Search results for: contextual toxicity detection

Authors: A. Ojha, Y. K. Gupta

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Organophosphate (OP) pesticides are among the most widely used synthetic chemicals for controlling a wide variety of pests throughout the world. Chlorpyrifos (CPF), methyl parathion (MPT), and malathion (MLT) are among the most extensively used OP pesticides in India. DNA strand breaks and DNA-protein crosslinks (DPC) are toxic lesions associated with the mechanisms of toxicity of genotoxic compounds. In the present study, we have examined the potential of CPF, MPT, and MLT individually and in combination, to cause DNA strand breakage and DPC formation. Peripheral blood lymphocytes of rat were exposed to 1/4 and 1/10 LC50 dose of CPF, MPT, and MLT for 2, 4, 8, and 12h. The DNA strand break was measured by the comet assay and expressed as DNA damage index while DPC estimation was done by fluorescence emission. There was significantly marked increase in DNA damage and DNA-protein crosslink formation in time and dose dependent manner. It was also observed that MPT caused the highest level of DNA damage as compared to other studied OP compounds. Thus, from present study, we can conclude that studied pesticides have genotoxic potential. The pesticides mixture does not potentiate the toxicity of each other. Nonetheless, additional in vivo data are required before a definitive conclusion can be drawn regarding hazard prediction to humans.

Keywords: organophosphate, pesticides, DNA damage, DNA protein crosslink, genotoxic

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Authors: Agastya Pratap Singh

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This review paper examines the emerging applications of generative adversarial networks (GANs) in the fields of image recognition and anomaly detection. With the rapid growth of digital image data, the need for efficient and accurate methodologies to identify and classify images has become increasingly critical. GANs, known for their ability to generate realistic data, have gained significant attention for their potential to enhance traditional image recognition systems and improve anomaly detection performance. The paper systematically analyzes various GAN architectures and their modifications tailored for image recognition tasks, highlighting their strengths and limitations. Additionally, it delves into the effectiveness of GANs in detecting anomalies in diverse datasets, including medical imaging, industrial inspection, and surveillance. The review also discusses the challenges faced in training GANs, such as mode collapse and stability issues, and presents recent advancements aimed at overcoming these obstacles.

Keywords: generative adversarial networks, image recognition, anomaly detection, synthetic data generation, deep learning, computer vision, unsupervised learning, pattern recognition, model evaluation, machine learning applications

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Authors: Sudkate Chaiyo, Weena Siangproh, Orawon Chailapakul, Kurt Kalcher

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In the present work, a simple and sensitive non-enzymatic electrochemical detection of glucose in disposable paper-based sensor was developed at ionic liquid/graphene/cobalt phthalocyanine composite (IL/G/CoPc) modified electrode. The morphology of the fabricated composite was characterized and confirmed by scanning electron microscopy and UV-Vis spectroscopy. The UV-Vis spectroscopy results confirmed that the G/CoPc composite formed via the strong π–π interaction between CoPc and G. Amperometric i-t technique was used for the determination of glucose. The response of glucose was linear over the concentration ranging from 10 µM to 1.5 mM. The response time of the sensor was found as 30 s with a limit of detection of 0.64 µM (S/N=3). The fabricated sensor also exhibited its good selectivity in the presence of common interfering species. In addition, the fabricated sensor exhibited its special advantages such as low working potential, good sensitivity along with good repeatability and reproducibility for the determination of glucose.

Keywords: glucose, paper-based sensor, ionic liquid/graphene/cobalt phthalocyanine composite, electrochemical detection

Procedia PDF Downloads 164

Authors: Grishma D. Solanki, Karshan Kandoriya

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In modern era of information age, digitalization has revolutionized like never before. Powerful computers, advanced photo editing software packages and high resolution capturing devices have made manipulation of digital images incredibly easy. As per as image forensics concerns, one of the most actively researched area are detection of copy move forgeries. Higher computational complexity is one of the major component of existing techniques to detect such tampering. Moreover, copy move forgery is usually performed in three steps. First, copying of a region in an image then pasting the same one in the same respective image and finally doing some post-processing like rotation, scaling, shift, noise, etc. Consequently, pseudo Zernike moment is used as a features extraction method for matching image blocks and as a primary factor on which performance of detection algorithms depends.

