Search results for: fraud prevention and detection

Authors: Anil P. Dewani, Alok S. Tripathi, Anil V. Chandewar

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Present manuscript reports the development and validation of a quantitative high performance liquid chromatography method for the pharmacokinetic evaluation of Glimepiride (GLM) and Ilaprazole (ILA) in rat plasma. The plasma samples were involved with Solid phase extraction process (SPE). The analytes were resolved on a Phenomenex C18 column (4.6 mm× 250 mm; 5 µm particle size) using a isocratic elution mode comprising methanol:water (80:20 % v/v) with pH of water modified to 3 using Formic acid, the total run time was 10 min at 225 nm as common wavelength, the flow rate throughout was 1ml/min. The method was validated over the concentration range from 10 to 600 ng/mL for GLM and ILA, in rat plasma. Metformin (MET) was used as Internal Standard. Validation data demonstrated the method to be selective, sensitive, accurate and precise. The limit of detection was 1.54 and 4.08 and limit of quantification was 5.15 and 13.62 for GLM and ILA respectively, the method demonstrated excellent linearity with correlation coefficients (r2) 0.999. The intra and inter-day precision (RSD%) values were < 2.0% for both ILA and GLM. The method was successfully applied in pharmacokinetic studies followed by oral administration in rats.

Keywords: pharmacokinetics, glimepiride, ilaprazole, HPLC, SPE

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Authors: Bo Ram Choi, Ji Su Kim, Juyeon Cho, Hyukjin Lee

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Food contaminated by bacteria (E.coli), causes food poisoning, which occurs to many patients worldwide annually. We have introduced an application of rolling circle amplification (RCA) as a versatile biosensor and developed a diagnostic platform composed of capillary tube and microbeads for rapid and easy detection of Escherichia coli (E. coli). When specific mRNA of E.coli is extracted from cell lysis, rolling circle amplification (RCA) of DNA template can be achieved and can be visualized by beads aggregation in capillary tube. In contrast, if there is no bacterial pathogen in sample, no beads aggregation can be seen. This assay is possible to detect visually target gene without specific equipment. It is likely to the development of a genetic kit for point of care testing (POCT) that can detect target gene using microbeads.

Keywords: rolling circle amplification (RCA), Escherichia coli (E. coli), point of care testing (POCT), beads aggregation, capillary tube

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Authors: Tauqir A. Moughal, Fusheng Yu, Abeer Mazher

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Land-cover and land-use change information are important because of their practical uses in various applications, including deforestation, damage assessment, disasters monitoring, urban expansion, planning, and land management. Therefore, developing change detection methods for remote sensing images is an important ongoing research agenda. However, detection of change through optical remote sensing images is not a trivial task due to many factors including the vagueness between the boundaries of changed and unchanged regions and spatial dependence of the pixels to its neighborhood. In this paper, we propose a binary change detection technique for bi-temporal optical remote sensing images. As in most of the optical remote sensing images, the transition between the two clusters (change and no change) is overlapping and the existing methods are incapable of providing the accurate cluster boundaries. In this regard, a methodology has been proposed which uses the fuzzy c-means clustering to tackle the problem of vagueness in the changed and unchanged class by formulating the soft boundaries between them. Furthermore, in order to exploit the neighborhood information of the pixels, the input patterns are generated corresponding to each pixel from bi-temporal images using 3×3, 5×5 and 7×7 window. The between images and within image spatial dependence of the pixels to its neighborhood is quantified by using Pearson product moment correlation and Moran’s I statistics, respectively. The proposed technique consists of two phases. At first, between images and within image spatial correlation is calculated to utilize the information that the pixels at different locations may not be independent. Second, fuzzy c-means technique is used to produce two clusters from input feature by not only taking care of vagueness between the changed and unchanged class but also by exploiting the spatial correlation of the pixels. To show the effectiveness of the proposed technique, experiments are conducted on multispectral and bi-temporal remote sensing images. A subset (2100×1212 pixels) of a pan-sharpened, bi-temporal Landsat 5 thematic mapper optical image of Los Angeles, California, is used in this study which shows a long period of the forest fire continued from July until October 2009. Early forest fire and later forest fire optical remote sensing images were acquired on July 5, 2009 and October 25, 2009, respectively. The proposed technique is used to detect the fire (which causes change on earth’s surface) and compared with the existing K-means clustering technique. Experimental results showed that proposed technique performs better than the already existing technique. The proposed technique can be easily extendable for optical hyperspectral images and is suitable for many practical applications.

Keywords: burned area, change detection, correlation, fuzzy clustering, optical remote sensing

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Authors: Leidy M. Aldana, Jorge E. Camargo

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Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense.

