Search results for: CSRF detection extension

Authors: Aicha Akrout, Amira Echtioui, Mohamed Ghorbel

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Pneumonia remains a significant global health concern, posing a substantial threat to human lives due to its contagious nature and potentially fatal respiratory complications caused by bacteria, fungi, or viruses. The reliance on chest X-rays for diagnosis, although common, often necessitates expert interpretation, leading to delays and potential inaccuracies in treatment. This study addresses these challenges by employing transfer learning techniques to automate the detection of lung diseases, with a focus on pneumonia. Leveraging three pre-trained models, VGG-16, ResNet50V2, and MobileNetV2, we conducted comprehensive experiments to evaluate their performance. Our findings reveal that the proposed model based on VGG-16 demonstrates superior accuracy, precision, recall, and F1 score, achieving impressive results with an accuracy of 93.75%, precision of 94.50%, recall of 94.00%, and an F1 score of 93.50%. This research underscores the potential of transfer learning in enhancing pneumonia diagnosis and treatment outcomes, offering a promising avenue for improving healthcare delivery and reducing mortality rates associated with this debilitating respiratory condition.

Keywords: chest x-ray, lung diseases, transfer learning, pneumonia detection

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Authors: Sunil Mittal, Sukhpreet Singh, Hardeep Kaur

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A simple voltammetric technique for on-line analysis of arsenite [As (III)] is reported. Owing to the affinity of As (III) with thiol group of proteins and enzymes, cysteine has been employed as reducing agent. The reduction study of As(III)-cysteine complex on indium tin oxide (ITO) electrode has been explored. The experimental parameters such as scan rate, cysteine concentration, pH etc. were optimized to achieve As (III) determination. The developed method provided dynamic linear range of detection from 0.1 to 1 mM with a detection limit of 0.1 mM. The method is applicable to environmental monitoring of As (III) from highly contaminated sources such as industrial effluents, wastewater sludge etc.

Keywords: arsenite, cysteine, linear sweep voltammetry, reduction

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Authors: Prakash K. Aithal, U. Dinesh Acharya, Rajesh Gopakumar

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Edge is variation of brightness in an image. Edge detection is useful in many application areas such as finding forests, rivers from a satellite image, detecting broken bone in a medical image etc. The paper discusses about finding edge of multiple aerial images in parallel .The proposed work tested on 38 images 37 colored and one monochrome image. The time taken to process N images in parallel is equivalent to time taken to process 1 image in sequential. The proposed method achieves pixel level parallelism as well as image level parallelism.

Keywords: edge detection, multicore, gpu, opencl, mpi

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Authors: Christos E. Lampropoulos, Maria Konsta, Tamta Sirbilatze, Ifigenia Apostolou, Vicky Dradaki, Konstantina Panouria, Irini Dri, Christina Kordali, Vaggelis Lambas, Georgios Mavras

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Objectives: Full Mini Nutritional Assessment (MNA) questionnaire is one of the most useful tools in diagnosis of malnutrition in hospitalized patients, which is related to increased morbidity and mortality. The purpose of our study was to assess the nutritional status of elderly, hospitalized patients and examine the hypothesis that MNA may predict mortality and extension of hospitalization. Methods: One hundred fifty patients (78 men, 72 women, mean age 80±8.2) were included in this cross-sectional study. The following data were taken into account in analysis: anthropometric and laboratory data, physical activity (International Physical Activity Questionnaires, IPAQ), smoking status, dietary habits, cause and duration of current admission, medical history (co-morbidities, previous admissions). Primary endpoints were mortality (from admission until 6 months afterwards) and duration of admission. The latter was compared to national guidelines for closed consolidated medical expenses. Logistic regression and linear regression analysis were performed in order to identify independent predictors for mortality and extended hospitalization respectively. Results: According to MNA, nutrition was normal in 54/150 (36%) of patients, 46/150 (30.7%) of them were at risk of malnutrition and the rest 50/150 (33.3%) were malnourished. After performing multivariate logistic regression analysis we found that the odds of death decreased 20% per each unit increase of full MNA score (OR=0.8, 95% CI 0.74-0.89, p < 0.0001). Patients who admitted due to cancer were 23 times more likely to die, compared to those with infection (OR=23, 95% CI 3.8-141.6, p=0.001). Similarly, patients who admitted due to stroke were 7 times more likely to die (OR=7, 95% CI 1.4-34.5, p=0.02), while these with all other causes of admission were less likely (OR=0.2, 95% CI 0.06-0.8, p=0.03), compared to patients with infection. According to multivariate linear regression analysis, each increase of unit of full MNA, decreased the admission duration on average 0.3 days (b:-0.3, 95% CI -0.45 - -0.15, p < 0.0001). Patients admitted due to cancer had on average 6.8 days higher extension of hospitalization, compared to those admitted for infection (b:6.8, 95% CI 3.2-10.3, p < 0.0001). Conclusion: Mortality and extension of hospitalization is significantly increased in elderly, malnourished patients. Full MNA score is a useful diagnostic tool of malnutrition.