Keywords: copy-move image forgery, digital forensics, image forensics, image forgery

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: Parthasarathi Pramanik

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Body packing is a way of transfer drugs across the international border or any drug prohibited area. The drugs are usually hidden in body packets inside the anatomical body cavities like mouth, intestines, rectum, ear, vagina etc. Cocaine is a very common drug for body packing across the world. A 48 year old male was reported dead in his hotel after complaining of chest pain and vomiting. At autopsy, there were eighty-two white cylindrical body packs in the stomach, small and large intestines. Seals of few of the packets were opened. Toxicological examination revealed presence of cocaine in the stomach, liver, kidney and hair samples. Microscopically, presence of myocardial necrosis with interstitial oedema along with hypertrophy and fibrosis of the myocardial fibre suggested heart failure due to cocaine cardio toxicity. However, focal lymphocyte infiltration and perivascular fibrosis in the myocardium also indicated chronic cocaine toxicity of the deceased. After careful autopsy examination it was considered the victim was died due congestive heart failure secondary to acute and chronic cocaine poisoning.

Keywords: cardiac failure, cocaine, body packer, sudden death

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Authors: Hatem Hajri, Mohamed-Cherif Rahal

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Both Lidars and Radars are sensors for obstacle detection. While Lidars are very accurate on obstacles positions and less accurate on their velocities, Radars are more precise on obstacles velocities and less precise on their positions. Sensor fusion between Lidar and Radar aims at improving obstacle detection using advantages of the two sensors. The present paper proposes a real-time Lidar/Radar data fusion algorithm for obstacle detection and tracking based on the global nearest neighbour standard filter (GNN). This algorithm is implemented and embedded in an automative vehicle as a component generated by a real-time multisensor software. The benefits of data fusion comparing with the use of a single sensor are illustrated through several tracking scenarios (on a highway and on a bend) and using real-time kinematic sensors mounted on the ego and tracked vehicles as a ground truth.

Keywords: ground truth, Hungarian algorithm, lidar Radar data fusion, global nearest neighbor filter

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Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

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The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

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Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

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Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: ring recognition, edge detection, X-ray computed tomography, dendrochronology

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Authors: Brahmanage S. Rangika, Dinithi C. Peiris

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Boiled Aqueous Flower Extract (AFE) of Nyctanthes arbor-tristis L. (Family: Oleaceae) is used in traditional Sri Lankan medicinal system to treat diabetes. However, this is not scientifically proven and the mechanisms by which the flowers reduce diabetes have not been investigated. The present study was carried out to examine the hypoglycemic potential and toxicity effects of aqueous flower extract of N. arbor-tristis. AFE was prepared and mice were treated orally either with 250, 500, and 750 mg/kg of AFE or distilled water (Control). Fasting and random blood glucose levels were determined. In addition, the toxicity of AFE was determined using chronic oral administration. In normoglycemic mice, mid dose (500mg/kg) of AFE significantly (p < 0.01) reduced fasting blood glucose levels by 49% at 4h post treatment. Further, 500mg/kg of AFE significantly (p < 0.01) lowered random blood glucose level of non-fasted normoglycemic mice. AFE significantly lowered total cholesterol and triglyceride levels while increasing the HDL levels in the serum. Further, AFE significantly inhibited the glucose absorption from the lumen of the intestine and it increases the diaphragm uptake of glucose. Alpha-amylase inhibitory activity was also evident. However, AFE did not induce any overt signs of toxicity or hepatotoxicity. There were no adverse effects on food and water intake and body weight of mice during the experimental period. It can be concluded that AFE of N. arbor-tristis posses safe oral anti diabetic potentials mediated via multiple mechanisms. Results of the present study scientifically proved the claims made about the uses of N. arbor-tristis in the treatment of diabetes mellitus in traditional Sri Lankan medicinal system. Further, flowers can also be used for as a remedy to improve blood lipid profile.

Keywords: aqueous extract, hypoglycemic hypolipidemic, Nyctanthes arbor-tristis flowers, hepatotoxicity

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Authors: Seyed Sadegh Naseralavi

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This paper presents a Kernel parallelization equation for damage identification in structures under unknown periodic excitations. Herein, the dynamic differential equation of the motion of structure is viewed as a mapping from displacements to external forces. Utilizing this viewpoint, a new method for damage detection in structures under periodic loads is presented. The developed method requires only two periods of load. The method detects the damages without finding the input loads. The method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering the concept, kernel parallelization equation (KPE) is derived for damage detection under unknown periodic loads. The method is verified for a case study under periodic loads.