Keywords: Malware, adversarial, machine learning, defense, attack

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Authors: B. Gonzalez-Yebra, H. Barajas, P. Palomares, M. Hernandez, O. Torres, M. Ayala, A. L. González, G. Vazquez-Ortiz, M. L. Guzman

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Leukemia arises by molecular alterations of the normal hematopoietic stem cell (HSC) transforming it into a leukemic stem cell (LSC) with high cell proliferation, self-renewal, and cell differentiation. Chronic myeloid leukemia (CML) originates from an LSC-leading to elevated proliferation of myeloid cells and acute lymphoblastic leukemia (ALL) originates from an LSC development leading to elevated proliferation of lymphoid cells. In both cases, LSC can be identified by multicolor flow cytometry using several antibodies. However, to date, LSC levels in peripheral blood (PB) are not established well enough in ALL and CML patients. On the other hand, the detection of the minimal residue disease (MRD) in leukemia is mainly based on the identification of the mRNA bcr-abl gene in CML patients and some other genes in ALL patients. There is no a properly biomarker to detect MDR in both types of leukemia. The objective of this study was to determine mRNA bcr-abl and the percentage of LSC in peripheral blood of patients with CML and ALL and identify a possible association between the amount of LSC in PB and clinical data. We included in this study 19 patients with Leukemia. A PB sample was collected per patient and leukocytes were obtained by Ficoll gradient. The immunophenotype for LSC CD34+ was done by flow cytometry analysis with CD33, CD2, CD14, CD16, CD64, HLA-DR, CD13, CD15, CD19, CD10, CD20, CD34, CD38, CD71, CD90, CD117, CD123 monoclonal antibodies. In addition, to identify the presence of the mRNA bcr-abl by RT-PCR, the RNA was isolated using TRIZOL reagent. Molecular (presence of mRNA bcr-abl and LSC CD34+) and clinical results were analyzed with descriptive statistics and a multiple regression analysis was performed to determine statistically significant association. In total, 19 patients (8 patients with ALL and 11 patients with CML) were analyzed, 9 patients with de novo leukemia (ALL = 6 and CML = 3) and 10 under treatment (ALL = 5 and CML = 5). The overall frequency of mRNA bcr-abl was 31% (6/19), and it was negative in ALL patients and positive in 80% in CML patients. On the other hand, LSC was determined in 16/19 leukemia patients (%LSC= 0.02-17.3). The Novo patients had higher percentage of LSC (0.26 to 17.3%) than patients under treatment (0 to 5.93%). The amount of LSC was significantly associated with the amount of LSC were: absence of treatment, the absence of splenomegaly, and a lower number of leukocytes, negative association for the clinical variables age, sex, blasts, and mRNA bcr-abl. In conclusion, patients with de novo leukemia had a higher percentage of circulating LSC than patients under treatment, and it was associated with clinical parameters as lack of treatment, absence of splenomegaly and a lower number of leukocytes. The mRNA bcr-abl detection was only possible in the series of patients with CML, and molecular detection of LSC could be identified in the peripheral blood of all leukemia patients, we believe the identification of circulating LSC may be used as biomarker for the detection of the MRD in leukemia patients.

Keywords: stem cells, leukemia, biomarkers, flow cytometry

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Authors: Evgen Prokhorov, Gabriel Luna-Barcenas

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The polysaccharide chitosan (CS) is an attractive biopolymer for the stabilization of several nanoparticles in acidic aqueous media. This is due in part to the presence of abundant primary NH2 and OH groups which may lead to steric or chemical stabilization. Applications of most CS nanocomposites are based upon the interaction of high surface area nanoparticles (NPs) with different substance. Therefore, agglomeration of NPs leads to decreasing effective surface area such that it may decrease the efficiency of nanocomposites. The aim of this work is to measure nanocomposite’s electrical conductivity phenomena that will allow one to formulate optimal concentrations of conductivity NPs in CS-based nanocomposites. Additionally, by comparing the efficiency of such nanocomposites, one can guide applications in the biomedical (antibacterial properties and tissue regeneration) and sensor fields (detection of copper and nitrate ions in aqueous solutions). It was shown that the best antibacterial (CS-AgNPs, CS-AgNPs-carbon nanotubes) and would healing properties (CS-AuNPs) are observed in nanocomposites with concentrations of NPs near the percolation threshold. In this regard, the best detection limit in potentiometric and impedimetric sensors for detection of copper ions (using CS-AuNPs membrane) and nitrate ions (using CS-clay membrane) in aqueous solutions have been observed for membranes with concentrations of NPs near percolation threshold. It is well known that at the percolation concentration of NPs an abrupt increasing of conductivity is observed due to the presence of physical contacts between NPs; above this concentration, agglomeration of NPs takes place such that a decrease in the effective surface and performance of nanocomposite appear. The obtained relationship between electrical percolation threshold and performance of polymer nanocomposites with conductivity NPs is important for the design and optimization of polymer-based nanocomposites for different applications.

