Search results for: computer virus classification

Authors: Babak Bashari Rad

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A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate.

Keywords: malware classification, computer virus classification, metamorphic virus, metamorphic malware, Hidden Markov Model

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Authors: Sulaiman Al amro, Ali Alkhalifah

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The growing number of computer viruses and the detection of zero day malware have been the concern for security researchers for a large period of time. Existing antivirus products (AVs) rely on detecting virus signatures which do not provide a full solution to the problems associated with these viruses. The use of logic formulae to model the behaviour of viruses is one of the most encouraging recent developments in virus research, which provides alternatives to classic virus detection methods. In this paper, we proposed a comparative study about different virus detection techniques. This paper provides the advantages and drawbacks of different detection techniques. Different techniques will be used in this paper to provide a discussion about what technique is more effective to detect computer viruses.

Keywords: computer viruses, virus detection, signature-based, behaviour-based, heuristic-based

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Authors: Harriet Jane R. Caleja, Joel I. Ballesteros, Florian R. Del Mundo

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The dengue fever belongs to the world’s major cause of death, especially in the tropical areas. In the Philippines, the number of dengue cases during the first half of 2015 amounted to more than 50,000. In 2012, the total number of cases of dengue infection reached 132,046 of which 701 patients died. Dengue Nonstructural 1 virus (Dengue NS1 virus) is a recently discovered biomarker for the early detection of dengue virus. It is present in the serum of the dengue virus infected patients even during the earliest stages prior to the formation of dengue virus antibodies. A biosensor for the dengue detection using NS1 virus was developed for faster and accurate diagnostic tool. Biotinylated anti-dengue virus NS1 was used as the receptor for dengue virus NS1. Using the Diffractive Optics Technology (dotTM) technique, real time binding of the NS1 virus to the biotinylated anti-NS1 antibody is observed. The dot®-Avidin sensor recognizes the biotinylated anti-NS1 and this served as the capture molecule to the analyte, NS1 virus. The increase in the signal of the diffractive intensity signifies the binding of the capture and the analyte. The LOD was found to be 3.87 ng/mL while the LOQ is 12.9 ng/mL. The developed biosensor was also found to be specific for the NS1 virus.

Keywords: avidin-biotin, diffractive optics technology, immunosensor, NS1

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Authors: R. L. Ujjwal, Abhishek Jain

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In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model.

Keywords: computer vision, age group, face detection

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Authors: Aishrith Rao

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COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Sujata S. Kulkarni

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Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. This paper gives a review on effective texture classification method. The objective of the problem of texture representation is to reduce the amount of raw data presented by the image, while preserving the information needed for the task. Texture analysis is important in many applications of computer image analysis for classification include industrial and biomedical surface inspection, for example for defects and disease, ground classification of satellite or aerial imagery and content-based access to image databases.

Keywords: compressed sensing, feature extraction, image classification, texture analysis

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Authors: Guofan Shao, Lina Tang, Hao Zhang

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The values of image classification accuracy are affected by class size distributions and classification schemes, making it difficult to compare the performance of classification algorithms across different remote sensing data sources and classification systems. Based on the term efficacy from medicine and pharmacology, we have developed the metrics of image classification efficacy at the map and class levels. The novelty of this approach is that a baseline classification is involved in computing image classification efficacies so that the effects of class statistics are reduced. Furthermore, the image classification efficacies are interpretable and comparable, and thus, strengthen the assessment of image data classification methods. We use real-world and hypothetical examples to explain the use of image classification efficacies. The metrics of image classification efficacy meet the critical need to rectify the strategy for the assessment of image classification performance as image classification methods are becoming more diversified.

Keywords: accuracy assessment, efficacy, image classification, machine learning, uncertainty

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Authors: Pritika Ramharack, Mahmoud E. S. Soliman

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The re-emerging Zika virus is an arthropod-borne virus that has been described to have explosive potential as a worldwide pandemic. The initial transmission of the virus was through a mosquito vector, however, evolving modes of transmission has allowed the spread of the disease over continents. The virus already been linked to irreversible chronic central nervous system (CNS) conditions. The concerns of the scientific and clinical community are the consequences of Zika viral mutations, thus suggesting the urgent need for viral inhibitors. There have been large strides in vaccine development against the virus but there are still no FDA-approved drugs available. Rapid rational drug design and discovery research is fundamental in the production of potent inhibitors against the virus that will not just mask the virus, but destroy it completely. In silico drug design allows for this prompt screening of potential leads, thus decreasing the consumption of precious time and resources. This study demonstrates an optimized and proven screening technique in the discovery of two potential small molecule inhibitors of Zika virus Methyltransferase and RNA-dependent RNA polymerase. This in silico “per-residue energy decomposition pharmacophore” virtual screening approach will be critical in aiding scientists in the discovery of not only effective inhibitors of Zika viral targets, but also a wide range of anti-viral agents.

