Search results for: computer virus classification

Authors: Ravi Vishwanath, Saumya Kumaar, S. N. Omkar

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Brain-computer interfacing (BCI) is a technology that is almost four decades old, and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering, etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, however, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the controllability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

Keywords: brain-computer interface, classification, machine learning, unmanned aerial vehicles

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Authors: Jaiden X. Schraut

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Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database.

Keywords: chest X-ray, deep learning, image segmentation, image classification

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Authors: Anjana Goel, Ashok Kumar Bhatia

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Ocimum sanctum, a well known medicinal plant is used for various alignments in Ayurvedic medicines. It was found to be effective in treating the humans suffering from different viral infections like chicken pox, small pox, measles and influenza. In addition, curative effect of the plant in malignant patients was also reported. In the present study, leaves of this plant were screened against animal viruses i.e. Bovine Herpes Virus-type-1 (BHV-1), Foot and Mouth disease virus (FMDV) and Newcastle Disease Virus (NDV). BHV-1 and FMDV were screened in MDBK and BHK cell lines respectively using cytopathic inhibition test. While NDV was propagated in chick embryo fibroblast culture and tested by haemagglutination inhibition test. Maximum non toxic dose of aqueous extract of Ocimum sanctum leaves was calculated by MTT assay in all the cell cultures and nontoxic doses were used for antiviral activity against viruses. 98.4% and 85.3% protection were recorded against NDV and BHV-1 respectively. However, Ocimum sanctum extract failed to show any inhibitory effect on the cytopathic effect caused by FMD virus. It can be concluded that Ocimum sanctum is a very effective remedy for curing viral infections in animals also.

Keywords: bovine herpes virus-type-1, foot and mouth disease virus, newcastle disease virus, Ocimum sanctum

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Authors: Vallejo Michael, Acuña Edgar, Roa Juan David, Peñuela Rosa, Parra Alejandra, Casallas Daniela, Rodriguez Sheyla

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Congenital Zika Virus Syndrome is an entity composed by a variety of birth defects presented in newborns that have been exposed to the Zika Virus during pregnancy. The syndrome characteristic features are severe microcephaly, cerebral tissue abnormalities, ophthalmological abnormalities such as uveitis and chorioretinitis, arthrogryposis, clubfoot deformity and muscular tone abnormalities. The confirmatory test is the Reverse transcription polymerase chain reaction (RT-PCR) associated to the physical findings. Here we present the case of a newborn with microcephaly whose mother presented a confirmed Zika Virus infection during the third trimester of pregnancy, despite of the evident findings and the history of Zika infection the RT-PCR in amniotic and cerebrospinal fluid of the newborn was negative. RT-PCR has demonstrated a low sensibility in samples with low viral loads, reason why, we propose a clinical diagnosis in patients with clinical history of Zika Virus infection during pregnancy accompanied by evident clinical manifestations of the child.

Keywords: congenital, Zika virus, microcephaly, reverse transcriptase polymerase chain reaction

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Authors: Dmitry Nosik, Nickolay Nosik, Elli Kaplina, Olga Lobach, Marina Chataeva, Lev Rasnetsov

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Background and aims: Human Immunodeficiency Virus (HIV) infection is very often associated with Herpes Simplex Virus (HSV) infection but HIV patients are treated with a cocktail of antiretroviral drugs which are toxic. The use of an antiviral drug which will be active against both viruses like ferrovir found in our previous studies is rather actual. Earlier we had shown that Fullerene poly-amino capronic acid (FPACA) was active in case of monoinfection of HIV-1 or HSV-1. The aim of the study was to analyze the efficiency of FPACA against mixed infection of HIV and HSV. Methods: The peripheral blood lymphocytes, CEM, MT-4 cells were simultaneously infected with HIV-1 and HSV-1. FPACA was added 1 hour before infection. Cells viability was detected by MTT assay, virus antigens detected by ELISA, syncytium formation detected by microscopy. The different multiplicity of HIV-1/HSV-1 ratio was used. Results: The double viral HIV-1/HSV-1 infection was more cytopathic comparing with monoinfections. In mixed infection by the HIV-1/HSV-1 concentration of HIV-1 antigens and syncytium formations increased by 1,7 to 2,3 times in different cells in comparison with the culture infected with HIV-1 alone. The concentration of HSV-1 increased by 1,5-1,7 times, respectively. Administration of FPACA (1 microg/ml) protected cells: HIV-1/HSV-1 (1:1) – 80,1%; HIV-1/HSV-1 (1:4) – 57,2%; HIV-1/HSV-1 (1:8) – 46,3 %; HIV-1/HSV-1 (1:16) – 17,0%. Virus’s antigen levels were also reduced. Syncytium formation was totally inhibited in all cases of mixed infection. Conclusion: FPACA showed antiviral activity in case of mixed viral infection induced by Human Immunodeficiency Virus and Herpes Simplex Virus. The effect of viral inhibition increased with the multiplicity of HIV-1 in the inoculum. The mechanism of FPACA action is connected with the blocking of the virus particles adsorption to the cells and it could be suggested that it can have an antiviral activity against some other viruses too. Now FPACA could be considered as a potential drug for treatment of HIV disease complicated with opportunistic herpes viral infection.