Keywords: chitosan, conductivity nanoparticles, percolation threshold, polymer nanocomposites

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Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: Windy Rakhmawati, Suryani Suryani, Sri Hendrawati, Nenden Nur Asriyani Maryam

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Tuberculosis (TB) is one of the leading causes of death in the world. Fear of COVID-19 has made people reluctant to visit health facilities, leading to disruptions to childhood TB control programs, which may increase household transmission and delay diagnosis and treatment. This study aimed to describe parents' knowledge, attitudes, and health-seeking behaviour towards childhood TB during the COVID-19 pandemic. This cross-sectional study was performed on 392 parents with TB children in three provinces with the highest proportion of TB cases in Indonesia. This study was conducted from February to December 2022. The inclusion criteria of respondents were parents with a child aged 0-14 years old with TB diagnosis who live with their parents. Data were collected using the Knowledge, Attitude, and Practice (KAP) survey guidelines from the World Health Organization and analyzed descriptively, as well as Spearman’s correlation. Overall, 392 parents of children with TB had poor knowledge (51.8%) including about causes, risk factors, transmission, symptoms, treatment, and prevention, which about 52.3%, 55.1%, 61.2%, 69.6%, 100%, 59.2%, respectively. Parents' health service-seeking behaviour towards Child TB was not normally distributed (P < 0.05) with knowledge test results (.000) and Seeking Health Services (.000). Health-seeking behaviour of parents in pediatric TB care was self-medication or self-treatment (86.2%), Traditional health seeking behaviour (4.8%), and modern health seeking behaviour (8.9%). The correlation between knowledge and seeking health services (Sig= .609) means there is no correlation between knowledge about TB and parents' health-seeking behaviour. Furthermore, 60.2% of the respondents would be shocked if their child had TB. More than half of the families in this study have poor knowledge and did self-medication or self-treatment regarding health-seeking behaviour for TB disease. Therefore, health workers, especially nurses, must provide TB-related education and health promotion and emphasize the importance of early detection. Health workers can also optimize their role in caring for and providing care to patients by increasing their trust in health workers, which will impact health-seeking behaviour in the future.

Keywords: attitude, child, health seeking behaviour, knowledge, tuberculosis

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Authors: Eva Savy, Anthony Ruiz

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In extrusion blow molding, there is great variability in product quality due to the sensitivity of the machine settings. These variations lead to unnecessary rejects and loss of time. Yet production control is a major challenge for companies in this sector to remain competitive within their market. Current quality control methods only apply to finished products (vision control, leak test...). It has been shown that material melt temperature, blowing pressure, and ambient temperature have a significant impact on the variability of product quality. Since blowing is a key step in the process, we have studied this parameter in this paper. The objective is to determine if airflow analysis allows the identification of quality problems before the full completion of the manufacturing process. We conducted tests to determine if it was possible to identify a leakage defect and an obstructed defect, two common defects on products. The results showed that it was possible to identify a leakage defect by airflow analysis.

Keywords: extrusion blow molding, signal, sensor, defects, detection

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Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

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Authors: Rupesh K. Mishra, Gaëlle Catanante, Akhtar Hayat, Jean-Louis Marty

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Ochratoxins (OTA) are secondary metabolites present in a wide variety of food stuff. They are dangerous by-products mainly produced by several species of storage fungi including the Aspergillus and Penicillium genera. OTA is known to have nephrotoxic, immunotoxic, teratogenic and carcinogenic effects. Thus, needs a special attention for a highly sensitive and selective detection system that can quantify these organic toxins in various matrices such as cocoa beans. This work presents a folding based aptasensors by employing an aptamer conjugated redox probe (methylene blue) specifically designed for OTA. The aptamers were covalently attached to the screen printed carbon electrodes using diazonium grafting. Upon sensing the OTA, it binds with the immobilized aptamer on the electrode surface, which induces the conformational changes of the aptamer, consequently increased in the signal. This conformational change of the aptamer before and after biosensing of target OTA could produce the distinguishable electrochemical signal. The obtained limit of detection was 0.01 ng/ml for OTA samples with recovery of up to 88% in contaminated cocoa samples.

