Search results for: cigarette smoke detection

Authors: Mauricio George Miguel Jardini, Jose Antonio Jardini

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Transmission power utilities participate with kilometers of circuits, many with particularities in terms of vegetation growth. To control these rights-of-way, maintenance teams perform ground, and air inspections, and the identification method is subjective (indirect). On a ground inspection, when identifying an irregularity, for example, high vegetation threatening contact with the conductor cable, pruning or suppression is performed immediately. In an aerial inspection, the suppression team is mobilized to the identified point. This work investigates the use of 3D modeling of a transmission line segment using RGB (red, blue, and green) images and LiDAR (Light Detection and Ranging) sensor data. Both sensors are coupled to unmanned aerial vehicle. The goal is the accurate and timely detection of vegetation along the right-of-way that can cause shutdowns.

Keywords: 3D modeling, LiDAR, right-of-way, transmission lines, vegetation

Procedia PDF Downloads 128

Authors: Yasufumi Yoshimoto, Enkhjargal Tserenochir, Eiji Kinoshita, Takeshi Otaka

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Next generation bio-alcohols produced from non-food based sources like cellulosic biomass are promising renewable energy sources. The present study investigates engine performance, combustion characteristics, and emissions of a small single cylinder direct injection diesel engine fueled by four kinds of next generation bio-alcohol isomer and diesel fuel blends with a constant blending ratio of 3:7 (mass). The tested bio-alcohol isomers here are n-butanol and iso-butanol (C₄ alcohol), and n-pentanol and iso-pentanol (C₅ alcohol). To obtain simultaneous reductions in NOx and smoke emissions, the experiments employed supercharging combined with EGR (Exhaust Gas Recirculation). The boost pressures were fixed at two conditions, 100 kPa (naturally aspirated operation) and 120 kPa (supercharged operation) provided with a roots blower type supercharger. The EGR rates were varied from 0 to 25% using a cooled EGR technique. The results showed that both with and without supercharging, all the bio-alcohol blended diesel fuels improved the trade-off relation between NOx and smoke emissions at all EGR rates while maintaining good engine performance, when compared with diesel fuel operation. It was also found that regardless of boost pressure and EGR rate, the ignition delays of the tested bio-alcohol isomer blends are in the order of iso-butanol > n-butanol > iso-pentanol > n-pentanol. Overall, it was concluded that, except for the changes in the ignition delays the influence of bio-alcohol isomer blends on the engine performance, combustion characteristics, and emissions are relatively small.

Keywords: alternative fuel, butanol, diesel engine, EGR (Exhaust Gas Recirculation), next generation bio-alcohol isomer blended fuel, pentanol, supercharging

Procedia PDF Downloads 167

Authors: Iftekhar Hassan, Sayedil Morsalin, Easir A Khan

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Accidents involving fire occur frequently in recent times and their causes showing a great deal of variety which require intervention methods and risk assessment strategies are unique in each case. On September 4, 2020, a fire and explosion occurred in a confined space caused by a methane gas leak from an underground pipeline in Baitus Salat Jame mosque during Night (Esha) prayer in Narayanganj District, Bangladesh that killed 34 people. In this research, this incident is simulated using Fire Dynamics Simulator (FDS) software to analyze and understand the nature of the accident and associated consequences. FDS is an advanced computational fluid dynamics (CFD) system of fire-driven fluid flow which solves numerically a large eddy simulation form of the Navier–Stokes’s equations for simulation of the fire and smoke spread and prediction of thermal radiation, toxic substances concentrations and other relevant parameters of fire. This study focuses on understanding the nature of the fire and consequence evaluation due to thermal radiation caused by vapor cloud explosion. An evacuation modeling was constructed to visualize the effect of evacuation time and fractional effective dose (FED) for different types of agents. The results were presented by 3D animation, sliced pictures and graphical representation to understand fire hazards caused by thermal radiation or smoke due to vapor cloud explosion. This study will help to design and develop appropriate respond strategy for preventing similar accidents.

Keywords: consequence modeling, fire and explosion, fire dynamics simulation (FDS), thermal radiation

Procedia PDF Downloads 223

Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.

