Search results for: skin detection

Authors: Chia-Yen Lee, Hao-Jen Wang, Jhih-Hao Lai

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In recent years, infrared (IR) imaging has been considered as a potential tool to assess the efficacy of chemotherapy and early detection of breast cancer. Regions of tumor growth with high metabolic rate and angiogenesis phenomenon lead to the high temperatures. Observation of differences between the heat maps in long term is useful to help assess the growth of breast cancer cells and detect breast cancer earlier, wherein the multi-time infrared image alignment technology is a necessary step. Representative feature points detection and matching are essential steps toward the good performance of image registration and quantitative analysis. However, there is no clear boundary on the infrared images and the subject's posture are different for each shot. It cannot adhesive markers on a body surface for a very long period, and it is hard to find anatomic fiducial markers on a body surface. In other words, it’s difficult to detect and match features in an IR sequence images. In this study, automated feature detection and matching algorithms with two type of automatic feature points (i.e., vascular branch points and modified Harris corner) are developed respectively. The preliminary results show that the proposed method could identify the representative feature points on the IR breast images successfully of 98% accuracy and the matching results of 93% accuracy.

Keywords: Harris corner, infrared image, feature detection, registration, matching

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Authors: Riya Roy, Anjana Bhasi

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Column-supported embankments provide a practical and efficient solution for construction on soft soil due to the low cost and short construction times. In the recent years, geosynthetic have been used in combination with column systems to support embankments. The load transfer mechanism in these systems is a combination of soil arching effect, which occurs between columns and membrane effect of the geosynthetic. This paper aims at the study of soil arching effect on columnar embankments using finite element software, ABAQUS. An axisymmetric finite element model is generated and using this model, parametric studies are carried out. Thus the effects of various factors such as height of embankment fill, elastic modulus of pile and tensile stiffness of geosynthetic, on soil arching have been studied. The development of negative skin friction along the pile-soil interface have also been studied and the results obtained from this study are compared with the current design methods.

Keywords: ABAQUS, geosynthetic, negative skin friction, soil arching

Procedia PDF Downloads 356

Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi

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The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.

Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks

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Authors: M. Riaz, Inamur Rehman, Abhishek Sharma

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The present work is the development of an experimental method for determining the thermal diffusivity variations with temperature of selected regular shaped solid fruits and vegetables subjected to forced convection cooling. Experimental investigations were carried on the sample chosen (potato and brinjal), which is approximately of spherical geometry. The variation of temperature within the food product is measured at several locations from centre to skin, under forced convection environment using a deep freezer, maintained at -10°C.This method uses one dimensional Fourier equation applied to regular shapes. For this, the experimental temperature data obtained from cylindrical and spherical shaped products during pre-cooling was utilised. Such temperature and thermal diffusivity profiles can be readily used with other information such as degradation rate, etc. to evaluate thermal treatments based on cold air cooling methods for storage of perishable food products.

Keywords: thermal diffusivity, skin temperature, precooling, forced convection, regular shaped

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

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In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

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Authors: Hadj Abd El Kader Benghenia, Fethi Bereksi Reguig

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In clinical medicine, blood pressure, raised blood hemodynamic monitoring is rich pathophysiological information of cardiovascular system, of course described through factors such as: blood volume, arterial compliance and peripheral resistance. In this work, we are interested in analyzing these signals to propose a detection algorithm to delineate the different sequences and especially systolic blood pressure (SBP), diastolic blood pressure (DBP), and the wave and dicrotic to do their analysis in order to extract the cardiovascular parameters.