Keywords: ochratoxin A, cocoa, DNA aptamer, labelled probe

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Authors: Kmiecik Dominik, Kobus-Cisowska Joanna, Gramza-Michalowska Anna, Marcinkowska Agata, Korczak Józef

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The relationship between the choice of a proper diet, a diet, lifestyle man and his health has been known for a long time. Because of the increase in public awareness of food ingredients and their influence on health status, measures have been taken towards the production of food, which is designed to not only eat, but also to protect against the incidence of lifestyle diseases. For this purpose, the bio active products with healthy properties was developed. Mulberry have a very high nutritional value, rich in chemical composition and many properties used in the prevention of lifestyle diseases. In addition to basic chemical components, nutrients, mulberry fruit contain compounds having a physiological effect. The aim of this study was to assess the effect of white mulberry fruit on the sensory quality of bread to be healthy diet of people suffering from anemia, diabetes, obesity and cardiovascular disease. Sensory analysis was carried out by the profile method. Intra-operative differentiators color, aroma, taste, texture, and overall assessment. Sensory analysis showed that all test trials were characterized by a uniform and concise consistency, similar in color from dark to light beige. The taste and smell of herbal characteristic was designed in an attempt to prevention of diabetes, while the other samples were characterized by a typical taste and smell of bread grain. There were no foreign taste and odor in the test bread. It was found that the addition of white mulberry fruit does not affect the sensory quality of the newly developed bioactive bread.

Keywords: mulberry, bread, bioactive, sensory analysis

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Authors: Amani Abdallah, Isam Shahrour

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The quality of drinking water is a major concern of public health. The control of this quality is generally performed in the laboratory, which requires a long time. This type of control is not adapted for accidental pollution from sudden events, which can have serious consequences on population health. Therefore, it is of major interest to develop real-time innovative solutions for the detection of accidental contamination in drinking water systems This paper presents researches conducted within the SunRise Demonstrator for ‘Smart and Sustainable Cities’ with a particular focus on the supervision of the water quality. This work aims at (i) implementing a smart water system in a large water network (Campus of the University Lille1) including innovative equipment for real-time detection of abnormal events, such as those related to the contamination of drinking water and (ii) develop a numerical modeling of the contamination diffusion in the water distribution system. The first step included verification of the water quality sensors and their effectiveness on a network prototype of 50m length. This part included the evaluation of the efficiency of these sensors in the detection both bacterial and chemical contamination events in drinking water distribution systems. An on-line optical sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in refractive index induced by injected loads of chemical (cadmium, mercury) and biological contaminations (Escherichia coli). All injected substances were detected by the sensor; the magnitude of the response depends on the type of contaminant introduced and it is proportional to the injected substance concentration.

Keywords: distribution system, drinking water, refraction index, sensor, real-time

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Authors: Nahla Elsherbiny, Ahmed Ahmed, Hamada Mohammed, Mohamed Ali

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Background: The enterococci may be considered as opportunistic agents particularly in immunocompromised patients. It is one of the top three pathogens causing many healthcare associated infections (HAIs). Resistance to several commonly used antimicrobial agents is a remarkable characteristic of most species which may carry various genes contributing to virulence. Objectives: to determine the prevalence of enterococci species in different intensive care units (ICUs) causing health care-associated infections (HAIs), intestinal carriage and environmental contamination. Also, to study the antimicrobial susceptibility pattern of the isolates with special reference to vancomycin resistance. In addition to phenotypic and genotypic detection of gelatinase, cytolysin and biofilm formation among isolates. Patients and Methods: This study was carried out in the infection control laboratory at Assiut University Hospitals over a period of one year. Clinical samples were collected from 285 patients with various (HAIs) acquired after admission to different ICUs. Rectal swabs were taken from 14 cases for detection of enterococci carriage. In addition, 1377 environmental samples were collected from the surroundings of the patients. Identification was done by conventional bacteriological methods and confirmed by analytical profile index (API). Antimicrobial sensitivity testing was performed by Kirby Bauer disc diffusion method and detection of vancomycin resistance was done by agar screen method. For the isolates, phenotypic detection of cytolysin, gelatinase production and detection of biofilm by tube method, Congo red method and microtiter plate. We performed polymerase chain reaction (PCR) for detection of some virulence genes (gelE, cylA, vanA, vanB and esp). Results: Enterococci caused 10.5% of the HAIs. Respiratory tract infection was the predominant type (86.7%). The commonest species were E.gallinarum (36.7%), E.casseliflavus (30%), E.faecalis (30%), and E.durans (3.4 %). Vancomycin resistance was detected in a total of 40% (12/30) of those isolates. The risk factors associated with acquiring vancomycin resistant enterococci (VRE) were immune suppression (P= 0.031) and artificial feeding (P= 0.008). For the rectal swabs, enterococci species were detected in 71.4% of samples with the predominance of E. casseliflavus (50%). Most of the isolates were vancomycin resistant (70%). Out of a total 1377 environmental samples, 577 (42%) samples were contaminated with different microorganisms. Enterococci were detected in 1.7% (10/577) of total contaminated samples, 50% of which were vancomycin resistant. All isolates were resistant to penicillin, ampicillin, oxacillin, ciprofloxacin, amikacin, erythromycin, clindamycin and trimethoprim-sulfamethaxazole. For the remaining antibiotics, variable percentages of resistance were reported. Cytolysin and gelatinase were detected phenotypically in 16% and 48 % of the isolates respectively. The microtiter plate method showed the highest percentages of detection of biofilm among all isolated species (100%). The studied virulence genes gelE, esp, vanA and vanB were detected in 62%, 12%, 2% and 12% respectively, while cylA gene was not detected in any isolates. Conclusions: A significant percentage of enterococci was isolated from patients and environments in the ICUs. Many virulence factors were detected phenotypically and genotypically among isolates. The high percentage of resistance, coupled with the risk of cross transmission to other patients make enterococci infections a significant infection control issue in hospitals.