Keywords: NS5 protein inhibitors, per-residue decomposition, pharmacophore model, virtual screening, Zika virus

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Authors: Sarah Hamdan Al-Jahdali, Khaled Alsaad, Abdullah Al-Sayyari

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Objectives: To study the prevalence, clinicopathological features, risk factors and outcome of biopsy proven polyoma (BK) virus infection among Saudi kidney transplant recipients and compare them to negative BK virus group. Methods: We retrospectively reviewed the charts of all the patients with biopsy-proven polyoma (BK) virus infection in King Abdulaziz Medical City in Riyadh between 2005 and 2011. The details of clinical presentation, the indication for kidney biopsy, the laboratory findings at presentation, the natural history of the disease, thepathological findings, the prognosis as well as the response to therapy were all recorded. Results: Kidney biopsy was performed in 37 cases of unexplained graft dysfunction. BK virus was found in 10 (27%). Out of those 10, 3 (30%) ended with graft failure. BK virus occurred in all patients who received ATG induction therapy 100% versus 59.3% in the non BK virus patients (p=0.06). Furthermore, the risk of BK virus was much less in those who received acyclovir as an anti-viral prophylaxis as compared to those who did not receive it (p=0.01). Also, patients with BK virus weighed much less (mean 46.7±20.6 Kgs) than those without BK virus at time of transplantation (mean 64.3±12.1). Graft survival was better among deceased donor kidneys compared to living ones (P=0.016) and with older age (P=0.005). Conclusion: Our findings suggest the involvement of ATG induction therapy, the lack of antiviral prophylaxis therapy and lower weight at transplant as significant risk factors for the development of BK virus infection.

Keywords: BKVAN, BKV, kidney transpant, Saudi Arabia

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Authors: Lucas B. Naves, Maria José Abreu

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Reprocessing single-use device has attracted interesting on the medical environment over the last decades. The reprocessing technique was sought in order to reduce the cost of purchasing the new medical device, which can achieve almost double of the price of the reprocessed product. In this manuscript, we have done a literature review, aiming the reuse of medical device that was firstly designed for single use only, but has become, more and more, effective on its reprocessing procedure. We also show the regulation, the countries which allows this procedure, the classification of these device and also the most important issue concerning the re-utilization of medical device, how to minimizing the risk of gram positive and negative bacteria, avoid cross-contamination, hepatitis B (HBV), and C (HCV) virus, and also human immunodeficiency virus (HIV).

Keywords: reusing, reprocessing, single-use medical device, HIV, hepatitis B and C

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Authors: Adrian Garrido Sanchis, Richard Pashley

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The MS2 (ATCC15597-B1) virus was used as a surrogate to estimate the inactivation rates for enteric viruses when using a hot air bubble column evaporator (HBCE) system in the treatment of household wastewater. In this study, we have combined MS2 virus surface charging properties with thermal inactivation rates, using an improved double layer plaque assay technique, in order to assess the efficiency of the HBCE process for virus removal in water. When bubbling a continuous flow of dry air, at 200°C, only heats the aqueous solution in the bubble column to about 50°C. Viruses are not inactivated by this solution temperature, as confirmed separately from water bath heating experiments. Hence, the efficiency of the HBCE process for virus removal in water appeared to be caused entirely by collisions between the hot air bubbles and the virus organisms. This new energy efficient treatment for water reuse applications can reduce the thermal energy required to only 25% (about 113.7 kJ/L) of that required for boiling (about 450 kJ/L).

