeoff. In this paper, we present a feature matching method based on k-means algorithm that reduces the matching cost and matches the features between images instead of using a simplified geometric assumption. Experimental results show that the proposed method outperforms the previous linear exhaustive search approaches in terms of the inlier ratio of matched pairs.

Keywords: feature matching, k-means clustering, SIFT, RANSAC

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Authors: A: Annis Fathima, V. Vaidehi, S. Ajitha

Abstract:

Face recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-Nearest Neighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach.

Keywords: face recognition, Gabor wavelet, LDA, k-NN classifier

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Authors: Seyed Nima Tayarani Bathaie

Abstract:

In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: face detection algorithm, Haar features, security of ATM

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Authors: Bjorn-Ole Meyer, Dominik Marti, Peter E. Andersen

Abstract:

A multimodal imaging system, combining spectrally resolved multiphoton microscopy (MPM) and optical coherence microscopy (OCM) is demonstrated. MPM and OCM are commonly integrated into multimodal imaging platforms to combine functional and morphological information. The MPM signals, such as two-photon fluorescence emission (TPFE) and signals created by second harmonic generation (SHG) are biomarkers which exhibit information on functional biological features such as the ratio of pyridine nucleotide (NAD(P)H) and flavin adenine dinucleotide (FAD) in the classification of cancerous tissue. While the spectrally resolved imaging allows for the study of biomarkers, using a spectrometer as a detector limits the imaging speed of the system significantly. To overcome those limitations, an OCM setup was added to the system, which allows for fast acquisition of structural information. Thus, after rapid imaging of larger specimens, navigation within the sample is possible. Subsequently, distinct features can be selected for further investigation using MPM. Additionally, by probing a different contrast, complementary information is obtained, and different biomarkers can be investigated. OCM images of tissue and cell samples are obtained, and distinctive features are evaluated using MPM to illustrate the benefits of the system.

Keywords: optical coherence microscopy, multiphoton microscopy, multimodal imaging, two-photon fluorescence emission

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Authors: Roike Iwan Montolalu, Henny Adeleida Dien, Feny Mentang, Kristhina P. Rahael, Tomy Moga, Ayub Meko, Siegfried Berhimpon

Abstract:

In the last 20 years, packaging materials derived from petrochemicals polymers were widely used as packaging materials. This due to various advantages such as flexible, strong, transparent, and the price is relatively cheap. However, the plastic polymer also has various disadvantages, such as the transmission monomer contamination into the material to be packed, and waste is non-biodegradable. Edible film (EF) is an up to date materials, generated after the biodegradable packaging materials. The advantages of the EF materials, is the materials can be eat together with food, and the materials can be applied as a coating materials for a widely kind of foods especially snack foods. The aims of this research are to produce and to analyze the characteristics of smoked EF made from carrageenan, myofibril and collagen of Black Marlin (Makaira indica) industrial waste. Smoked EF made with an addition of 0.8 % smoke liquid. Three biopolymers i.e. carrageenan, myofibril, and collagen were used as treatments, and homogenate for 1 hours at speed of 1500 rpm. The analysis carried out on the pH and physical properties i.e. thickness, solubility, tensile strength, % elongation, and water vapor transmission rate (WVTR), as well as on the sensory characteristics of texture i.e. wateriness, firmness, elasticity, hardness, and juiciness of the coated products. The result shown that the higher the concentration the higher the thickness of EF, where as for myofibril proteins appeared higher than carrageenan and collagen. Both of collagen and myofibril shown that concentration of 6% was most soluble, while for carrageenan were in concentration of 2 to 2.5%. For tensile strength, carrageenan was significantly higher than myofibril and collagen; while for elongation, collagen film more elastic than carragenan and myofibril protein. Water vapor transmission rate, shown that myofibril protein film lower than carrageenan and collagen film. From sensory assessment of texture, carrageenan has a high elasticity and juiciness, while collagen and myofibril have a high in firmness and hardness.

Keywords: edible film, collagen, myofibril, carrageenan

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