Keywords: duration of admission, malnutrition, mini nutritional assessment score, prognostic factors for mortality

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Authors: Rachel F. Ajayi, Unathi Sidwaba, Usisipho Feleni, Samantha F. Douman, Ezo Nxusani, Lindsay Wilson, Candice Rassie, Oluwakemi Tovide, Priscilla G.L. Baker, Sibulelo L. Vilakazi, Robert Tshikhudo, Emmanuel I. Iwuoha

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Pyrazinamide (PZA) is among the first-line pro-drugs in the tuberculosis (TB) combination chemotherapy used to treat Mycobacterium tuberculosis. Numerous reports have suggested that hepatotoxicity due to pyrazinamide in patients is due to inappropriate dosing. It is therefore necessary to develop sensitive and reliable techniques for determining the PZA metabolic profile of diagnosed patients promptly and at point-of-care. This study reports the determination of PZA based on nanobiosensor systems developed from disuccinimidyl octanedioate modified Cytochrome P450-2E1 (CYP2E1) electrodeposited on gold substrates derivatised with (poly(8-anilino-1-napthalene sulphonic acid) PANSA/PVP-AgNPs nanocomposites. The rapid and sensitive amperometric PZA detection gave a dynamic linear range of 2 µM to 16 µM revealing a limit of detection of 0.044 µM and a sensitivity of 1.38 µA/µM. The Michaelis-Menten parameters; KM, KMapp and IMAX were also calculated and found to be 6.0 µM, 1.41 µM and 1.51 µA respectively indicating a nanobiosensor suitable for use in serum.

Keywords: tuberculosis, cytochrome P450-2E1, disuccinimidyl octanedioate, pyrazinamide

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Authors: Adamu Abdullahi, On Francisca, Saidu Isah Rambo, G. N. Obunadike, D. T. Chinyio

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The paper discusses the potential threat of Denial of Service (DoS) attacks in the Internet of Things (IoT) networks on constrained application protocols (CoAP). As billions of IoT devices are expected to be connected to the internet in the coming years, the security of these devices is vulnerable to attacks, disrupting their functioning. This research aims to tackle this issue by applying mixed methods of qualitative and quantitative for feature selection, extraction, and cluster algorithms to detect DoS attacks in the Constrained Application Protocol (CoAP) using the Machine Learning Algorithm (MLA). The main objective of the research is to enhance the security scheme for CoAP in the IoT environment by analyzing the nature of DoS attacks and identifying a new set of features for detecting them in the IoT network environment. The aim is to demonstrate the effectiveness of the MLA in detecting DoS attacks and compare it with conventional intrusion detection systems for securing the CoAP in the IoT environment. Findings: The research identifies the appropriate node to detect DoS attacks in the IoT network environment and demonstrates how to detect the attacks through the MLA. The accuracy detection in both classification and network simulation environments shows that the k-means algorithm scored the highest percentage in the training and testing of the evaluation. The network simulation platform also achieved the highest percentage of 99.93% in overall accuracy. This work reviews conventional intrusion detection systems for securing the CoAP in the IoT environment. The DoS security issues associated with the CoAP are discussed.