Keywords: MS2 virus inactivation, water reuse, hot bubble column evaporator, water treatment

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Authors: Maia Kukhaleishvili, Ekaterine Bulauri, Iveta Megrelishvili, Tamar Shamatava, Tamar Chipashvili

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Plant viruses can cause loss of yield and quality in a lot of important crops. Symptoms of pathogens are variable depending on the cultivars and virus strain. Selection of resistant potato varieties would reduce the risk of virus transmission and significant economic impact. Other way to avoid reduced harvest yields is regular potato seed production sampling and testing for viral infection. The aim of this study was to determine the occurrence and distribution of viral diseases according potato cultivars for further selection of virus-free material in Georgia. During the summer 2015- 2016, 5 potato cultivars (Sante, Laura, Jelly, Red Sonia, Anushka) at 5 different farms located in Akhalkalaki were tested for 6 different potato viruses: Potato virus A (PVA), Potato virus M (PVM), Potato virus S (PVS), Potato virus X (PVX), Potato virus Y (PVY) and potato leaf roll virus (PLRV). A serological method, Double Antibody Sandwich-Enzyme linked Immunosorbent Assay (DASELISA) was used at the laboratory to analyze the results. The result showed that PVY (21.4%) and PLRV (19.7%) virus presence in collected samples was relatively high compared to others. Researched potato cultivars except Jelly and Laura were infected by PVY with different concentrations. PLRV was found only in three potato cultivars (Sante, Jelly, Red Sonia) and PVM virus (3.12%) was characterized with low prevalence. PVX, PVA and PVS virus infection was not reported. It would be noted that 7.9% of samples were containing PVY/PLRV mix infection. Based on the results it can be concluded that PVY and PLRV infections are dominant in all research cultivars. Therefore significant yield losses are expected. Systematic, long-term control of potato viral infection, especially seed-potatoes, must be regarded as the most important factor to increase seed productivity.

Keywords: virus, potato, infection, diseases

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Authors: Lamyaa Gamal El-Deen Taha, Ashraf Sharawi

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China launched satellite GF-2 in 2014. This study deals with comparing nearest neighbor object-based classification and neural network classification methods for classification of the fused GF-2 image. Firstly, rectification of GF-2 image was performed. Secondly, a comparison between nearest neighbor object-based classification and neural network classification for classification of fused GF-2 was performed. Thirdly, the overall accuracy of classification and kappa index were calculated. Results indicate that nearest neighbor object-based classification is better than neural network classification for urban mapping.

Keywords: GF-2 images, feature extraction-rectification, nearest neighbour object based classification, segmentation algorithms, neural network classification, multilayer perceptron

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Authors: Rami Ayadi, Mohsen Maraoui, Mounir Zrigui

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In this paper, we have presented a brief current state of the art for Arabic text representation and classification methods. We decomposed Arabic Task Classification into four categories. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new classification methods and finally we investigate the impact of preprocessing on Arabic TC.

Keywords: text classification, Arabic, impact of preprocessing, classification algorithms

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Authors: Ergun Kaya

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Apple mosaic virus (ApMV) is an ilarvirus causing harmful damages and product loses in many plant species. Because of xylem and phloem vessels absence, plant meristem tissues used for meristem cultures are virus-free, but sometimes only meristem cultures are not sufficient for virus elimination. Cryotherapy, a new method based on cryogenic techniques, is used for virus elimination. In this technique, 0.1-0.3mm meristems are excised from organized shoot apex of a selected in vitro donor plant and these meristems are frozen in liquid nitrogen (-196 °C) using suitable cryogenic technique. The aim of this work was to develop an efficient procedure for ApMV-free hazelnut via cryotherapy technique and confirmation of virus-free plants using Reverse Transcriptase-PCR technique. 100% virus free plantlets were obtained using droplet-vitrification method involved cold hardening in vitro cultures of hazelnut, 24 hours sucrose preculture of meristems on MS medium supplemented with 0.4M sucrose, and a 90 min PVS2 treatment in droplets.