Procedia PDF Downloads 356

Authors: Ali A. Aldosari

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Change detection of Earth surface features is extremely important for better understanding of our environment in order to promote better decision making. Al-Jawf is remarkable for its abundant agricultural water where there is fertile agricultural land due largely to underground water. As result, this region has large areas of cultivation of dates, olives and fruits trees as well as other agricultural products such as Alfa Alfa and wheat. However this agricultural area was declined due to the reduction of government supports in the last decade. This reduction was not officially recorded or measured in this region at large scale or governorate level. Remote sensing data are primary sources extensively used for change detection in agriculture applications. This study is applied the technology of GIS and used the Normalized Difference Vegetation Index (NDVI) which can be used to measure and analyze the spatial and temporal changes in the agriculture areas in the Aljouf region.

Keywords: spatial analysis, geographical information system, change detection

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Authors: Helmi Baazaoui, Mounir Zrigui

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This study addresses the challenge of hate speech detection in Tunisian Arabic text, a critical issue for online safety and moderation. Leveraging the strengths of the AraBERT model, we fine-tuned and evaluated its performance against the Bi-LSTM model across four distinct datasets: T-HSAB, TNHS, TUNIZI-Dataset, and a newly compiled dataset with diverse labels such as Offensive Language, Racism, and Religious Intolerance. Our experimental results demonstrate that AraBERT significantly outperforms Bi-LSTM in terms of Recall, Precision, F1-Score, and Accuracy across all datasets. The findings underline the robustness of AraBERT in capturing the nuanced features of Tunisian Arabic and its superior capability in classification tasks. This research not only advances the technology for hate speech detection but also provides practical implications for social media moderation and policy-making in Tunisia. Future work will focus on expanding the datasets and exploring more sophisticated architectures to further enhance detection accuracy, thus promoting safer online interactions.

Keywords: hate speech detection, Tunisian Arabic, AraBERT, Bi-LSTM, Gemini annotation tool, social media moderation

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Authors: Duc V. Nguyen

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Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where the high cost of a continuous monitoring system can be Justified. Nevertheless, induced current monitoring can be implemented inexpensively on machines with arbitrary sizes by using current transformers. In this regard, effective and low-cost fault detection techniques can be implemented, hence reducing the maintenance and downtime costs of motors. This work proposes a method for fault detection and diagnosis of induction motors, which combines classical fast Fourier transform and modern/advanced machine learning techniques. The proposed method is validated on real-world data and achieves a precision of 99.7% for fault detection and 100% for fault classification with minimal expert knowledge requirement. In addition, this approach allows users to be able to optimize/balance risks and maintenance costs to achieve the highest benet based on their requirements. These are the key requirements of a robust prognostics and health management system.

Keywords: fault detection, FFT, induction motor, predictive maintenance

Procedia PDF Downloads 168

Authors: Temesgen Geremew

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The excessive emission of heavy metal ions from industrial processes poses a serious threat to both the environment and human health. This study presents a distinct approach utilizing (PTh-MnS/CoS NPs) for the highly selective and sensitive detection of Hg²⁺ ions in water. Such detection is crucial for safeguarding human health, protecting the environment, and accurately assessing toxicity. The fabrication method employs a simple and efficient chemical precipitation technique, harmoniously combining polythiophene, MnS, and CoS NPs to create highly active substrates for SERS. The MnS@Hg²⁺ exhibits a distinct Raman shift at 1666 cm⁻¹, enabling specific identification and demonstrating the highest responsiveness among the studied semiconductor substrates with a detection limit of only 1 nM. This investigation demonstrates reliable and practical SERS detection for Hg²⁺ ions. Relative standard deviation (RSD) ranged from 0.49% to 9.8%, and recovery rates varied from 96% to 102%, indicating selective adsorption of Hg²⁺ ions on the synthesized substrate. Furthermore, this research led to the development of a remarkable set of substrates, including (MnS, CoS, MnS/CoS, and PTh-MnS/CoS) nanoparticles were created right there on SiO₂/Si substrate, all exhibiting sensitive, robust, and selective SERS for Hg²⁺ ion detection. These platforms effectively monitor Hg²⁺ concentrations in real environmental samples.

Keywords: surface-enhanced raman spectroscopy (SERS), sensor, mercury ions, nanoparticles, and polythiophene.

Procedia PDF Downloads 73

Authors: Sheldon McCall, Miao Yu, Liyun Gong, Shigang Yue, Stefanos Kollias

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Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a trans- former model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls.

Keywords: healthcare, fall detection, transformer, transfer learning

Procedia PDF Downloads 141

Authors: Bin Liu

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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

Procedia PDF Downloads 160

Authors: Izuru Nomura, Tatsuya Takino, Yuji Kageyama, Shin Nagata, Hiroyuki Kamata

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We propose a method of crater detection from the image of the lunar surface captured by the small space probe. We use the principal component analysis (PCA) to detect craters. Nevertheless, considering　severe environment of the space, it is impossible to use generic computer in practice. Accordingly, we have to implement the method in FPGA. This paper compares FPGA and generic computer by the processing time of a method of crater detection using principal component analysis.