Keywords: algorithm, CoAP, DoS, IoT, machine learning

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Authors: P. O. Fatoki, R. S. Olaleye, B. O. Adeniji

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The study investigated farmers’ perception of the effects of climate change on rice production in Nasarawa State, Nigeria. Multi-stage sampling technique was used in selecting a total of 248 rice farmers from the study area. Data for the study were collected through the use of interview schedule. The data were analysed using both descriptive and inferential statistics. Results showed that majority (71.8%) of the respondents were married and the mean age of the respondents was 44.54 years. The results also showed that most adapted strategies for mitigating the effects of climate change on rice production were change of planting and harvesting date (67.7%), movement to another site (63.7%) and increased or reduced land size (58.5%). Relationship between the roles of extension agents in mitigating climate change effects on rice production and farmers’ perception were significant as revealed Chi-Square analysis from the study ; Dissemination of information ( = 2.16, P < 0.05) and use of demonstration methods ( = 2.15, P < 0.05). Poisson regression analysis revealed that educational status, farm size, experience and yield had significant relationship with the perception of the effects of climate change at 0.01 significance level while household size was as well significant at 0.05. It is recommended that some of the adaptive strategies and practices for mitigating the effects of climate change in rice production should be improved, while the extension outfits should be strengthened to ensure adequate dissemination of relevant information on climate change with a view to mitigate its effects on rice production.

Keywords: perception, rice farmers, climate change, mitigation, adaptive strategies

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Authors: Joshua van Staden, Dane Brown, Karen Bradshaw

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Gun violence is a significant problem in modern-day society. Early detection of carried handguns through closed-circuit television (CCTV) can aid in preventing potential gun violence. However, CCTV operators have a limited attention span. Machine learning approaches to automating the detection of dangerous gun carriers provide a way to aid CCTV operators in identifying these individuals. This study provides insight into the relationship between human key points extracted using human pose estimation (HPE) and their intention to fire a weapon. We examine the feature importance of each keypoint and their correlations. We use principal component analysis (PCA) to reduce the feature space and optimize detection. Finally, we run a set of classifiers to determine what form of classifier performs well on this data. We find that hips, shoulders, and knees tend to be crucial aspects of the human pose when making these predictions. Furthermore, the horizontal position plays a larger role than the vertical position. Of the 66 key points, nine principal components could be used to make nonlinear classifications with 86% accuracy. Furthermore, linear classifications could be done with 85% accuracy, showing that there is a degree of linearity in the data.

Keywords: feature engineering, human pose, machine learning, security

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Authors: Maad Kamal Al-Anni

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This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Intrusion Detection System (IDS), Mobile Adhoc Networks (MANET), Back Propagation Algorithm (BPA), Neural Networks (NN)

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Authors: Shih-Hao Chen, Chi-Wai Chow

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Multiple-input and multiple-output (MIMO) scheme can extend the transmission capacity for the light-emitting-diode (LED) visible light communication (VLC) system. The MIMO VLC system using the popular mobile-phone camera as the optical receiver (Rx) to receive MIMO signal from n x n Red-Green-Blue (RGB) LED array is desirable. The key step of decoding the received RGB LED array signals is detecting the direction of received array signals. If the LED transmitter (Tx) is rotated, the signal may not be received correctly and cause an error in the received signal. In this work, we propose and demonstrate a novel hierarchical transmission scheme which can reduce the computation complexity of rotation detection in LED array VLC system. We use the n x n RGB LED array as the MIMO Tx. A novel two dimension Hadamard coding scheme is proposed and demonstrated. The detection correction rate is above 95% in the indoor usage distance. Experimental results confirm the feasibility of the proposed scheme.

Keywords: Visible Light Communication (VLC), Multiple-input and multiple-output (MIMO), Red-Green-Blue (RGB), Hadamard coding scheme

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Authors: Bastian Vollrath, Hartwig Hubel