Keywords: Kernel, unknown periodic load, damage detection, Kernel parallelization equation

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

Procedia PDF Downloads 452

Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

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An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electromechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

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Authors: Muhammad Fayyaz Nazir, Ellen Wayenberg, Shahzadaah Faahed Qureshi

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Introduction: The study is based on the Contextual Interaction Theory (CIT) constructs to explore the role of policy actors in implementing the COVID-19 Disease Prevention and Control (DP&C) Policy. The study analyzes the role of healthcare workers' contextual factors, such as cognition, motives, and resources, and their interactions in implementing Social Distancing (SD). In this way, we test a two actors policy implementation theory, i.e., the CIT in a three-actor context. Methods: Data was collected through document analysis and semi-structured interviews. For a qualitative study design, interviews were conducted with questions on cognition, motives, and resources from the healthcare workers involved in implementing SD in the local context in Multan – Pakistan. The possible interactions resulting from contextual factors of the policy actors – healthcare workers were identified through framework analysis protocol guided by CIT and supported by trustworthiness criterion and data saturation. Results: This inquiry resulted in theory application, addition, and enrichment. The theoretical application in the three actor's contexts illustrates the different levels of motives, cognition, and resources of healthcare workers – senior administrators, managers, and healthcare professionals. The senior administrators working in National Command and Operations Center (NCOC), Provincial Technical Committees (PTCs), and Districts Covid Teams (DCTs) were playing their role with high motivation. They were fully informed about the policy and moderately resourceful. The policy implementors: healthcare managers working on implementing the SD within their respective hospitals were playing their role with high motivation and were fully informed about the policy. However, they lacked the required resources to implement SD. The target medical and allied healthcare professionals were moderately motivated but lack of resources and information. The interaction resulted in cooperation and the need for learning to manage the future healthcare crisis. However, the lack of resources created opposition to the implementation of SD. Objectives of the Study: The study aimed to apply a two actors theory in a multi actors context. We take this as an opportunity to qualitatively test the theory in a novel situation of the Covid-19 pandemic and make way for its quantitative application by designing a survey instrument so that implementation researchers can apply CIT through multivariate analyses or higher-order statistical modeling. Conclusion: Applying two actors' implementation theory in exploring a complex case of healthcare intervention in three actors context is a unique work that has never been done before, up to the best of our knowledge. So, the work will contribute to the policy implementation studies by applying, extending, and enriching an implementation theory in a novel case of the Covi-19 pandemic, ultimately fulfilling the gap in implementation literature. Policy institutions and other low or middle-income countries can learn from this research and improve SD implementation by working on the variables with weak significance levels.

Keywords: COVID-19, disease prevention and control policy, implementation, policy actors, social distancing

Procedia PDF Downloads 58

Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

Abstract:

An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electro-mechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

Procedia PDF Downloads 293

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: Arto Grasten

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Given the rising trend in obesity in children and youth, age-related decline in moderate- to- vigorous-intensity physical activity (MVPA) in several Western, African, and Asian countries in addition to limited evidence of behavioral, affective, cognitive outcomes in physical education, it is important to clarify the motivational processes in physical education classes behind total MVPA engagement. The present study examined the full sequence of the Hierarchical Model of Motivation in physical education including motivational climate, basic psychological needs, intrinsic motivation, contextual behavior, affect, cognition, total MVPA, and associated links to body mass index (BMI) and gender differences. A cross-sectional data comprised self-reports and objective assessments of 770 middle school students (Mage = 13.99 ± .81 years, 52% of girls) in North-East Finland. In order to test the associations between motivational climate, psychological needs, intrinsic motivation, cognition, behavior, affect, and total MVPA, a path model was implemented. Indirect effects between motivational climate and cognition, behavior, affect and total MVPA were tested by setting basic needs and intrinsic motivation as mediators into the model. The findings showed that direct and indirect paths for girls and boys associated with different contextual outcomes and girls’ indirect paths were not related with total MVPA. Precisely, task-involving climate-mediated by physical competence and intrinsic motivation related to enjoyment, importance, and graded assessments within girls, whereas task-involving climate associated with enjoyment and importance via competence and autonomy, and total MVPA via autonomy, intrinsic motivation, and importance within boys. Physical education assessments appeared to be essential in motivating students to participate in greater total MVPA. BMI was negatively linked with competence and relatedness only among girls. Although, the current and previous empirical findings supported task-involving teaching methods in physical education, in some cases, ego-involving climate should not be totally avoided. This may indicate that girls and boys perceive physical education classes in a different way. Therefore, both task- and ego-involving teaching practices can be useful ways of driving behavior in physical education classes.