Keywords: crater, PCA, eigenvector, strength value, FPGA, processing time

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Authors: Dinesh Gundavaram

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This paper presents an investigation on bridge damage detection based on the dynamic responses estimated from a passing vehicle. A numerical simulation of steel truss bridge for railway was used in this investigation. The bridge response at different locations is measured using CSI-Bridge software. Several damage scenarios are considered including different locations and severities. The possibilities of dynamic properties of global modes in the identification of structural changes in truss bridges were discussed based on the results of measurement.

Keywords: bridge, damage, dynamic responses, detection

Procedia PDF Downloads 269

Authors: Keyur Joshi, Philip Dietrich, Tjark Windisch, Markus König

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In the field of surveillance-based fire detection, the volume of incoming data is increasing rapidly. However, the labeling of a large industrial dataset is costly due to the high annotation costs associated with current state-of-the-art methods, which often require bounding boxes or segmentation masks for model training. This paper introduces VideoAssist, a video annotation solution that utilizes a video-based foundation model to annotate entire videos with minimal effort, requiring the labeling of bounding boxes for only a few keyframes. To the best of our knowledge, VideoAssist is the first method to significantly reduce the effort required for labeling fire detection videos. The approach offers bounding box and segmentation annotations for the video dataset with minimal manual effort. Results demonstrate that the performance of labels annotated by VideoAssist is comparable to those annotated by humans, indicating the potential applicability of this approach in fire detection scenarios.

Keywords: fire detection, label annotation, foundation models, object detection, segmentation

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Authors: Nuur Ezaini Akmar Ismail, Norbazilah Rahim, Norul Huda Md Rasdi, Maslina Daud

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A web application is the most targeted by the attacker because the web application is accessible by the end users. It has become more advantageous to the attacker since not all the end users aware of what kind of sensitive data already leaked by them through the Internet especially via social network in shake on ‘sharing’. The attacker can use this information such as personal details, a favourite of artists, a favourite of actors or actress, music, politics, and medical records to customize phishing attack thus trick the user to click on malware-laced attachments. The Phishing attack is one of the most popular attacks for social engineering technique against web applications. There are several methods to detect phishing websites such as Blacklist/Whitelist based detection, heuristic-based, and visual similarity-based detection. This paper illustrated a comparison between the heuristic-based technique using features of a uniform resource locator (URL) and visual similarity-based detection techniques that compares the content of a suspected phishing page with the legitimate one in order to detect new phishing sites based on the paper reviewed from the past few years. The comparison focuses on three indicators which are false positive and negative, accuracy of the method, and time consumed to detect phishing website.

Keywords: heuristic-based technique, phishing detection, social engineering and visual similarity-based technique

Procedia PDF Downloads 173

Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

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The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

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Authors: Pajaree Donkhampa, Fuangfa Unob

Abstract:

Ceftazidime is an antibiotic drug commonly used to treat several human and veterinary infections. However, the presence of ceftazidime residues in the environment may induce microbial resistance and cause side effects to humans. Therefore, monitoring the level of ceftazidime in environmental resources is important. In this work, a melamine foam platform was proposed for simultaneous extraction and colorimetric detection of ceftazidime based on the azo dye formation on the surface. The melamine foam was chemically modified with polyethyleneimine (PEI) and characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Ceftazidime is a sample that was extracted on the PEI-modified melamine foam and further reacted with nitrite in an acidic medium to form an intermediate diazonium ion. The diazotized molecule underwent an azo coupling reaction with chromotropic acid to generate a red-colored compound. The material color changed from pale yellow to pink depending on the ceftazidime concentration. The photo of the obtained material was taken by a smartphone camera and the color intensity was determined by Image J software. The material fabrication and ceftazidime extraction and detection procedures were optimized. The detection of a sub-ppm level of ceftazidime was achieved without using a complex analytical instrument.