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In case of over-elastic and cyclic loading, strain may accumulate due to a ratcheting mechanism until the state of shakedown is possibly achieved. Load history dependent numerical investigations by a step-by-step analysis are rather costly in terms of engineering time and numerical effort. In the case of multi-parameter loading, where various independent loadings affect the final state of shakedown, the computational effort becomes an additional challenge. Therefore, direct methods like the Simplified Theory of Plastic Zones (STPZ) are developed to solve the problem with a few linear elastic analyses. Post-shakedown quantities such as strain ranges and cyclic accumulated strains are calculated approximately by disregarding the load history. The STPZ is based on estimates of a transformed internal variable, which can be used to perform modified elastic analyses, where the elastic material parameters are modified, and initial strains are applied as modified loading, resulting in residual stresses and strains. The STPZ already turned out to work well with respect to cyclic loading between two states of loading. Usually, few linear elastic analyses are sufficient to obtain a good approximation to the post-shakedown quantities. In a multi-dimensional load domain, the approximation of the transformed internal variable transforms from a plane problem into a hyperspace problem, where time-consuming approximation methods need to be applied. Therefore, a solution restricted to structures with four stress components was developed to estimate the transformed internal variable by means of three-dimensional vector algebra. This paper presents the extension to cyclic multi-parameter loading so that an unlimited number of load cases can be taken into account. The theoretical basis and basic presumptions of the Simplified Theory of Plastic Zones are outlined for the case of elastic shakedown. The extension of the method to many load cases is explained, and a workflow of the procedure is illustrated. An example, adopting the FE-implementation of the method into ANSYS and considering multilinear hardening is given which highlights the advantages of the method compared to incremental, step-by-step analysis.

Keywords: cyclic loading, direct method, elastic shakedown, multi-parameter loading, STPZ

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Authors: Manon Giraud, Francois-Damien Delapierre, Guenaelle Jasmin-Lebras, Cecile Feraudet-Tarisse, Stephanie Simon, Claude Fermon

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Early diagnosis or detection of harmful substances at low level is a growing field of high interest. The ideal test should be cheap, easy to use, quick, reliable, specific, and with very low detection limit. Combining the high specificity of antibodies-functionalized magnetic beads used to immune-capture biologic objects and the high sensitivity of a GMR-based sensors, it is possible to even detect these biologic objects one by one, such as a cancerous cell, a bacteria or a disease biomarker. The simplicity of the detection process makes its use possible even for untrained staff. Giant Magneto Resistance (GMR) is a recently discovered effect consisting in the electrical resistance modification of some conductive layers when exposed to a magnetic field. This effect allows the detection of very low variations of magnetic field (typically a few tens of nanoTesla). Magnetic nanobeads coated with antibodies targeting the analytes are mixed with a biological sample (blood, saliva) and incubated for 45 min. Then the mixture is injected in a very simple microfluidic chip and circulates above a GMR sensor that detects changes in the surrounding magnetic field. Magnetic particles do not create a field sufficient to be detected. Therefore, only the biological objects surrounded by several antibodies-functionalized magnetic beads (that have been captured by the complementary antigens) are detected when they move above the sensor. Proof of concept has been carried out on NS1 mouse cancerous cells diluted in PBS which have been bonded to magnetic 200nm particles. Signals were detected in cells-containing samples while none were recorded for negative controls. Binary response was hence assessed for this first biological model. The precise quantification of the analytes and its detection in highly diluted solution is the step now in progress.

Keywords: early diagnosis, giant magnetoresistance, lab-on-a-chip, submicron particle

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Authors: Shashank Kumar, Mansi Chandra, Ujjawal Singh, Parth Gupta, Rishi Ram, Arnab Sarkar

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Milk is one of the basic and primary sources of food and energy as we start consuming milk from birth. Hence, milk quality and purity and checking the concentration of its constituents become necessary steps. Considering the importance of the purity of milk for human health, the following study has been carried out to simultaneously detect and quantify the different adulterants like urea and starch in milk with the help of a paper-based microfluidic device integrated with a smartphone. The detection of the concentration of urea and starch is based on the principle of colorimetry. In contrast, the fluid flow in the device is based on the capillary action of porous media. The microfluidic channel proposed in the study is equipped with a specialized detection zone, and it employs a colorimetric indicator undergoing a visible color change when the milk gets in touch or reacts with a set of reagents which confirms the presence of different adulterants in the milk. In our proposed work, we have used iodine to detect the percentage of starch in the milk, whereas, in the case of urea, we have used the p-DMAB. A direct correlation has been found between the color change intensity and the concentration of adulterants. A calibration curve was constructed to find color intensity and subsequent starch and urea concentration. The device has low-cost production and easy disposability, which make it highly suitable for widespread adoption, especially in resource-constrained settings. Moreover, a smartphone application has been developed to detect, capture, and analyze the change in color intensity due to the presence of adulterants in the milk. The low-cost nature of the smartphone-integrated paper-based sensor, coupled with its integration with smartphones, makes it an attractive solution for widespread use. They are affordable, simple to use, and do not require specialized training, making them ideal tools for regulatory bodies and concerned consumers.