Keywords: antiviral drug, human immunodeficiency virus (hiv), herpes simplex virus (hsv), mixed viral infection

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Authors: Muhammad Farooq Baig, Saleem Qadeer

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Background: The frequency of hepatitis C virus infection along with tuberculosis has not been widely investigated and very low statistics on rates of hepatitis C virus co-infection in tuberculosis patients. Hepatotoxicity is the major side effect of anti-tuberculosis therapy hepatitis HCVliver disease elevates the chances of hepatotoxicity up-to five folds. Objectives & Aim: To see the frequency of Hepatitis Cvirus infection amongst people with diagnosed Tuberculosis using gene X-pert technique. To evaluate the factors associated with HCVinfection in patients with MTBtuberculosis and to determine sensitivity and specificity of the tests. Study design: Comparative analytical study. Methodology: Three hundred and thirteen patients of tuberculosis diagnosed by Genexpert included while testing hepatitis C virus using immunochromotography rapid test technique, enzyme linked immunosorbent assay method and polymerase chain reaction test for confirmation. Results:Higher frequency of tuberculosis infection in males 57.8%, 42.5% between 20-39 years and 22% of hepatitis C virus infection in tuberculosis patients.The sensitivity of rapid test and enzyme-linked immunosorbent assay was 79% and 96% respectively while the specificity of rapid test and enzyme-linked immunosorbent assay was 91% and 99% respectively.

Keywords: Mycobactrium Tuberculosis, PC'R, Gene x pert, Hepatitis C virus

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Authors: Khabat Barkhordari, Ali Mohammad Arabzadeh

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Hepatitis delta virus (HDV) is a RNA virus that needs the function of hepatitis B virus (HBV) for its propagation and assembly. Infection by HDV can occur spontaneously with HBV infection and cause acute hepatitis or develop as secondary infection in HBV suffering patients. Based on genome sequence analysis, HDV has several genotypes which show broad geographic and diverse clinical features. The aim of current study is determine the prevalence of hepatitis delta virus genotype in patients with positive HBsAg in Kerman province of Iran. This cross-sectional study a total of 400 patients with HBV infection attending the clinic center of Besat from 2012 to 2014 were included. We carried out ELISA to detect anti-HDV antibodies. Those testing positive were analyzed further for HDV-RNA and for genotyping using restriction fragment length polymorphism (RFLP) and RT-nested PCR- sequencing. Among 400 patients in this study, 67 cases (16.75 %) were containing anti-HDV antibody which we found HDV RNA in just 7 (1.75%) serum samples. Analysis of these 7 positive HDV showed that all of them have genotype I. According to current study the HDV prevalence in Kerman is higher than the reported prevalence of 6.6% for Iran as a whole and clade 1 (genotype 1) is the predominant clade of HDV in Kerman.

Keywords: genotyping, hepatitis delta virus, molecular epidemiology, Kerman, Iran

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Authors: Guler D. Uzel, Andrew G. Parker, Robert L. Mach, Adly Abd-Alla