Keywords: droplet vitrification, hazelnut, liquid nitrogen, PVS2

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Authors: C. U. Om Kumar, S. Kishore, A. Geetha

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An Operating system (OS) is software that manages computer hardware and software resources by providing services to computer programs. One of the important user expectations of the operating system is to provide the practice of defending information from unauthorized access, disclosure, modification, inspection, recording or destruction. Operating system is always vulnerable to the attacks of malwares such as computer virus, worm, Trojan horse, backdoors, ransomware, spyware, adware, scareware and more. And so the anti-virus software were created for ensuring security against the prominent computer viruses by applying a dictionary based approach. The anti-virus programs are not always guaranteed to provide security against the new viruses proliferating every day. To clarify this issue and to secure the computer system, our proposed expert system concentrates on authorizing the processes as wanted and unwanted by the administrator for execution. The Expert system maintains a database which consists of hash code of the processes which are to be allowed. These hash codes are generated using MD5 message-digest algorithm which is a widely used cryptographic hash function. The administrator approves the wanted processes that are to be executed in the client in a Local Area Network by implementing Client-Server architecture and only the processes that match with the processes in the database table will be executed by which many malicious processes are restricted from infecting the operating system. The add-on advantage of this proposed Expert system is that it limits CPU usage and minimizes resource utilization. Thus data and information security is ensured by our system along with increased performance of the operating system.

Keywords: virus, worm, Trojan horse, back doors, Ransomware, Spyware, Adware, Scareware, sticky software, process table, MD5, CPU usage and resource utilization

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Authors: Makram Ben Jeddou

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The ABC classification is widely used by managers for inventory control. The classical ABC classification is based on the Pareto principle and according to the criterion of the annual use value only. Single criterion classification is often insufficient for a closely inventory control. Multi-criteria inventory classification models have been proposed by researchers in order to take into account other important criteria. From these models, we will consider the ZF model in order to make a sensitive analysis on the composite score calculated for each item. In fact, this score based on a normalized average between a good and a bad optimized index can affect the ABC items classification. We will then focus on the weights assigned to each index and propose a classification compromise.

Keywords: ABC classification, multi criteria inventory classification models, ZF-model

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Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Hossein Pourbashash

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Recent experimental studies have shown that HIV can be transmitted directly from cell to cell when structures called virological synapses form during interactions between T cells. In this article, we describe a new within-host model of HIV infection that incorporates two mechanisms: infection by free virions and the direct cell-to-cell transmission. We conduct the local and global stability analysis of the model. We show that if the basic reproduction number R0 1, the virus is cleared and the disease dies out; if R0 > 1, the virus persists in the host. We also prove that the unique positive equilibrium attracts all positive solutions under additional assumptions on the parameters.

Keywords: HIV virus model, cell-to-cell transmission, global stability, Lyapunov function, second compound matrices

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Authors: Rafik Djemili, Hocine Bourouba, M. C. Amara Korba

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In this paper, we present a method in order to classify EEG signals for Brain-Computer Interfaces (BCI). EEG signals are first processed by means of spectral estimation methods to derive reliable features before classification step. Spectral estimation methods used are standard periodogram and the periodogram calculated by the Welch method; both methods are compared with Logarithm of Band Power (logBP) features. In the method proposed, we apply Linear Discriminant Analysis (LDA) followed by Support Vector Machine (SVM). Classification accuracy reached could be as high as 85%, which proves the effectiveness of classification of EEG signals based BCI using spectral methods.

Keywords: brain-computer interface, motor imagery, electroencephalogram, linear discriminant analysis, support vector machine

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Authors: Juliana Lukša, Bazilė Ravoitytė, Elena Servienė, Saulius Serva

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Totiviridae L-A virus is a persistent Saccharomyces cerevisiae dsRNA virus. It encodes the major structural capsid protein Gag and Gag-Pol fusion protein, responsible for virus replication and encapsulation. These features also enable the copying of satellite dsRNAs (called M dsRNAs) encoding a secreted toxin and immunity to it (known as killer toxin). Viral capsid pore presumably functions in nucleotide uptake and viral mRNA release. During cell division, sporogenesis, and cell fusion, the virions remain intracellular and are transferred to daughter cells. By employing high throughput RNA sequencing data analysis, we describe the influence of solely L-A virus on the expression of genes in three different S. cerevisiae hosts. We provide a new perception into Totiviridae L-A virus-related transcriptional regulation, encompassing multiple bioinformatics analyses. Transcriptional responses to L-A infection were similar to those induced upon stress or availability of nutrients. It also delves into the connection between the cell metabolism and L-A virus-conferred demands to the host transcriptome by uncovering host proteins that may be associated with intact virions. To better understand the virus-host interaction, we applied differential proteomic analysis of virus particle-enriched fractions of yeast strains that harboreither complete killer system (L-A-lus and M-2 virus), M-2 depleted orvirus-free. Our analysis resulted in the identification of host proteins, associated with structural proteins of the virus (Gag and Gag-Pol). This research was funded by the European Social Fund under the No.09.3.3-LMT-K-712-19-0157“Development of Competences of Scientists, other Researchers, and Students through Practical Research Activities” measure.