Keywords: paper based microfluidic device, milk adulteration, urea detection, starch detection, smartphone application

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Authors: Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh K. Pandey

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The edges of low contrast images are not clearly distinguishable to the human eye. It is difficult to find the edges and boundaries in it. The present work encompasses a new approach for low contrast images. The Chebyshev polynomial based fractional order filter has been used for filtering operation on an image. The preprocessing has been performed by this filter on the input image. Laplacian of Gaussian method has been applied on preprocessed image for edge detection. The algorithm has been tested on two test images.

Keywords: low contrast image, fractional order differentiator, Laplacian of Gaussian (LoG) method, chebyshev polynomial

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Authors: Jacintha Menezes, Ramalingam Dharmalingam, Palaiahnakote Shivakumara

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In the recent years, Omani acid lime cultivation and production has been affected by Citrus greening or Huanglongbing (HLB) disease. HLB disease is one of the most destructive diseases for citrus, with no remedies or countermeasures to stop the disease. Currently used Polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) HLB detection tests require lengthy and labor-intensive laboratory procedures. Furthermore, the equipment and staff needed to carry out the laboratory procedures are frequently specialized hence making them a less optimal solution for the detection of the disease. The current research uses hyperspectral imaging technology for automatic detection of citrus trees with HLB disease. Omani citrus tree leaf images were captured through portable Specim IQ hyperspectral camera. The research considered healthy, nutrition deficient, and HLB infected leaf samples based on the Polymerase chain reaction (PCR) test. The highresolution image samples were sliced to into sub cubes. The sub cubes were further processed to obtain RGB images with spatial features. Similarly, RGB spectral slices were obtained through a moving window on the wavelength. The resized spectral-Spatial RGB images were given to Convolution Neural Networks for deep features extraction. The current research was able to classify a given sample to the appropriate class with 92.86% accuracy indicating the effectiveness of the proposed techniques. The significant bands with a difference in three types of leaves are found to be 560nm, 678nm, 726 nm and 750nm.

Keywords: huanglongbing (HLB), hyperspectral imaging (HSI), · omani citrus, CNN

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Authors: Arvind Srivastav, Tarun Kanti Bhattacharyya

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The north-eastern, Himalayan, and Eastern Ghats Belt of India comprise of earthquake-prone, remote, and hilly terrains. Earthquakes have caused enormous damages in these regions in the past. A wireless sensor network based earthquake early warning system (EEWS) is being developed to mitigate the damages caused by earthquakes. It consists of sensor nodes, distributed over the region, that perform majority voting of the output of the seismic sensors in the vicinity, and relay a message to a base station to alert the residents when an earthquake is detected. At the heart of the EEWS is a low-power two-stage seismic sensor that continuously tracks seismic events from incoming three-axis accelerometer signal at the first-stage, and, in the presence of a seismic event, triggers the second-stage P-wave detector that detects the onset of P-wave in an earthquake event. The parameters of the P-wave detector have been optimized for minimizing detection time and maximizing the accuracy of detection.Working of the sensor scheme has been verified with seven earthquakes data retrieved from IRIS. In all test cases, the scheme detected the onset of P-wave accurately. Also, it has been established that the P-wave onset detection time reduces linearly with the sampling rate. It has been verified with test data; the detection time for data sampled at 10Hz was around 2 seconds which reduced to 0.3 second for the data sampled at 100Hz.