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Several tsetse fly species (Diptera: Glossinidae) in natural or colonized populations can be infected with the salivary gland hypertrophy virus (SGHV), a circular dsDNA virus (Hytrosaviridae). The virus infection is mainly asymptomatic but, in some species under certain conditions, the infection can produce salivary gland hypertrophy (SGH) symptoms. In the laboratory colonized tsetse, flies with SGH have reduced fertility, which negatively affects colony performance. Therefore, a high prevalence of SGH in insect mass rearing represents a major challenge for tsetse control using the sterile insect technique. The main objective of this study is to analyze the impact of Glossina pallidipes SGHV infection in various tsetse species on mortality and productivity and its impact on the symbiotic bacteria. Hypertropied salivary glands (SG) were collected from G. pallidipes into phosphate buffered saline (PBS) to prepare suspension; 2 µl aliquots were injected into adults of several tsetse species (G. pallidipes (Gp), G. p. gambiensis (Gpg), G. brevipalpis (Gb), G. morsitans morsitans (Gmm), G. morsitans centralis (Gmc) and G. fuscipes (Gf)) and the change in virus and symbiont titers were analyzed using qPCR. The development of SGH in the F1 was detected by dissection 10 days after emergence and virus infection was confirmed by PCR. The impact of virus infection on fly mortality and productivity was recorded. 2 µl aliquots were also injected into 3rd instar larvae of the different species and the adult SGs assayed by PCR for virus. Virus positive SGs from each species were homogenized in PBS and pooled within species for injection into larvae of the same species. Flies injected with PBS were used as control. Injecting teneral flies with SGHV caused increasing virus titer over time in all species but no SGH was detected. Dissection of the F1 also showed no development of SGH except in Gp (the homologous host). Injection of SGHV did not have any impact on the prevalence of the tsetse symbionts, but an increase in Sodalis titer was observed correlated with fly age regardless of virus infection. The virus infection had a negative impact on productivity and mortality. SGHV injection into larvae of the different species produced SGHV infected glands in the adults determined by PCR with a rate of 60%, 27%, 16%, 7% and 7% for Gp, Gf, Gpg, Gmm and Gmc, respectively. Virus positive SGs observed in the heterologous species were smaller than SGH found in Gp. No virus positive SG was detected by PCR in Gb and no SGH was observed in any adults except in Gp. Injecting virus suspension from the virus positive SGs into conspecific larvae did not produce any adults with infected SGs (except in Gp). SGHV can infect all tested tsetse species. Although the virus can infect and increase in titer in other tsetse species and affect fly mortality and productivity, no vertical virus transmission was observed in other tsetse species with might indicate a transmission barrier in these species, and virus collected from flies injected as larvae was not infective by injection.

Keywords: DNA viruses, glossina, hytrosaviridae, symbiotic bacteria, tsetse

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: Fabio Fabris, Alex A. Freitas

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Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Keywords: algorithm recommendation, meta-learning, bioinformatics, hierarchical classification

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Authors: Noureddine Adjlane, Nizar Haddad

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Deformed Wing Virus (DWV) is considered as the most prevalent virus that dangerous the honeybee health worldwide today. In this study we aimed to evaluate the impact of the virus on honeybees (Apis mellifera intermissa) mortality in Algeria and we conducted the study on samples collected from the central area in the country. We used PCR for the diagnoses of the (DWV) in the diagnosis. The results had shown a high infestation in the sampled colonies and it represented 42% of the total sample. In this study, we found a clear role of both Varroa destructor mite and DWV on hive mortality in the experimented apiary. Further studies need to be conducted in order to give soled recommendations to the beekeepers, decision makers and stockholders of the Algerian beekeeping sector.

Keywords: honey bee, DWV, Varroa destructor, mortality, prevalence, infestation

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Authors: Barbra Bhebhe, Melvyn Quan

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A serological survey was done to detect antibodies against Shuni virus (SHUV) from cattle in Western Kenya. In Kenya the disease status of SHUV in cattle has never been established. It is a zoonotic virus and even though studies have been carried out as early as the 1960s, little research has been published and SHUV is still not a well-recognised Orthobunyavirus. One hundred serum samples were collected from healthy cattle in Kenya and tested for antibodies against SHUV by a serum neutralization assay. All antibody titre values were greater than 1:160, with most of the samples greater than 1:320. Of the samples tested, 87 % had titres greater than 1:320, 12% had a titre of 1:320 and 2% had a titre of 1:160. Samples were classified as positive if the antibody titre was ≥ 1:10 and negative if < 1:10. This study suggests that cattle are exposed commonly to SHUV, which may be endemic in Kenya.