Keywords: blood pressure, SBP, DBP, detection algorithm

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

Procedia PDF Downloads 128

Authors: Khaled M. Mohamed

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Background: P-Phenylenediamine (PPD) is used in the manufacture of hair dyes and skin decoration. In some developing countries, suicidal, homicidal and accidental cases by PPD were recorded. In this work, a sensitive LC-MS/MS method for determination of PPD and its metabolites N-acetyl-p-phenylenediamine (MAPPD) and N,N-diacetyl-p-phenylenediamine (DAPPD) in human urine has been developed and validated. Methods: PPD, MAPPD and DAPPD were extracted from urine by methylene chloride at alkaline pH. Acetanilide was used as internal standard (IS). The analytes and IS were separated on an Eclipse XDB- C18 column (150 X 4.6 mm, 5 µm) using a mobile phase of acetonitrile-1% formic acid in gradient elution. Detection was performed by LC-MS/MS using electrospray positive ionization under multiple reaction-monitoring mode. The transition ions m/z 109 → 92, m/z 151 → 92, m/z 193 → 92, and m/z 136 → 77 were selected for the quantification of PPD, MAPPD, DAPPD, and IS, respectively. Results: Calibration curves were linear in the range 10–2000 ng/mL for all analytes. The mean recoveries for PPD, MAPPD and DAPPD were 57.62, 74.19 and 50.99%, respectively. Intra-assay and inter-assay imprecisions were within 1.58–9.52% and 5.43–9.45% respectively for PPD, MAPPD and DAPPD. Inter-assay accuracies were within -7.43 and 7.36 for all compounds. PPD, MAPPD and DAPPD were stable in urine at –20 degrees for 24 hours. Conclusions: The method was successfully applied to the analysis of PPD, MAPPD and DAPPD in urine samples collected from suicidal cases.

Keywords: p-Phenylenediamine, metabolites, urine, LC-MS/MS, validation

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

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Authors: Mohammad Arabi

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The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: Aysun Sezer

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Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Adnan A. Y. Mustafa

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Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.

Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping

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Authors: Devendra Gupta, Vikash Arya, Prabhat K. Singh

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Background: Ultrasound guidance could be beneficial in placing central venous catheters by improving the success rate, reducing the number of needle passes, and decreasing complications. Central venous cannulation set has a single puncture needle of a fixed length of 6.4 cm. However, the average distance of midpoint of IJV to the skin is around 1 cm to 2 cm. The long length needle has tendency to go in depth more than required and this is very common during learning period of any individual. Therefore, we devised a long needle with a guard which can be adjusted according to the required length. Methods: After approval from the institute ethics committee and patient’s written informed consent, a prospective, randomized, single-blinded controlled study was conducted. Adult patient aged of both sexes with ASA grade 1-2 undergoing surgery requiring internal jugular venous (IJV) access was included. After intubation, the head was rotated to the contralateral side at 30 degree head rotation on the position of the right IJV. The transducer probe a 6.5 to 13-MHz linear transducer (Sonosite, USA) had been placed at the apex of triangle with minimal pressure to avoid IJV compression. The distance from skin to midpoint of the right IJV and skin to anterior wall of Common Carotid Artery (CCA) had been done using B-mode duplex sonography with a 6.5 to 13-MHz linear transducer. Depending upon the results of randomization 420 patients had been divided into two groups of equal numbers (n=210). Group 1. USG guided right sided IJV cannulation was done with conventional (6.4 cm) needle; and Group 2. USG guided right sided IJV cannulation was done with conventional (6.4 cm) needle with guard fixed to a required length (length between skin and midpoint of IJV) by an experienced anesthesiologist. Independent observer has noted the number of attempts and occurrence of complications (CCA puncture, pneumothorax or adjacent tissue damage). Results: Demographic data were similar in both the group. The groups were comparable when considered for relationship of IJV to CCA. There was no significant difference between groups as regard to distance of midpoint of IJV to the skin (p<0.05). IJV cannulation was successfully done in single attempts in 180 (85.7%), in two attempts in 27 (12.9%) and three attempts in 3 (1.4%) in group I, whereas in single attempt in 207 (98.6%) and second attempts in 3 (1.4%) in group II (p <0.000). Incidence of carotid artery puncture was significantly more in group I (7.1%) compared to group II (0%) (p<0.000). Incidence of adjacent tissue puncture was significantly more in group I (8.6%) compared to group II (0%) (p<0.000). Conclusion: Therefore IJV catheterization using guard over the needle at predefined length with the help of real-time ultrasound results in better success rates and lower immediate complications.

Keywords: ultrasound guided, internal jugular vein cannulation, measured guided needle, common carotid artery puncture

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Authors: Cebrail Çiflikli, Emre Öner Tartan

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Parallel to the fast worldwide increase of elderly population and spreading unhealthy life habits, there is a significant rise in the number of patients and health problems. The supervision of people who have health problems and oversight in detection of people who have potential risks, bring a considerable cost to health system and increase workload of physician. To provide an efficient solution to this problem, in the recent years mobile applications have shown their potential for wide usage in health monitoring. In this paper we present an Android mobile application that records and evaluates ECG signal using Pan-Tompkins algorithm for QRS detection. The application model includes an alarm mechanism that is proposed to be used for sending message including abnormality information and location information to health supervisor.