Keywords: earthquake early warning system, EEWS, STA/LTA, polarization, wavelet, event detector, P-wave detector

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Authors: K. Shiba, T. Kaburagi, Y. Kurihara

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With global aging, people who require care, such as people with dementia (PwD), are increasing within many developed countries. And PwDs may wander and unconsciously set foot outdoors, it may lead serious accidents, such as, traffic accidents. Here, round-the-clock monitoring by caregivers is necessary, which can be a burden for the caregivers. Therefore, an automatic wandering detection system is required when an elderly person wanders outdoors, in which case the detection system transmits a ‘moving’ followed by an ‘absence’ state. In this paper, we focus on the transition from the ‘resting’ to the ‘absence’ state, via the ‘moving’ state as one of the wandering transitions. To capture the transition of the three states, our method based on the hidden Markov model (HMM) is built. Using our method, the restraint where the ‘resting’ state and ‘absence’ state cannot be transmitted to each other is applied. To validate our method, we conducted the experiment with 10 subjects. Our results show that the method can classify three states with 0.92 accuracy.

Keywords: wander, microwave Doppler sensor, respiratory frequency band, the state transition, hidden Markov model (HMM).

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Authors: A. A. M. A. S. S. Perera, L. A. Ranasinghe, T. K. H. Nimeshika, D. M. Dhanushka Dissanayake, Namalie Walgampaya

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Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases.

Keywords: Circularity Index, Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix, Local Binary Pattern, Object detection, Ring Detection, Shape Identification

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Authors: Mahmoud Mabrok, Channarong Rodkhum

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Flavobacterium columanre is one of the devastating pathogen that causes noticeable economic losses in freshwater cultured fish. Like other filamentous bacteria, F. columanre tends to aggregate and fluctuate to all kind of media, thus revealing obstacles in recognition of its colonies. Since the molecular typing is the only fundamental tool for rapid and precise detection of this pathgen. The present study developed a species-specific PCR assay based on cslA unique gene of F. columnare. The cslA gene sequences of 13 F. columnare, strains retrieved from gene bank database, were aligned to identify a conserved homologous segment prior to primers design. The new primers yielded amplicons of 287 bp from F. columnare strains but not from relevant or other pathogens, unlike to other published set that showed no specificity and cross-reactivity with F. indicum. The primers were sensitive and detected as few as 7 CFUs of bacteria and 3 pg of gDNA template. The sensitivity was reduced ten times when using tissue samples. These primers precisely defined all field isolates in a double-blind study, proposing their applicable use for field detection.

Keywords: Columnaris infection, cslA gene, Flavobacterium columnare, PCR

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Authors: Francielle A. Vargas, Thiago A. S. Pardo

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According to the standard philosophical definition, lying is saying something that you believe to be false with the intent to deceive. For deception detection, the FBI trains its agents in a technique named statement analysis, which attempts to detect deception based on parts of speech (i.e., linguistics style). This method is employed in interrogations, where the suspects are first asked to make a written statement. In this poster, we model false statements using linguistics style. In order to achieve this, we methodically analyze linguistic features in a corpus of fake news in the Portuguese language. The results show that they present substantial lexical, syntactic and semantic variations, as well as punctuation and emotion distinctions.

Keywords: deception detection, linguistics style, computational linguistics, natural language processing

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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: Gurinder Singh, Kulbir Singh

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Digital multimedia contents such as image, video, and audio can be tampered easily due to the availability of powerful editing softwares. Multimedia forensics is devoted to analyze these contents by using various digital forensic techniques in order to validate their authenticity. Digital image forensics is dedicated to investigate the reliability of digital images by analyzing the integrity of data and by reconstructing the historical information of an image related to its acquisition phase. In this paper, a survey is carried out on the forgery detection by considering the most recent and promising digital image forensic techniques.

Keywords: Computer Forensics, Multimedia Forensics, Image Ballistics, Camera Source Identification, Forgery Detection

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Authors: Mohamad Reza Asariha

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The development and extension of modern communication innovations at an extraordinary speed has caused crucial changes in all financial, social, social and political areas of the world. The improvement of toady and cable innovations, in expansion to expanding the generation and dissemination needs of worldwide programs; the financial defense made it more appealing. The alter of the administration of mechanical economy to data economy and benefit economy in created nations brought approximately uncommon advancements within the standards of world exchange and as a result, it caused the extension of media organizations in outside measurements, and the advancement of financial speculations in many Asian nations, beside the worldwide demand for the utilization of media merchandise, made new markets, and the media both within the household scene of the nations and within the universal field. Universal and financial are of great significance and have and viable and compelling nearness within the condition of picking up, keeping up and expanding financial control and riches within the world. Moreover, mechanical progresses and mechanical joining are critical components in media auxiliary alter. This auxiliary alter took put beneath the impact of digitalization. That’s, the method that broke the boundaries between electronic media administrations. Until presently, the direction of mass media was totally subordinate on certain styles of data transmission that were for the most part utilized. Digitization made it conceivable for any content to be effortlessly transmitted through distinctive electronic transmission styles, and this media merging has had clear impacts on media approaches and the way mass media are controlled.