Keywords: Shuni virus, Orthobunyavuruses, serum neutralization test, cell-culture

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Authors: Sehrish Jalal, Choon-Mee Kim, Dong-Min Kim

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Background: Many pathogens causing hemorrhagic fevers of medical and veterinary importance have been identified and isolated from rodents in the Republic of Korea (ROK). Objective: We investigated the prevalence of emerging viruses causing hemorrhagic fevers, such as hemorrhagic fever with renal syndrome (HFRS), severe fever with thrombocytopenia syndrome (SFTS) and flaviviruses, from wild rodents. Methods: Striped field mice, Apodemus agrarius, (n=39) were captured during 2014-2015 in the south-west of ROK. Using molecular methods, lung samples were evaluated for SFTS virus, HFRS virus and flavivirus, and seropositivity was evaluated in the blood. Results: A high positive rate of Hantavirus (46.2%) was detected in A.agrarius lungs by reverse transcription-nested polymerase chain reaction (RT-N-PCR). The monthly prevalence of HFRS virus was 16.7% in October, 86.7% in November and 25% in August of the following year (p < 0.001). Moreover, 17.9% of blood samples were serologically positive for Hantavirus antibodies. The most prevalent strain in A. agrarius was Hantaan virus. All samples were positive for neither SFTS nor flavivirus. Conclusion: Hantan virus was detected in 86.7% of A. agrarius in November (autumn), and thus, virus shedding from A. agrarius can increase the risk of humans contracting HFRS. These findings may help to predict and prevent disease outbreaks in ROK.

Keywords: hemorrhagic fever virus, molecular diagnostic technique, rodents, Korea

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Authors: K. Domagala, D. Kata, T. Graule

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Diseases caused by contaminated drinking water are the worldwide problem, which leads to the death and severe illnesses for hundreds of millions million people each year. There is an urgent need for efficient water treatment techniques for virus pathogens removal. The aim of the research was to develop safe and economic solution, which help with the water treatment. In this study, the synthesis of copper-based multi-walled carbon nanotube composites is described. Proposed solution utilize combination of a low-cost material with a high active surface area and copper antiviral properties. Removal of viruses from water was possible by adsorption based on electrostatic interactions of negatively charged virus with a positively charged filter material.

Keywords: multi walled carbon nanotubes, water purification, virus removal, water treatment

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Authors: Hanjun Zhao

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Limited efficacy of current antivirals and antiviral-resistant mutations impair anti-influenza treatment. Here, we evaluated the in vitro and in vivo antiviral effect of three defective interfering genes (DIG-3) of influenza virus. Virus replication was significantly reduced in 293T and A549 cells transfected with DIG-3. Mice transfected with DIG-3 encoded by jetPEI-vector, as prophylaxis and therapeutics against A(H7N7) virus respectively, had significantly better survivals (80% and 50%) than control mice (0%). We further developed a dual-functional peptide TAT-P1, which delivers DIG-3 with high transfection efficiency and concomitantly exerts antiviral activity by preventing endosomal acidification. TAT-P1/DIG-3 was more effective than jetPEI/DIG-3 in treating A(H7N7) or A(H1N1)pdm09-infected mice and showed potent prophylactic protection on A(H7N7) or A(H1N1)pdm09-infected mice. The addition of P1 peptide, preventing endosomal acidification, could enhance the protection of TAT-P1/DIG-3 on A(H1N1)pdm09-infected mice. Dual-functional TAT-P1 with DIG-3 can effectively protect or treat mice infected by avian and seasonal influenza virus infection.

Keywords: antiviral peptide, dual-functional peptide, defective interfering genes, influenza virus

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Authors: Khabat Barkhordari

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Hepatitis delta virus (HDV) is a RNA virus that needs the function of hepatitis B virus (HBV) for its propagation and assembly. Infection by HDV can occur spontaneously with HBV infection and cause acute hepatitis or develop as secondary infection in HBV suffering patients. Based on genome sequence analysis, HDV has several genotypes which show broad geographic and diverse clinical features. The aim of current study is determine the molecular epidemiology of hepatitis delta virus genotype in patients with positive HBsAg among different ethnic groups of Iran. This systematic review study reviews the results of different studies which examined 2000 Iranian patients with HBV infection from 2010 to 2015. Among 2000 patients in this study, 16.75 % were containing anti-HDV antibody and HDV RNA was found in just 1.75% cases. All of positive cases also have genotype I.

Keywords: HDV, genotype, epidemiology, distribution

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Authors: Tao Youjun, Zhao Younan

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The separation effect of fine coal can be improved by the process of pre-desliming. It was significantly enhanced when the fine coal was processed using Falcon concentrator with the removal of -45um coal slime. Ultrafine classification tests using Krebs classification cyclone with annular rinse water showed that increasing feeding pressure can effectively avoid the phenomena of heavy particles passing into overflow and light particles slipping into underflow. The increase of rinse water pressure could reduce the content of fine-grained particles while increasing the classification size. The increase in feeding concentration had a negative effect on the efficiency of classification, meanwhile increased the classification size due to the enhanced hindered settling caused by high underflow concentration. As a result of optimization experiments with response indicator of classification efficiency which based on orthogonal design using Design-Expert software indicated that the optimal classification efficiency reached 91.32% with the feeding pressure of 0.03MPa, the rinse water pressure of 0.02MPa and the feeding concentration of 12.5%. Meanwhile, the classification size was 49.99 μm which had a good agreement with the predicted value.