Keywords: Android mobile application, ECG monitoring, QRS detection, Pan-Tompkins Algorithm

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Authors: Ilia Zharikov, Philipp Nikitin, Ilia Vasiliev, Vladimir Dokholyan

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With the increasing popularity of document analysis and recognition systems, text detection (TD) and optical character recognition (OCR) in document images become challenging tasks. However, according to our best knowledge, no publicly available datasets for these particular problems exist. In this paper, we introduce a Distorted Document Images dataset (DDI-100) and provide a detailed analysis of the DDI-100 in its current state. To create the dataset we collected 7000 unique document pages, and extend it by applying different types of distortions and geometric transformations. In total, DDI-100 contains more than 100,000 document images together with binary text masks, text and character locations in terms of bounding boxes. We also present an analysis of several state-of-the-art TD and OCR approaches on the presented dataset. Lastly, we demonstrate the usefulness of DDI-100 to improve accuracy and stability of the considered TD and OCR models.

Keywords: document analysis, open dataset, optical character recognition, text detection

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Authors: Recep Kesli, Huseyin Bilgin, Yasar Unlu, Gokhan Gungor

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Aim: The aim of this study was to compare diagnostic values of two different molecular-based tests (GenoType® HelicoDR ve Seeplex® H. pylori-ClaR- ACE Detection) in detection presence of the H. pylori from gastric biopsy specimens. In addition to this also was aimed to determine resistance ratios of H. pylori strains against to clarytromycine and quinolone isolated from gastric biopsy material cultures by using both the genotypic (GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection) and phenotypic (gradient strip, E-test) methods. Material and methods: A total of 266 patients who admitted to Konya Education and Research Hospital Department of Gastroenterology with dyspeptic complaints, between January 2011-June 2013, were included in the study. Microbiological and histopathological examinations of biopsy specimens taken from antrum and corpus regions were performed. The presence of H. pylori in all the biopsy samples was investigated by five differnt dignostic methods together: culture (C) (Portagerm pylori-PORT PYL, Pylori agar-PYL, GENbox microaer, bioMerieux, France), histology (H) (Giemsa, Hematoxylin and Eosin staining), rapid urease test (RUT) (CLOtest, Cimberly-Clark, USA), and two different molecular tests; GenoType® HelicoDR, Hain, Germany, based on DNA strip assay, and Seeplex ® H. pylori -ClaR- ACE Detection, Seegene, South Korea, based on multiplex PCR. Antimicrobial resistance of H. pylori isolates against clarithromycin and levofloxacin was determined by GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, and gradient strip (E-test, bioMerieux, France) methods. Culture positivity alone or positivities of both histology and RUT together was accepted as the gold standard for H. pylori positivity. Sensitivity and specificity rates of two molecular methods used in the study were calculated by taking the two gold standards previously mentioned. Results: A total of 266 patients between 16-83 years old who 144 (54.1 %) were female, 122 (45.9 %) were male were included in the study. 144 patients were found as culture positive, and 157 were H and RUT were positive together. 179 patients were found as positive with GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection together. Sensitivity and specificity rates of studied five different methods were found as follows: C were 80.9 % and 84.4 %, H + RUT were 88.2 % and 75.4 %, GenoType® HelicoDR were 100 % and 71.3 %, and Seeplex ® H. pylori -ClaR- ACE Detection were, 100 % and 71.3 %. A strong correlation was found between C and H+RUT, C and GenoType® HelicoDR, and C and Seeplex ® H. pylori -ClaR- ACE Detection (r:0.644 and p:0.000, r:0.757 and p:0.000, r:0.757 and p:0.000, respectively). Of all the isolated 144 H. pylori strains 24 (16.6 %) were detected as resistant to claritromycine, and 18 (12.5 %) were levofloxacin. Genotypic claritromycine resistance was detected only in 15 cases with GenoType® HelicoDR, and 6 cases with Seeplex ® H. pylori -ClaR- ACE Detection. Conclusion: In our study, it was concluded that; GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection was found as the most sensitive diagnostic methods when comparing all the investigated other ones (C, H, and RUT).