Keywords: media, digital era, digital ages, media convergence

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Authors: Shivani Jha, Klaus Baier, Rafiq Azzam, Ramakar Jha

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In India, generally people migrate from rural area to the urban area for better infra-structural facilities, high standard of living, good job opportunities and advanced transport/communication availability. In fact, unplanned urban development due to migration of people causes seriou damage to the land use, water pollution and available water resources. In the present work, an attempt has been made to use satellite data of different years for urban change detection of Chennai metropolitan city along with pattern analysis to generate future scenario of urban development using buffer zoning in GIS environment. In the analysis, SRTM (30m) elevation data and IRS-1C satellite data for the years 1990, 2000, and 2014, are used. The flow accumulation, aspect, flow direction and slope maps developed using SRTM 30 m data are very useful for finding suitable urban locations for industrial setup and urban settlements. Normalized difference vegetation index (NDVI) and Principal Component Analysis (PCA) have been used in ERDAS imagine software for change detection in land use of Chennai metropolitan city. It has been observed that the urban area has increased exponentially in Chennai metropolitan city with significant decrease in agriculture and barren lands. However, the water bodies located in the study regions are protected and being used as freshwater for drinking purposes. Using buffer zone analysis in GIS environment, it has been observed that the development has taken place in south west direction significantly and will do so in future.

Keywords: urban change, satellite data, the Chennai metropolis, change detection

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Authors: Yash Jain

Abstract:

The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Edemealem Desalegn Kingawa, Kafte Tasew Timkete, Mekashaw Girmaw Abebe, Terefe Feyisa, Abiyot Bitew Mihretie, Senait Teklemarkos Haile

Abstract:

Currently, hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite this, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of the literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last six years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions, are discussed in detail.

Keywords: Amharic hate speech, deep learning approach, hate speech detection review, Afaan Oromo hate speech detection

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Authors: Behrooz Nikahval, Mohammad Saeed Ahrari-Khafi, Sakineh Behroozpoor, Saeed Nazifi

Abstract:

Osteoarthritis (OA) is a progressive and degenerative condition of the articular cartilage and other joints’ structures. It is essential to diagnose this condition as early as possible. The present research was performed to measure the Osteoprotegrin (OPG) and Tumor Necrosis Factor α (TNF-α) in synovial fluid and blood serum of dogs with surgically transected cruciate ligament as a model of OA, to evaluate if measuring of these parameters can be used as a way of early diagnosis of OA. In the present study, four mature, clinically healthy dogs were selected to investigate the effect of experimental OA, on OPG and TNF-α as a way of early detection of OA. OPG and TNF-α were measured in synovial fluid and blood serum on days 0, 14, 28, 90 and 180 after surgical transaction of cranial cruciate ligament in one stifle joint. Statistical analysis of the results showed that there was a significant increase in TNF-α in both synovial fluid and blood serum. OPG showed a decrease two weeks after OA induction. However, it fluctuated afterward. In conclusion, TNF-α could be used in both synovial fluid and blood serum as a way of early detection of OA; however, further research still needs to be conducted on OPG values in OA detection.

Keywords: osteoarthritis, osteoprotegrin, tumor necrosis factor α, synovial fluid, serum, dog

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Authors: Saman Ghaffarian, Ilgin Gökaşar

Abstract:

This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: aerial images, intelligent transportation systems, traffic density measurement, vehicle detection

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Authors: Shashank Gahlaut, Chandrashekhar Sharan, J. P. Singh

Abstract:

Increasing incidence of antibiotic-resistant bacteria is a big concern for the treatment of pathogenic diseases. The effect of treatment of patients with antibiotics often leads to the evolution of antibiotic resistance in the pathogens. The detection of antibiotic or antimicrobial resistant bacteria (microbes) is quite essential as it is becoming one of the big threats globally. Here we propose a novel technique to tackle this problem. We are taking a step forward to prevent the infections and diseases due to drug resistant microbes. This detection is based on some unique features of silver (a noble metal) nanorods (AgNRs) which are fabricated by a physical deposition method called thermal glancing angle deposition (GLAD). Silver nanorods are found to be highly sensitive and selective for hydrogen sulfide (H2S) gas. Color and water wetting (contact angle) of AgNRs are two parameters what are effected in the presence of this gas. H₂S is one of the major gaseous products evolved in the bacterial metabolic process. It is also known as gasotransmitter that transmits some biological singles in living systems. Nitric Oxide (NO) and Carbon mono oxide (CO) are two another members of this family. Orlowski (1895) observed the emission of H₂S by the bacteria for the first time. Most of the microorganism produce these gases. Here we are focusing on H₂S gas evolution to determine live/dead and antibiotic-resistant bacteria. AgNRs array has been used for the detection of H₂S from micro-organisms. A mobile app is also developed to make it easy, portable, user-friendly, and cost-effective.

Keywords: antibiotic resistance, hydrogen sulfide, live and dead bacteria, mobile app

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Authors: Pawel Kiper, Alfonc Baba, Mahmoud Alhelou, Giorgia Pregnolato, Michela Agostini, Andrea Turolla

Abstract:

Impairment of cervical spine mobility is often related to pain triggered by musculoskeletal disorders or direct traumatic injuries of the spine. To date, these disorders are assessed with goniometers and inclinometers, which are the most popular devices used in clinical settings. Nevertheless, these technologies usually allow measurement of no more than two-dimensional range of motion (ROM) quotes in static conditions. Conversely, the wide use of motion tracking systems able to measure 3 to 6 degrees of freedom dynamically, while performing standard ROM assessment, are limited due to technical complexities in preparing the setup and high costs. Thus, motion tracking systems are primarily used in research. These systems are an integral part of virtual reality (VR) technologies, which can be used for measuring spine mobility. To our knowledge, the accuracy of VR measure has not yet been studied within virtual environments. Thus, the aim of this study was to test the reliability of a protocol for the assessment of sensorimotor function of the cervical spine in a population of healthy subjects and to compare whether using immersive or non-immersive VR for visualization affects the performance. Both VR assessments consisted of the same five exercises and random sequence determined which of the environments (i.e. immersive or non-immersive) was used as first assessment. Subjects were asked to perform head rotation (right and left), flexion, extension and lateral flexion (right and left side bending). Each movement was executed five times. Moreover, the participants were invited to perform head reaching movements i.e. head movements toward 8 targets placed along a circular perimeter each 45°, visualized one-by-one in random order. Finally, head repositioning movement was obtained by head movement toward the same 8 targets as for reaching and following reposition to the start point. Thus, each participant performed 46 tasks during assessment. Main measures were: ROM of rotation, flexion, extension, lateral flexion and complete kinematics of the cervical spine (i.e. number of completed targets, time of execution (seconds), spatial length (cm), angle distance (°), jerk). Thirty-five healthy participants (i.e. 14 males and 21 females, mean age 28.4±6.47) were recruited for the cervical spine assessment with immersive and non-immersive VR environments. Comparison analysis demonstrated that: head right rotation (p=0.027), extension (p=0.047), flexion (p=0.000), time (p=0.001), spatial length (p=0.004), jerk target (p=0.032), trajectory repositioning (p=0.003), and jerk target repositioning (p=0.007) were significantly better in immersive than non-immersive VR. A regression model showed that assessment in immersive VR was influenced by height, trajectory repositioning (p<0.05), and handedness (p<0.05), whereas in non-immersive VR performance was influenced by height, jerk target (p=0.002), head extension, jerk target repositioning (p=0.002), and by age, head flex/ext, trajectory repositioning, and weight (p=0.040). The results of this study showed higher accuracy of cervical spine assessment when executed in immersive VR. The assessment of ROM and kinematics of the cervical spine can be affected by independent and dependent variables in both immersive and non-immersive VR settings.

Keywords: virtual reality, cervical spine, motion analysis, range of motion, measurement validity

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