Keywords: hydrocyclone, ultrafine classification, slime, classification efficiency, classification size

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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: 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: Zoltan Horvath, Dora Nagy

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In this research, it was examined whether individual COVID variants cause differences in the lactate curve of cyclists. After all, the virus variants attacked different organs in our body during the infections. During our tests, we used a traditional lactate step test, the results of which were compared with the values before the infection. In the tests, it has been proven that different virus variants show unique lactate curves. In this way, based on the lactate curve, it is possible to identify which variant caused the disease. Thanks to this, it has been shorten the return time, because we can apply the best return protocol after infection to the competitors.

Keywords: COVID-Sars19, lactate, virus mutation, lactate profile

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Authors: R. R. Ramsheeja, R. Sreeraj

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For view the internal structures of the human body such as liver, brain, kidney etc have a wide range of different modalities for medical images are provided nowadays. Computer Tomography is one of the most significant medical image modalities. In this paper use CT liver images for study the use of automatic computer aided techniques to calculate the volume of the liver tumor. Segmentation method is used for the detection of tumor from the CT scan is proposed. Gaussian filter is used for denoising the liver image and Adaptive Thresholding algorithm is used for segmentation. Multiple Region Of Interest(ROI) based method that may help to characteristic the feature different. It provides a significant impact on classification performance. Due to the characteristic of liver tumor lesion, inherent difficulties appear selective. For a better performance, a novel proposed system is introduced. Multiple ROI based feature selection and classification are performed. In order to obtain of relevant features for Support Vector Machine(SVM) classifier is important for better generalization performance. The proposed system helps to improve the better classification performance, reason in which we can see a significant reduction of features is used. The diagnosis of liver cancer from the computer tomography images is very difficult in nature. Early detection of liver tumor is very helpful to save the human life.

Keywords: computed tomography (CT), multiple region of interest(ROI), feature values, segmentation, SVM classification

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

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Authors: Emad Al-Abdallat, Faris G. Bakri, Azmi Mahafza, Rayyan Al Ali, Nidaa Ababneh, Ahmed Idhair

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Background: Morgues are high-risk areas for the spread of infection from the cadavers to the staff during the postmortem examination. Infection can spread from corpses to workers by the airborne route, by direct contact, or from needle and sharp object injuries. Objective: Knowledge about the prevalence of these infections among autopsies is prudent to appreciate any risk of transmission and to further enforce safety measures. Method: A total of 242 autopsies were tested. Age ranged from 3 days to 94 years (median 75.5 years, mean 45.3 (21.9 ± SD)). There were 172 (71%) males. Results: The cause of death was considered natural in 137 (56.6%) cases, accidental in 89 (36.8%), homicidal in 9 (3.7%), suicidal in 4 (1.7%), and unknown in 3 (1.2%). Hepatitis B surface antigen was positive in 5 (2.1%) cases. Hepatitis C virus antibody was detected in 5 (2.1%) cases and the hepatitis C virus polymerase chain reaction was positive in 2 of them (0.8%). HIV antibody was not detected in any of the cases. Conclusions: Autopsies can be associated with exposure to blood borne viruses. Autopsies performed during the study period were tested for hepatitis B surface antigen, hepatitis C virus antibody, and human immunodeficiency virus antibody. Positive tests were subsequently confirmed by polymerase chain reaction. There is low prevalence of infections with these viruses in our autopsy cases. However, the risk of transmission remains a threat. Healthcare workers in the forensic departments should adhere to standard precautions.

Keywords: autopsy, hepatitis B virus, hepatitis C virus, human immunodeficiency virus, Jordan

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Authors: Shreya Pare, Parvin Akhter

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Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures.