Keywords: Helicobacter pylori, GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, antimicrobial resistance

Procedia PDF Downloads 139

Authors: Awol Mekonnen, Temesgen Sidamo, Epherm Engdawork, Kaleab Asresb

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Ethnopharmacological Relevance: The leaves of Kalanchoe petitiana A. Rich (Crassulaceae) are used in Ethiopian folk medicine for treatment of evil eye, fractured surface for bone setting and several skin disorders including for the treatment of sores, boils, and malignant wounds. Aim of the Study: In order to scientifically prove the claimed utilization of the plant, the effects of the extracts and the fractions were investigated using in vivo excision, incision and dead space wound models. Materials and Method: Mice were used for wound healing study, while rats and rabbit were used for skin irritation test. For studying healing activity, 80% methanolic extract and the fractions were formulated in strength of 5% and 10%, either as ointment (hydroalcoholic extract, aqueous and methanol fractions) or gel (chloroform fraction). Oral administration of the crude extract was used for dead space model. Negative controls were treated either with simple ointment or sodium carboxyl methyl cellulose xerogel, while positive controls were treated with nitrofurazone (0.2 w/v) skin ointment. Negative controls for dead space model were treated with 1% carboxy methyl cellulose. Parameters, including rate of wound contraction, period of complete epithelializtion, hydroxyproline contents and skin breaking strength were evaluated. Results: Significant wound healing activity was observed with ointment formulated from the crude extract at both 5% and 10% concentration (p<0.01) compared to controls in both excision and incision models. In dead space model, 600 mg/kg (p<0.01), but not 300 mg/kg, significantly increased hydroxyproline content. Fractions showed variable effect, with the chloroform fraction lacking any significant effect. Both 5% and 10% formulations of the aqueous and methanolic fractions significantly increased wound contraction, decreased epithelializtion time and increased hydroxyproline content in excision wound model (p<0.05) as compared to controls. These fractions were also endowed with higher skin breaking strength in incision wound model (p<0.01). Conclusions: The present study provided evidence that the leaves of Kalanchoe petitiana A. Rich possess remarkable wound healing activities supporting the folkloric assertion of the plant. Fractionation revealed that polar or semi-polar compound may play vital role, as both aqueous and methanolic fractions were endowed with wound healing activity.

Keywords: wound healing, Kalanchoae petitiana, excision wound, incision wound, dead space model

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Authors: Issa Qabaja, Fadi Thabtah

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Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

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Authors: Kashif Jabbar

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Milk adulteration is a global concern, and the current study was plan to synthesize Selenium nanoparticles by green method using plant extract of garlic, Allium Sativum, and to characterize Selenium nanoparticles through different analytical techniques and to apply Selenium nanoparticles as fast and easy technique for the detection of salicylic acid in milk. The highly selective, sensitive, and quick interference green synthesis-based sensing of possible milk adulterants i.e., salicylic acid, has been reported here. Salicylic acid interacts with nanoparticles through strong bonding interactions, hence resulting in an interruption within the formation of selenium nanoparticles which is confirmed by UV-VIS spectroscopy, scanning electron microscopy, and x-ray diffraction. This interaction in the synthesis of nanoparticles resulted in transmittance wavelength that decrease with the increasing amount of salicylic acid, showing strong binding of selenium nanoparticles with adulterant, thereby permitting in-situ fast detection of salicylic acid from milk having a limit of detection at 10-3 mol and linear coefficient correlation of 0.9907. Conclusively, it can be draw that colloidal selenium could be synthesize successfully by garlic extract in order to serve as a probe for fast and cheap testing of milk adulteration.

Keywords: adulteration, green synthesis, selenium nanoparticles, salicylic acid, aggregation

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Authors: Ansah Iris Baffour, Dong-Ho Kim, Sung-Gyu Park