Keywords: totiviridae, killer virus, proteomics, transcriptomics

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Authors: M.R Asghari Bejestani, Gh. R. Mohammad Khani, V.R. Nafisi

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Imagined speech recognition is one of the most interesting approaches to BCI development and a lot of works have been done in this area. Many different experiments have been designed and hundreds of combinations of feature extraction methods and classifiers have been examined. Reported classification accuracies range from the chance level to more than 90%. Based on non-stationary nature of brain signals, we have introduced 3 classification modes according to time difference in inter and intra-class samples. The modes can explain the diversity of reported results and predict the range of expected classification accuracies from the brain signal accusation procedure. In this paper, a few samples are illustrated by inspecting results of some previous works.

Keywords: brain computer interface, silent talk, imagined speech, classification, signal processing

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Authors: Abdul-Dahiru El-Yuguda, Saka Saheed Baba, Tawa Monilade Adisa, Mustapha Bala Abubakar

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Background: Chikungunya (CHIK) virus, a previously anecdotally described arbovirus, is now assuming a worldwide public health burden. The CHIK virus infection is characterized by potentially life threatening and debilitating arthritis in addition to the high fever, arthralgia, myalgia, headache and rash. Method: Three hundred and seventy (370) serum samples were collected from outpatients with febrile illness attending University of Maiduguri Teaching Hospital, Nigeria, and was used to detect for Chikungunya (CHIK) virus IgG and IgM antibodies using the Enzyme Linked Immunosorbent Assays (ELISAs). Result: Out of the 370 sera tested, 39 (10.5%) were positive for presence of CHIK virus antibodies. A total of 24 (6.5%) tested positive for CHIK virus IgM only while none (0.0%) was positive for presence of CHIK virus IgG only and 15 (4.1%) of the serum samples were positive for both IgG and IgM antibodies. A significant difference (p<0.0001) was observed in the distribution of CHIK virus antibodies in relation to gender. The males had prevalence of 8.5% IgM antibodies as against 4.6% observed in females. On the other hand 4.6% of the females were positive for concurrent CHIK virus IgG and IgM antibodies when compared to a prevalence of 3.4% observed in males. Only the age groups ≤ 60 years and the undisclosed age group were positive for presence of CHIK virus IgG and/or IgM antibodies. No significant difference (p>0.05) was observed in the seasonal prevalence of CHIK virus antibodies among the study subjects Analysis of the prevalence of CHIK virus antibodies in relation to clinical presentation (as observed by Clinicians) of the patients revealed that headache and fever were the most frequently encountered ailments. Conclusion: The CHIK virus IgM and concurrent IgM and IgG antibody prevalence rates of 6.5% and 4.1% observed in this study indicates a current infection and the lack of IgG antibody alone observed shows that the infection is not endemic but sporadic. Recommendation: Further studies should be carried to establish the seasonal prevalence of CHIK virus infection vis-à-vis vector dynamics in the study area. A comprehensive study need to be carried out on the molecular characterization of the CHIK virus circulating in Nigeria with a view to developing CHIK virus vaccine.

Keywords: Chikungunya virus, IgM and IgG antibodies, febrile patients, enzyme linked immunosorbent assay

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Authors: Romasa Qasim, G. M. Sayedur Rahman, Nahid Hasan, M. Shazzad Hosain

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Millions of people worldwide suffer from Hepatitis C, one of the fatal diseases. Interferon (IFN) and ribavirin are the available treatments for patients with Hepatitis C, but these treatments have their own side-effects. Our research focused on the development of an orally taken small molecule drug targeting the proteins in Hepatitis C Virus (HCV), which has lesser side effects. Our current study aims to the Pharmacophore based drug development of a specific small molecule anti-viral drug for Hepatitis C Virus (HCV). Drug designing using lab experimentation is not only costly but also it takes a lot of time to conduct such experimentation. Instead in this in silico study, we have used computer-aided techniques to propose a Pharmacophore-based anti-viral drug specific for the protein domains of the polyprotein present in the Hepatitis C Virus. This study has used homology modeling and ab initio modeling for protein 3D structure generation followed by pocket identification in the proteins. Drug-able ligands for the pockets were designed using de novo drug design method. For ligand design, pocket geometry is taken into account. Out of several generated ligands, a new Pharmacophore is proposed, specific for each of the protein domains of HCV.