Keywords: antimicrobial resistance, enterococci, ICUs, virulence factors

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Authors: Julius Denafas, Irina Kliopova, Gintaras Denafas

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Three opportunities for implementation of industrial ecology principles in the real industrial production of c-Si solar cells and modules are presented in this study. It includes: material flow dematerialisation, product modification and industrial symbiosis. Firstly, it is shown how the collaboration between R&D institutes and industry helps to achieve significant reduction of material consumption by a) refuse from phosphor silicate glass cleaning process and b) shortening of SiNx coating production step. This work was performed in the frame of Eco-Solar project, where Soli Tek R&D is collaborating together with the partners from ISC-Konstanz institute. Secondly, it was shown how the modification of solar module design can reduce the CO2 footprint for this product and enhance waste prevention. It was achieved by implementing a frameless glass/glass solar module design instead of glass/backsheet with aluminium frame. Such a design change is possible without purchasing new equipment and without loss of main product properties like efficiency, rigidity and longevity. Thirdly, industrial symbiosis in the solar cell production is possible in such case when manufacturing waste (silicon wafer and solar cell breakage) are collected, sorted and supplied as raw-materials to other companies involved in the production chain of c-Si solar cells. The obtained results showed that solar cells produced from recycled silicon can have a comparable electrical parameters like produced from standard, commercial silicon wafers. The above mentioned work was performed at solar cell producer Soli Tek R&D in the frame of H2020 projects CABRISS and Eco-Solar.

Keywords: solar cells and solar modules, manufacturing, waste prevention, recycling

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Authors: Vaidehi Murarka, Sneha Mehta, Dishant Upadhyay

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Our project topic is “Alphabet Recognition using pixel probability distribution”. The project uses techniques of Image Processing and Machine Learning in Computer Vision. Alphabet recognition is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files etc. Alphabet Recognition based OCR application is sometimes used in signature recognition which is used in bank and other high security buildings. One of the popular mobile applications includes reading a visiting card and directly storing it to the contacts. OCR's are known to be used in radar systems for reading speeders license plates and lots of other things. The implementation of our project has been done using Visual Studio and Open CV (Open Source Computer Vision). Our algorithm is based on Neural Networks (machine learning). The project was implemented in three modules: (1) Training: This module aims “Database Generation”. Database was generated using two methods: (a) Run-time generation included database generation at compilation time using inbuilt fonts of OpenCV library. Human intervention is not necessary for generating this database. (b) Contour–detection: ‘jpeg’ template containing different fonts of an alphabet is converted to the weighted matrix using specialized functions (contour detection and blob detection) of OpenCV. The main advantage of this type of database generation is that the algorithm becomes self-learning and the final database requires little memory to be stored (119kb precisely). (2) Preprocessing: Input image is pre-processed using image processing concepts such as adaptive thresholding, binarizing, dilating etc. and is made ready for segmentation. “Segmentation” includes extraction of lines, words, and letters from the processed text image. (3) Testing and prediction: The extracted letters are classified and predicted using the neural networks algorithm. The algorithm recognizes an alphabet based on certain mathematical parameters calculated using the database and weight matrix of the segmented image.

Keywords: contour-detection, neural networks, pre-processing, recognition coefficient, runtime-template generation, segmentation, weight matrix

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Authors: Hemalatha Rallapalli, K. Lal Kishore

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In comparison to systems that are typically designed to provide capabilities over a narrow frequency range through hardware elements, the next generation cognitive radios are intended to implement a broader range of capabilities through efficient spectrum exploitation. This offers the user the promise of greater flexibility, seamless roaming possible on different networks, countries, frequencies, etc. It requires true paradigm shift i.e., liberalization over a wide band of spectrum as well as a growth path to more and greater capability. This work contributes towards the design and implementation of an open spectrum access (OSA) feature to unlicensed users thus offering a frequency agile radio platform that is capable of performing spectrum sensing over a wideband. Thus, an ultra-wideband (UWB) radio, which has the intelligence of spectrum sensing only, unlike the cognitive radio with complete intelligence, is named as a Smart Software Radio (SSR). The spectrum sensing mechanism is implemented based on energy detection. Simulation results show the accuracy and validity of this method.