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The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus and is gradually shifting to an endemic phase which implies the outbreak is far from over and will be difficult to eradicate. Global cooperation has led to unified precautions that aim to suppress epidemiological spread (e.g., through travel restrictions) and reach herd immunity (through vaccinations); however, the primary strategy to restrain the spread of the virus in mass populations relies on screening protocols that enable rapid on-site diagnosis of infections. Herein, we employed surface enhanced Raman spectroscopy (SERS) for the rapid detection of SARS-CoV-2 lysate on an Au-modified Au nanodimple(AuND)electrode. Through in situone-pot Au electrodeposition on the AuND electrode, Au-lysate nanocomposites were synthesized, generating3D internal hotspots for large SERS signal enhancements within 30 s of the deposition. The capture of lysate into newly generated plasmonic nanogaps within the nanocomposite structures enhanced metal-spike protein contact in 3D spaces and served as hotspots for sensitive detection. The limit of detection of SARS-CoV-2 lysate was 5 x 10-2 PFU/mL. Interestingly, ultrasensitive detection of the lysates of influenza A/H1N1 and respiratory syncytial virus (RSV) was possible, but the method showed ultimate selectivity for SARS-CoV-2 in lysate solution mixtures. We investigated the practical application of the approach for rapid on-site diagnosis by detecting SARS-CoV-2 lysate spiked in normal human saliva at ultralow concentrations. The results presented demonstrate the reliability and sensitivity of the assay for rapid diagnosis of COVID-19.

Keywords: label-free detection, nanocomposites, SARS-CoV-2, surface-enhanced raman spectroscopy

Procedia PDF Downloads 99

Authors: Seongho Jang

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Objective: To investigate an appropriate depth of needle insertion during trigger point injection into the rhomboid major muscle. Methods: Sixty-two patients who visited our department with shoulder or upper back pain participated in this study. The distance between the skin and the rhomboid major muscle (SM) and the distance between the skin and rib (SB) were measured using ultrasonography. The subjects were divided into 3 groups according to BMI: BMI less than 23 kg/m2 (underweight or normal group); 23 kg/m2 or more to less than 25 kg/m2 (overweight group); and 25 kg/m2 or more (obese group). The mean ±standard deviation (SD) of SM and SB of each group were calculated. A range between mean+1 SD of SM and the mean-1 SD of SB was defined as a safe margin. Results: The underweight or normal group’s SM, SB, and the safe margin were 1.2±0.2, 2.1±0.4, and 1.4 to 1.7 cm, respectively. The overweight group’s SM and SB were 1.4±0.2 and 2.4±0.9 cm, respectively. The safe margin could not be calculated for this group. The obese group’s SM, SB, and the safe margin were 1.8±0.3, 2.7±0.5, and 2.1 to 2.2 cm, respectively. Conclusion: This study will help us to set the standard depth of safe needle insertion into the rhomboid major muscle in an effective manner without causing any complications.

Keywords: pneumothorax, rhomboid major muscle, trigger point injection, ultrasound

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Authors: Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

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With the increase in popularity of mobile devices, new and varied forms of malware have emerged. Consequently, the organizations for cyberdefense have echoed the need to deploy more effective defensive schemes adapted to the challenges posed by these recent monitoring environments. In order to contribute to their development, this paper presents a malware detection strategy for mobile devices based on sequence alignment algorithms. Unlike the previous proposals, only the system calls performed during the startup of applications are studied. In this way, it is possible to efficiently study in depth, the sequences of system calls executed by the applications just downloaded from app stores, and initialize them in a secure and isolated environment. As demonstrated in the performed experimentation, most of the analyzed malicious activities were successfully identified in their boot processes.

Keywords: android, information security, intrusion detection systems, malware, mobile devices

Procedia PDF Downloads 280

Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

Abstract:

Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: Aika Umemuro, Mitsuru Sato, Mizuki Narita, Saya Hori, Saya Sakurai, Tomomi Nakayama, Ayano Nakazawa, Toshihiro Ogura

Abstract:

Mammography requires the detection of very small calcifications, and physicians search for microcalcifications by magnifying the images as they read them. The mouse is necessary to zoom in on the images, but this can be tiring and distracting when many images are read in a single day. Therefore, an image magnification system combining an eye-detector and a simple electroencephalograph (EEG) scanner was devised, and its operability was evaluated. Two experiments were conducted in this study: the measurement of eye-detection error using an eye-detector and the measurement of the time required for image magnification using a simple EEG scanner. Eye-detector validation showed that the mean distance of eye-detection error ranged from 0.64 cm to 2.17 cm, with an overall mean of 1.24 ± 0.81 cm for the observers. The results showed that the eye detection error was small enough for the magnified area of the mammographic image. The average time required for point magnification in the verification of the simple EEG scanner ranged from 5.85 to 16.73 seconds, and individual differences were observed. The reason for this may be that the size of the simple EEG scanner used was not adjustable, so it did not fit well for some subjects. The use of a simple EEG scanner with size adjustment would solve this problem. Therefore, the image magnification system using the eye-detector and the simple EEG scanner is useful.