Keywords: achievement goal theory, assessment, enjoyment, hierarchical model of motivation, physical activity, self-determination theory

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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: Daniel R. McDougall, Martin D. de Jonge, Gordon M. Miskelly, Duncan J. McGillivray, Andrew G. Jeffs

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The chelating agent ethylenediaminetetraacetic acid (EDTA) is commonly added as a cure-all to seawater in aquaculture hatcheries around the world to reduce heavy metal toxicity, significantly improve the survival of larval shellfish, and to therefore improve the overall production efficiency of the aquaculture industry. However, EDTA is not a biodegradable chemical and is considered to be a persistent organic pollutant, which will accumulate in the environment over time. This makes the use of EDTA unsustainable environmentally, and therefore alternatives should be considered. Ethylenediaminedisuccinic acid (EDDS) is a biodegradable alternative to EDTA with very similar metal chelation properties. This study investigates the effect of EDTA and EDDS at two different concentrations, on metal concentrations found within developing New Zealand green-lipped mussel (Perna canaliculus) larvae. P. canaliculus is New Zealand’s main shellfish aquaculture species, providing a major export for New Zealand’s economy, with excellent potential for increased production in the near future. It is well known that the early stages of bivalve development are the most vulnerable to metal toxicity and P. canaliculus is no exception. The commercially used concentration (12 µmol L⁻¹) of EDTA added to P. canaliculus larval rearing tanks often increases the yield of D-larvae by over 80%. This concentration of EDTA and EDDS will be tested in this study, along with a lower concentration (3 µmol L⁻¹). After 48 hours of larval development, the D-larvae will be analyzed for heavy metal content with Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and heavy metal distribution with synchrotron X-ray Fluorescence Microscopy (XFM). In this study, we found that EDDS also improves the yield of P. canaliculus larvae and could be a viable alternative to EDTA in aquaculture. Furthermore, results suggest a higher concentration of chelating agent is more effective for improving the yield of developing P. canaliculus larvae. Metals with significant differences in concentration with the addition of EDTA were Cr, Cu, Zn, Cd and Pb (P < 0.05). We observed for the first time to the author’s best knowledge, metal distribution within 100 µm P. canaliculus D-larvae using synchrotron XFM and found changes in the distribution of metals with the addition of EDTA. XFM also has the potential to provide information about the chemical state of the metals within mussel larvae. This research provides greater insight into the reasons for the effectiveness of adding the chelating agent to aquaculture culture water, and a more environmentally conscious alternative to the currently used EDTA, which could be extremely valuable for the aquaculture industry.

Keywords: EDDS, EDTA, heavy metals, P. canaliculus, toxicity, water treatment

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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

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Authors: Shreyas Gambhirrao, Aditya Vichare, Aniket Tembhurne, Shahuraj Bhosale

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In today's rapidly evolving environment, stress has emerged as a significant health concern across different age groups. Stress that isn't controlled, whether it comes from job responsibilities, health issues, or the never-ending news cycle, can have a negative effect on our well-being. The problem is further aggravated by the ongoing connection to technology. In this high-tech age, identifying and controlling stress is vital. In order to solve this health issue, the study focuses on three key metrics for stress detection: body temperature, heart rate, and galvanic skin response (GSR). These parameters along with the Support Vector Machine classifier assist the system to categorize stress into three groups: 1) Stressed, 2) Not stressed, and 3) Moderate stress. Proposed training model, a NodeMCU combined with particular sensors collects data in real-time and rapidly categorizes individuals based on their stress levels. Real-time stress detection is made possible by this creative combination of hardware and software.