Keywords: cognitive radio, energy detection, software radio, spectrum sensing

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Authors: Sneha Leslie

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The current situation in the cyber world is that crimes performed by Botnets are increasing and the masterminds (botmaster) are not detectable easily. The botmaster in the botnet compromises the legitimate host machines in the network and make them bots or zombies to initiate the cyber-attacks. This paper will focus on the live detection of the botmaster in the network by using the strong framework 'metasploit', when distributed denial of service (DDOS) attack is performed by the botnet. The affected victim machine will be continuously monitoring its incoming packets. Once the victim machine gets to know about the excessive count of packets from any IP, that particular IP is noted and details of the noted systems are gathered. Using the vulnerabilities present in the zombie machines (already compromised by botmaster), the victim machine will compromise them. By gaining access to the compromised systems, applications are run remotely. By analyzing the incoming packets of the zombies, the victim comes to know the address of the botmaster. This is an effective and a simple system where no specific features of communication protocol are considered.

Keywords: bonet, DDoS attack, network security, detection system, metasploit framework

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Authors: Marko Batic, Nikola Tomasevic, Sanja Vranes

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One of the strongholds in the ubiquitous efforts related to the energy conservation and energy efficiency improvement is represented by the retrofit of high energy consumers in buildings. In general, HVAC systems represent the highest energy consumers in buildings. However they usually suffer from mal-operation and/or malfunction, causing even higher energy consumption than necessary. Various Fault Detection and Diagnosis (FDD) systems can be successfully employed for this purpose, especially when it comes to the application at a single device/unit level. In the case of more complex systems, where multiple devices are operating in the context of the same building, significant energy efficiency improvements can only be achieved through application of comprehensive FDD systems relying on additional higher level knowledge, such as their geographical location, served area, their intra- and inter- system dependencies etc. This paper presents a comprehensive FDD system that relies on the utilization of common knowledge repository that stores all critical information. The discussed system is deployed as a test-bed platform at the two at Fiumicino and Malpensa airports in Italy. This paper aims at presenting advantages of implementation of the knowledge base through the utilization of ontology and offers improved functionalities of such system through examples of typical queries and reasoning that enable derivation of high level energy conservation measures (ECM). Therefore, key SPARQL queries and SWRL rules, based on the two instantiated airport ontologies, are elaborated. The detection of high level irregularities in the operation of airport heating/cooling plants is discussed and estimation of energy savings is reported.

Keywords: airport ontology, knowledge management, ontology modeling, reasoning

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Authors: Lotfi O. Gargab, Ruichong R. Zhang

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A wave-based framework is presented for modeling seismic motion in multistory buildings and using measured response for system identification which can be utilized to extract important information regarding structure integrity. With one pair of building response at two locations, a generalized model response is formulated based on wave propagation features and expressed as frequency and time response functions denoted, respectively, as GFRF and GIRF. In particular, GIRF is fundamental in tracking arrival times of impulsive wave motion initiated at response level which is dependent on local model properties. Matching model and measured-structure responses can help in identifying model parameters and infer building properties. To show the effectiveness of this approach, the Millikan Library in Pasadena, California is identified with recordings of the Yorba Linda earthquake of September 3, 2002.

Keywords: system identification, continuous-discrete mass modeling, damage detection, post-earthquake

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Authors: Dhanuja S. Patil, Sanjay B. Waykar

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Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

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Authors: Agnieszka Stempniewicz, Piotr Ceranowicz, Wojciech Macyk, Jakub Cieszkowski, Beata Kuśnierz-Cabała, Katarzyna Gałązka, Zygmunt Warzecha

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Objectives: There are numerous strategies for the prevention or treatment of oral mucositis. However, their effectiveness is limited and does not correspond to expectations. Recent studies have shown that obestatin exhibits a protective effect and accelerates the healing of gastrointestinal mucosa. The aim of the present study was to examine the influence of obestatin administration on oral ulcers in rats. Methods: lingual ulcers were induced by the use of acetic acid. Rats were treated twice a day intraperitoneally with saline or obestatin(4, 8, or 16 nmol/kg/dose) for five days. The study determined: lingual mucosa morphology, cell proliferation, mucosal blood flow, and mucosal pro-inflammatory interleukin-1β level(IL-1β). Results: In animals without induction of oral ulcers, treatment with obestatin was without any effect. Obestatin administration in rats with lingual ulcers increased the healing rate of these ulcers. Obestatin given at the dose of 8 or 16 nmol/kg/dose caused the strongest and similar therapeutic effect. This result was associated with a significant increase in blood flow and cell proliferation in gingival mucosa, as well as a significant decrease in IL-1β level. Conclusions: Obestatin accelerates the healing of lingual ulcers in rats. This therapeutic effect is well-correlated with an increase in blood flow and cell proliferation in oral mucosa, as well as a decrease in pro-inflammatory IL-1β levels. Obestatin is a potentially useful candidate for the prevention and treatment of oral mucositis. Acknowledgment: Agnieszka Stempniewicz acknowledges the support of InterDokMed project no. POWR.03.02.00- 00-I013/16.