Keywords: EEG scanner, eye-detector, mammography, observers

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

Abstract:

Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

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Authors: Alhage Drammeh

Abstract:

The research is on the occurrence of certain diseases among children in rural and far-flung parts of the Gambia and the extent to which they are caused by lack of access to clean water. A baseline survey was used to discover, describe, and explain the actual processes. The paper explains the purpose of the research, which is majorly to improve the health condition of children, especially those living in rural communities. The paper also gives a brief overview of the socio-economic situation of The Gambia, emphasizing its status as a Least Developed Country (LDC) and the majority of its population living below the poverty line, with women and children hardest hit. The research used as case studies of two rural communities in the Gambia -Basse Dampha Kunda Village and Foni Besse. Data was collected through oral interviews and medical tests conducted among people in both villages, with an emphasis on children. The demographic detail of those tested is tabulated for a clearer understanding. The results were compared, revealing that skin rashes, hepatitis, and certain other diseases are more prevalent in communities lacking access to safe drinking water. These results were also presented in a tabular form. The study established how some policy failures and neglect on the part of the Government of The Gambia are imperiling the health of many rural dwellers in the country, the most glaring being that the research team was unable to test water samples collected from the two communities, as there are no laboratory reagents for testing water anywhere in The Gambia. Many rural communities lack basic amenities, especially clean and potable water, as well as health facilities. The study findings also highlighted the need for healthcare providers and medical NGOs to voice the plight of rural dwellers and collaborate with the government to set up health facilities in rural areas of The Gambia.

Keywords: jaundice, skin rashes, children, rural communities, the Gambia, causes

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Authors: Usama Shahid

Abstract:

Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

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Authors: Zhang Xingnan, Huang Jingjia, Feng Yue, Burra Venkata Durga Kumar

Abstract:

This paper proposes a comprehensive approach to mitigate ROP (Return-Oriented Programming) attacks by combining internal operating system protection mechanisms and hardware-assisted techniques. Through extensive literature review, we identify the effectiveness of ASLR (Address Space Layout Randomization) and LBR (Last Branch Record) in preventing ROP attacks. We present a process involving buffer overflow detection, hardware-assisted ROP attack detection, and the use of Turing detection technology to monitor control flow behavior. We envision a specialized tool that views and analyzes the last branch record, compares control flow with a baseline, and outputs differences in natural language. This tool offers a graphical interface, facilitating the prevention and detection of ROP attacks. The proposed approach and tool provide practical solutions for enhancing software security.

Keywords: operating system, ROP attacks, returning-oriented programming attacks, ASLR, LBR, CFI, DEP, code randomization, hardware-assisted CFI

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Authors: Lucas Kurth de Azambuja, Loise Silveira da Silva, Gean Vitor Salmoria, Darlan Dallacosta, Carlos Rodrigo de Mello Roesler

Abstract:

This work provides a physicochemical and thermal characterization assessment of three different hyaluronic acid (HA)-based injections used for rejuvenation skin treatments. The three products analyzed are manufactured by the same manufacturer and commercialized for application on different skin levels. According to the manufacturer, all three HA-based injections are crosslinked and have a concentration of 23 mg/mL of HA, and 0.3% of lidocaine. Samples were characterized by Fourier-transformed infrared (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and scanning electron microscope (SEM) techniques. FTIR analysis resulted in a similar spectrum when comparing the different products. DSC analysis demonstrated that the fusion points differ in each product, with a higher fusion temperature observed in specimen A, which is used for subcutaneous applications, when compared with B and C, which are used for the middle dermis and deep dermis, respectively. TGA data demonstrated a considerable mass loss at 100°C, which means that the product has more than 50% of water in its composition. TGA analysis also showed that Specimen A had a lower mass loss at 100°C when compared to Specimen C. A mass loss of around 220°C was observed on all samples, characterizing the presence of hyaluronic acid. SEM images displayed a similar structure on all samples analyzed, with a thicker layer for Specimen A when compared with B and C. This series of analyses demonstrated that, as expected, the physicochemical and thermal properties of the products differ according to their application. Furthermore, to better characterize the crosslinking degree of each product and their mechanical properties, a set of different techniques should be applied in parallel to correlate the results and, thereby, relate injection application with material properties.

Keywords: hyaluronic acid, characterization, soft-tissue fillers, injectable gels

Procedia PDF Downloads 77