Keywords: classification, hyperspectral images, large distribution margin, modified fuzzy entropy function, multilevel thresholding, tree search algorithm, hyperspectral image classification using tree search algorithm

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Authors: Nikolai Nosik, Vladislav Podmasterjev, Nina Kondrashina, Marina Chataeva, Olga Lobach, Dmitry Noosik, Sergei Razumovskii

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Virucidal activity of ozone is well known: dissolved in water it kill viruses very fast. The virucidal capacity of ozone in ozone-air mixture is less known. The goal of the study was to investigate the virucidal potentials of the ozone–air mixture and kinetics of virus inactivation. Materials and methods. Ozone (O3 ) was generated from oxygen with ozonizer ( 1.0 – 75.0 mg\l). The ozone concentration was determined by the spectrophotometric methods. Virus contaminated samples were placed into the flowing reactor. Viruses: poliovirus type 1, vaccine strain (Sabin) and adenovirus, type 5, were obtained from the State virus collection. Titrations of viruses were carried out in appropriate cell cultures. CxT value ( mg\l x min) was calculated. Results. Metallic, polycarbonic and fiber “Kevlar” samples were contaminated with virus, dried and treated with ozone-air mixture in the flowing reactor. Kinetics of poliovirus inactivation: in 15 min at 5.0 mg\l -2.0 lg TCID50 inhibition , in 15 min at 10 mg\l – 2.5 lg TCID50 , 4.0 lg TCID50 inactivation of poliovirus was achieved after 75min at ozone concentration 20.0mg\l (99.99%). ( CxT = 75, 150 and 1500 mg\l x min on all three types of surfaces). It was found that the inactivation of poliovirus was more effective when the virus contaminated samples were wet (in 15 min at 20mg\l inhibition of virus in dry samples was 2.0 TCID50 , in wet samples – 4.0 TCID50). Adenovirus was less resistant to ozone treatment then poliovirus: 4.0 lg TCID50 inhibition was observed after 30 min of the treatment with ozone at 20mg\l ( CxT mg\l x min = 300 for adenovirus as for poliovirus it was 1500). Conclusion. It was found that ozone-air mixture inactivates viruses at rather high concentrations (compared to the reported effect of ozone dissolved in water). Despite of that there is a difference in the resistance to ozone action between viruses – poliovirus is more resistant then adenovirus-ozone-air mixture can be used for disinfection of large rooms. The maintaining of the virus-contaminated surfaces in wet condition allow to decrease the ozone load for virus inactivation.

Keywords: adenovirus, disinfection, ozone, poliovirus

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Authors: Bingquan Shen

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Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline.

Keywords: convolutional neural networks, object classification, pose normalization, viewpoint invariant

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Authors: Y. Tiamaz, N. Souissi

Abstract:

The purpose of this paper is to present a classification of Lean models which aims to capture all the concepts related to this approach and thus facilitate its implementation. This classification allows the identification of the most relevant models according to several dimensions. From this perspective, we present a review and an analysis of Lean models literature and we propose dimensions for the classification of the current proposals while respecting among others the axes of the Lean approach, the maturity of the models as well as their application domains. This classification allowed us to conclude that researchers essentially consider the Lean approach as a toolbox also they design their models to solve problems related to a specific environment. Since Lean approach is no longer intended only for the automotive sector where it was invented, but to all fields (IT, Hospital, ...), we consider that this approach requires a generic model that is capable of being implemented in all areas.

Keywords: lean approach, lean models, classification, dimensions, holistic view

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Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, Lionel L. J. Ang, Algernon C. S. Hong, Danette S. E. Tan, Grace H. B. Foo, K. Q. Hong, L. M. Cheng, M. L. Leong

Abstract:

This paper presents a novel rapid soil classification technique that combines computer vision with four-probe soil electrical resistivity method and cone penetration test (CPT), to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from local construction projects are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labour-intensive. Thus, a rapid classification method is needed at the SGs. Computer vision, four-probe soil electrical resistivity and CPT were combined into an innovative non-destructive and instantaneous classification method for this purpose. The computer vision technique comprises soil image acquisition using industrial grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). Complementing the computer vision technique, the apparent electrical resistivity of soil (ρ) is measured using a set of four probes arranged in Wenner’s array. It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the soil strength is measured using a modified mini cone penetrometer, and w is measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay” and an even mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay”. It is also found that these parameters can be integrated with the computer vision technique on-site to complete the rapid soil classification in less than three minutes.

Keywords: Computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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Authors: Bharatendra Rai

Abstract:

The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning.

Keywords: long short-term memory networks, convolutional recurrent networks, text classification, hyperparameter tuning, Tukey honest significant differences

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Authors: Iqra Yaseen

Abstract:

One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

Procedia PDF Downloads 64