Keywords: real time stress detection, NodeMCU, sensors, heart-rate, body temperature, galvanic skin response (GSR), support vector machine

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Authors: Osama Saber

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Cyanate is one such anion which is produced during protein poisoning in the body and has been studied extensively in the field of biochemistry because of its toxicity. The present work aims at confinement and storage of cyanate in the nano scale. It was achieved through the intercalation of cyanate anions into nanolayerd structures of Ni-Al LDH. In addition, the effect of aging time on the intercalation of cyanate was clarified using X-ray diffraction and scanning electron microscopy. Furthermore, the effect of cations on the affinity towards the intercalation of cyanate anions inside LDH structure was studied by replacement of tetra-valent cations Ti4+ instead of the tri-vallent cations Al3+ during the preparation of LDH structure. X-ray diffraction patterns of the Ni-Ti LDH showed that the interlayer spacing was 0.73 nm. This spacing was smaller than that of Ni-Al LDH suggesting that the interlayered anions into Ni-Ti LDH are different from those into Ni-Al LDH. Thermal analyses (TG, DTG, and DTA) and Infra-red spectra revealed the presence of only cyanate anions into Ni-Ti LDH while, in the case of Ni-Al LDH, both cyanate and carbonate anions were observed. SEM images showed plate-like morphology for both Ni-Ti and Ni-Al LDHs although the shapes of their plates are not similar. Our results suggested that the LDH structures containing titanium cations have higher affinity for cyanate anions than those containing aluminum cations. Therefore, this choice for cyanate in the interlayered spacing widens the applicability to study the effect of the confinement on the toxicity of cyanate by bio researchers.

Keywords: nanolayered structures, Ni-Al LDH, Ni-Ti LDH, intercalation of cyanate anions, urea hydrolysis

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Authors: Bipul Behari Saha, Sunil Kumar Singh, P. Padmaja, Kamlesh Vishwakarma

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Dissipation study of tebuconazole in alluvial, black and deep-black clayey soils collected from paddy, mango and peanut cropland of tropical agro-climatic zone of India at three concentration levels were carried out for monitoring the water contamination through persisted residual toxicity. The soil-slurry samples were analyzed by capillary GC-NPD methods followed by ultrasound-assisted extraction (UAE) technique and cleanup process. An excellent linear relationship between peak area and concentration obtained in the range 1 to 50 μgkg-1. The detection (S/N, 3 ± 0.5) and quantification (S/N, 7.5 ± 2.5) limits were 3 and 10 μgkg-1 respectively. Well spiked recoveries were achieved from 96.28 to 99.33 % at levels 5 and 20 μgkg-1 and method precision (% RSD) was ≤ 5%. The soils dissipation of tebuconazole was fitted in first order kinetic-model with half-life between 34.48 to 48.13 days. The soil organic-carbon (SOC) content correlated well with the dissipation rate constants (DRC) of the fungicide Tebuconazole. An increase in the SOC content resulted in faster dissipation. The results indicate that the soil organic carbon and tebuconazole concentrations plays dominant role in dissipation processes. The initial concentration illustrated that the degradation rate of tebuconazole in soils was concentration dependent.

Keywords: cropland soil, dissipation, laboratory incubation, tebuconazole

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Authors: Mohammad Y. Alfaifi

Abstract:

Methidathion (MTD) (Trade name Supracide®) is a non-systemic organophosphorus insecticide used intensively worldwide including Saudi Arabia. However, there is a lack in published studies about it's genotoxicity. In this study we evaluated MTD toxicity in rat bone marrow cells (in vivo) and in lymphocytes (in vitro) using different doses based on LD50. MNNCE (Micronucleated normocromatic erythrocytes) and MNPCE (Micronucleated polychromatic erythrocytes), NDI (Nuclear division index) and NDCI (nuclear division cytotoxicity index), necrotic and apoptotic cells were recorded in rat's bone marrow samples. CA, MI (number of cells undergoing mitosis) necrotic, and apoptotic cells recorded in lymphocytes. Results showed that there was a slight increase in the frequency of micronucleated bone marrow cells. However, no structural chromosomal aberrations were detected in vivo or in vitro. On the other hand, the results showed significant increase in necrotic and apoptotic cells following MTD administration in a dose-dependent manner comparing to positive and negative control groups. In light of these results, MTD can be considered highly cytotoxic and moderate genotoxic, and precaution should be taken when using MTD.

Keywords: methidathion, micronucleus, NDI, NDCI, toxicity, chromosomal aberrations

Procedia PDF Downloads 412

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

Abstract:

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

Procedia PDF Downloads 198

Authors: Mohammad Arabi

Abstract:

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

Procedia PDF Downloads 48

Authors: Aysun Sezer

Abstract:

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

Procedia PDF Downloads 66

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

Abstract:

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

Procedia PDF Downloads 73