Keywords: oral mucositis, ulcers, obestatin, lingual mucosa

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Authors: Catherine Bradshaw

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Educators often report difficulty managing behavior problems and other mental health concerns that students display at school. These concerns also interfere with the learning process and can create distraction for teachers and other students. As such, schools play an important role in both preventing and intervening with students who experience these types of challenges. A number of models have been proposed to serve as a framework for delivering prevention and early intervention services in schools. One such model is called Positive Behavioral Interventions and Supports (PBIS), which has been scaled-up to over 26,000 schools in the U.S. and many other countries worldwide. PBIS aims to improve a range of student outcomes through early detection of and intervention related to behavioral and mental health symptoms. PBIS blends and applies social learning, behavioral, and organizational theories to prevent disruptive behavior and enhance the school’s organizational health. PBIS focuses on creating and sustaining tier 1 (universal), tier 2 (selective), and tier 3 (individual) systems of support. Most schools using PBIS have focused on the core elements of the tier 1 supports, which includes the following critical features. The formation of a PBIS team within the school to lead implementation. Identification and training of a behavioral support ‘coach’, who serves as a on-site technical assistance provider. Many of the individuals identified to serve as a PBIS coach are also trained as a school psychologist or guidance counselor; coaches typically have prior PBIS experience and are trained to conduct functional behavioral assessments. The PBIS team also identifies a set of three to five positive behavioral expectations that are implemented for all students and by all staff school-wide (e.g., ‘be respectful, responsible, and ready to learn’); these expectations are posted in all settings across the school, including in the classroom, cafeteria, playground etc. All school staff define and teach the school-wide behavioral expectations to all students and review them regularly. Finally, PBIS schools develop or adopt a school-wide system to reward or reinforce students who demonstrate those 3-5 positive behavioral expectations. Staff and administrators create an agreed upon system for responding to behavioral violations that include definitions about what constitutes a classroom-managed vs. an office-managed discipline problem. Finally, a formal system is developed to collect, analyze, and use disciplinary data (e.g., office discipline referrals) to inform decision-making. This presentation provides a brief overview of PBIS and reports findings from a series of four U.S. based longitudinal randomized controlled trials (RCTs) documenting the impacts of PBIS on school climate, discipline problems, bullying, and academic achievement. The four RCTs include 80 elementary, 40 middle, and 58 high schools and results indicate a broad range of impacts on multiple student and school-wide outcomes. The session will highlight lessons learned regarding PBIS implementation and scale-up. We also review the ways in which PBIS can help educators and school leaders engage in data-based decision-making and share data with other decision-makers and stakeholders (e.g., students, parents, community members), with the overarching goal of increasing use of evidence-based programs in schools.

Keywords: positive behavioral interventions and supports, mental health, randomized trials, school-based prevention

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Authors: Abdullah Malek, Muzn Al Khaldi, Lian Odeh, Atheer Tariq, Mohammad Al Fardan, Hiba Barqawi

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H. pylori bacterium is a known underlying agent for gastritis, peptic ulcer disease, and gastric cancer and is believed to infect half of the world’s population. Even with the ubiquity of H. pylori bacterium, there is lack of knowledge regarding its modes of transmission, associated diseases, carcinogenic effect and means of prevention; especially in the UAE. A cross sectional study of 500 participants, of which 58% (n= 289) of the respondents were female, and 42% (n=210) were male, was conducted in Sharjah to assess the knowledge, and explore the attitudes and practices among UAE residents towards Helicobacter Pylori and its associated PUD as well as its carcinogenic nature. A structured self-administered questionnaire was distributed to the target population to establish their demographic background and selected aspects of their lifestyle. General knowledge about H. Pylori was poor, only 24.6% stated they have heard of H. pylori. Attitudes towards prevention and practices were relatively poor as well. Subjects who suffered from severe symptoms (ALARM symptoms) had significantly lower habit scores than those with mild and moderate symptoms (p=0.0078**). To the authours’ knowledge, no previous studies were conducted in the United Arab Emirates regarding the epidemiology of the infection to detect the extent of H. Pylori’s impact on the public health. The results of this study can be used to draw conclusions about the average knowledge of the UAE population regarding H. pylori. It can also be a starting point to devise new education programs and campaigns that raise awareness of this health issue which could be easily avoided with early diagnosis and antibiotic treatment.