Keywords: pharmacophore-based drug design, anti-viral drug, in-silico drug design, Hepatitis C virus (HCV)

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Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

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Authors: Jai Myung Yang, Na Young Kim, Sung Ho Shin

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The adverse reactions of current live smallpox vaccines and potential use of smallpox as a bioterror weapon have heightened the development of new effective vaccine for this infectious disease. In the present study, DNA vaccine vector was produced which was optimized for expression of the vaccinia virus L1 antigen in the mouse model. A plasmid IgM-tL1R, which contains codon-optimized L1R gene, was constructed and fused with an IgM signal sequence under the regulation of a SV40 enhancer. The expression and secretion of recombinant L1 protein was confirmed in vitro 293 T cell. Mice were administered the DNA vaccine by electroporation and challenged with vaccinia virus. We observed that immunization with IgM-tL1R induced potent neutralizing antibody responses and provided complete protection against lethal vaccinia virus challenge. Isotyping studies reveal that immunoglobulin G2 (IgG2) antibody predominated after the immunization, indicative of a T helper type 1 response. Our results suggest that an optimized DNA vaccine, IgM-tL1R, can be effective in stimulating anti-vaccinia virus immune response and provide protection against lethal orthopoxvirus challenge.

Keywords: DNA vaccine, electroporation, L1R, vaccinia virus

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Authors: Karim Abouelmehdi, Loubna Dali, Elmoutaoukkil Abdelmajid, Hoda Elsayed, Eladnani Fatiha, Benihssane Abderahim

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The security of cloud services is the concern of cloud service providers. In this paper, we will mention different classifications of cloud attacks referred by specialized organizations. Each agency has its classification of well-defined properties. The purpose is to present a high-level classification of current research in cloud computing security. This classification is organized around attack strategies and corresponding defenses.

Keywords: cloud computing, classification, risk, security

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Authors: Dilek Zorlu Kaya, Sena Çenesiz, Utku Duran

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Infectious pancreatic necrosis virus is a disease of great concern in aquaculture, causing mortality of 80 - 90% of the stocks in salmonid production. We aimed to investigate the efficacy of quercetin on oxidant and antioxidant parameters of infectious pancreatic necrosis virus, which is important for fish farming and economy in vitro. Quercetin experimental model was used in the cell culture of Oncorhynchus mykiss infected with infectious pancreatic necrosis virus. Malondialdehyde, ceruloplasmin, total oxidant capacity, total antioxidant levels, and glutathione-peroxidase were measured in the samples. As a result of the study, it was observed that quercetin can minimize the damage caused by scavenging free radicals in cells infected with infectious pancreatic necrosis virus. Thus, we think that an important development can be achieved for fish farming and the economy.

Keywords: IPNV, oncorhynchus mykiss, TAS, TOS, quercetin

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Authors: Osagiede Efosa Kelvin

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The Ebola virus disease (EVD); also Ebola hemorrhagic fever, is a disease of humans and other primates caused by Ebola viruses. Signs and symptoms typically start between two days and three weeks after contracting the virus as a fever, sore throat, muscle pain, and headaches. Then, vomiting, diarrhoea and rash usually follow, along with decreased function of the liver and kidneys. At this time, some people begin to bleed both internally and externally. The first death in Nigeria was reported on 25 July 2014: a Liberian-American with Ebola flew from Liberia to Nigeria and died in Lagos soon after arrival. As part of the effort to contain the disease, possible contacts were monitored –353 in Lagos and 451 in Port Harcourt On 22 September, the World Health Organisation reported a total of 20 cases, including eight deaths. The WHO's representative in Nigeria officially declared Nigeria Ebola-free on 20 October after no new active cases were reported in the follow-up contact. This paper looks at the Ebola Virus in general and the measures taken by Nigeria to combat its spread.

Keywords: Ebola virus, hemorrhagic fever, Nigeria, outbreak

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