Keywords: chronic gastritis, community health, gastric cancer, Helicobacter pylori, peptic ulcers

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Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

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Authors: Anthony Idowu Ajayi, Ezebunwa Ethelbert Nwokocha, Wilson Akpan, Oladele Vincent Adeniyi

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Studies on emergency contraception (EC) mostly utilise quantitative methods and focus on medically approved drugs for the prevention of unwanted pregnancies. This methodological bias necessarily obscures insider perspectives on sexual behaviour, particularly on why specific methods are utilized by women who seek to prevent unplanned pregnancies. In order to privilege this perspective, with a view to further enriching the discourse and policy on the prevention and management of unplanned pregnancies, this paper brings together the findings from several focus groups and in-depth interviews conducted amongst unmarried female undergraduate students in two Nigerian universities. The study found that while the research participants had good knowledge of the consequences of unprotected sexual intercourses - with abstinence and condom widely used - participants’ willingness to rely only on medically sound measures to prevent unwanted pregnancies was not always mediated by such knowledge. Some of the methods favored by participants appeared to be those commonly associated with people of low socio-economic status in the society where the study was conducted. Medically unsafe concoctions, some outright dangerous, were widely believed to be efficacious in preventing unwanted pregnancy. Furthermore, respondents’ narratives about their sexual behaviour revealed that inadequate sex education, socio-economic pressures, and misconceptions about the efficacy of “crude” emergency contraception methods were all interrelated. The paper therefore suggests that these different facets of the unplanned pregnancy problem should be the focus of intervention.

Keywords: unplanned pregnancy, unsafe abortion, emergency contraception, concoctions

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Authors: Naser Ahmadi Sani, Karim Solaimani, Lida Razaghnia, Jalal Zandi

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In recent decades, rapid and incorrect changes in land-use have been associated with consequences such as natural resources degradation and environmental pollution. Detecting changes in land-use is one of the tools for natural resource management and assessment of changes in ecosystems. The target of this research is studying the land-use changes in Haraz basin with an area of 677000 hectares in a 15 years period (1996 to 2011) using LANDSAT data. Therefore, the quality of the images was first evaluated. Various enhancement methods for creating synthetic bonds were used in the analysis. Separate training sites were selected for each image. Then the images of each period were classified in 9 classes using supervised classification method and the maximum likelihood algorithm. Finally, the changes were extracted in GIS environment. The results showed that these changes are an alarm for the HARAZ basin status in future. The reason is that 27% of the area has been changed, which is related to changing the range lands to bare land and dry farming and also changing the dense forest to sparse forest, horticulture, farming land and residential area.

Keywords: Haraz basin, change detection, land-use, satellite data

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Authors: Alaa Abdullah Altaee

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This paper presents an adaptive decision feedback equalizer (ADFE) for multi-Gbps serial links utilizing a fix-step error signal extracted from cross-points of received data symbols. The extracted signal is generated based on violation of received data symbols with minimum detection requirements at the clock and data recovery (CDR) stage. The iterations of the adaptation process search for the optimum feedback tap coefficients to maximize the data eye-opening and minimize the adaptation convergence time. The effectiveness of the proposed architecture is validated using the simulation results of a serial link designed in an IBM 130 nm 1.2V CMOS technology. The data link with variable channel lengths is analyzed using Spectre from Cadence Design Systems with BSIM4 device models.

Keywords: adaptive DFE, CMOS equalizer, error detection, serial links, timing jitter, wire-line communication

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Authors: Zzibu Mark Brian

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As software systems become increasingly complex, ensuring code security is a growing concern. Traditional vulnerability detection methods often rely on manual code reviews or static analysis tools, which can be time-consuming and prone to errors. This paper presents a distinct approach to enhancing code security by leveraging artificial intelligence (AI) and machine learning (ML) techniques. Our proposed system utilizes a combination of natural language processing (NLP) and deep learning algorithms to identify and classify vulnerabilities in real-world codebases. By analyzing vast amounts of open-source code data, our AI-powered tool learns to recognize patterns and anomalies indicative of security weaknesses. We evaluated our system on a dataset of over 10,000 open-source projects, achieving an accuracy rate of 92% in detecting known vulnerabilities. Furthermore, our tool identified previously unknown vulnerabilities in popular libraries and frameworks, demonstrating its potential for improving software security.

Keywords: AI, machine language, cord security, machine leaning

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