Keywords: colorimetric detection, ceftazidime, melamine foam, extraction, azo dye

Procedia PDF Downloads 166

Authors: Saeedeh Hajihosseini, Zahra Aghili, Navid Nasirizadeh

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An innovative method of a DNA electrochemical biosensor based on Oracet Blue (OB) as an electroactive label and gold electrode (AuE) for detection of Helicobacter pylori, was offered. A single–stranded DNA probe with a thiol modification was covalently immobilized on the surface of the AuE by forming an Au–S bond. Differential pulse voltammetry (DPV) was used to monitor DNA hybridization by measuring the electrochemical signals of reduction of the OB binding to double– stranded DNA (ds–DNA). Our results showed that OB–based DNA biosensor has a decent potential for detection of single–base mismatch in target DNA. Selectivity of the proposed DNA biosensor was further confirmed in the presence of non–complementary and complementary DNA strands. Under optimum conditions, the electrochemical signal had a linear relationship with the concentration of the target DNA ranging from 0.3 nmol L-1 to 240.0 nmol L-1, and the detection limit was 0.17 nmol L-1, whit a promising reproducibility and repeatability.

Keywords: DNA biosensor, oracet blue, Helicobacter pylori, electrode (AuE)

Procedia PDF Downloads 263

Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

Abstract:

Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes a distinct detection framework based on the one stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: roads, defect detection, visualization, deep learning

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Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev

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In this paper two approaches to joint signal detection, time of arrival (ToA) and angle of arrival (AoA) estimation in multi-element antenna array are investigated. Two scenarios were considered: first one, when the waveform of the useful signal is known a priori and, second one, when the waveform of the desired signal is unknown. For first scenario, the antenna array signal processing based on multi-element matched filtering (MF) with the following non-coherent detection scheme and maximum likelihood (ML) parameter estimation blocks is exploited. For second scenario, the signal processing based on the antenna array elements covariance matrix estimation with the following eigenvector analysis and ML parameter estimation blocks is applied. The performance characteristics of both signal processing schemes are thoroughly investigated and compared for different useful signals and noise parameters.

Keywords: antenna array, signal detection, ToA, AoA estimation

Procedia PDF Downloads 492

Authors: Insaf Bellamine, Hamid Tairi

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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

Procedia PDF Downloads 376

Authors: Hyun-Woo Cho

Abstract:

The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

Procedia PDF Downloads 236

Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad

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Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.

Keywords: breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration

Procedia PDF Downloads 215

Authors: R. Nagarani

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An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures.

Keywords: community detection, complex network, genetic algorithm, package, refactoring

Procedia PDF Downloads 417

Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner

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A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.

Keywords: radio base station, maintenance, classification, detection, deep learning, automation

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Authors: Devineni Vijay Bhaskar, Yendluri Raja

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We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”.

Keywords: convolutional neural network, support vector machine, detection, traffic signs, bootstrap procedures, precision-recall curve

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Authors: Simon B. N. Thompson

Abstract:

Retinoblastoma is a rare type of childhood genetic cancer that affects children worldwide. The diagnosis is often missed due to lack of education and difficulty in presentation of the tumor. Frequently, the tumor on the retina is noticed by photography when the red-eye flash, commonly seen in normal eyes, is not produced. Instead, a yellow or white colored patch is seen or the child has a noticeable strabismus. Early detection can be life-saving though often results in removal of the affected eye. Remaining functioning in the healthy eye when the child is young has resulted in super-vision and high or above-average intelligence. Technological advancement of cameras has helped in early detection. Brain imaging has also made possible early detection of neurological diseases and, together with the monitoring of cortisol levels and yawning frequency, promises to be the next new early diagnostic tool for the detection of neurological diseases where cortisol insufficiency is particularly salient, such as multiple sclerosis and Cushing’s disease.

Keywords: cortisol, neurological disease, retinoblastoma, Thompson cortisol hypothesis, yawning

Procedia PDF Downloads 385

Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

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Authors: Shanshan Zhu, Mohammad Nasim

Abstract:

Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

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Authors: Sadia Ameen, M. Nazim, Hyumg-Kee Seo, Hyung-Shik Shin

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TiO2 nanotube (NT) arrays were grown on titanium (Ti) foil substrate by electrochemical anodic oxidation and utilized as working electrode to fabricate a highly sensitive and reproducible chemical sensor for the detection of harmful phenyl hydrazine chemical. The fabricated chemical sensor based on TiO2 NT arrays electrode exhibited high sensitivity of ~40.9 µA.mM-1.cm-2 and detection limit of ~0.22 µM with short response time (10s).

Keywords: TiO2 NT, phenyl hydrazine, chemical sensor, sensitivity, electrocatalytic properties

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Authors: Chanho Park, Juneseok You, Kuewhan Jang, Sungsoo Na

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Detection technique of nanotoxic materials is strongly imperative, because nano-toxic materials can harmfully influence human health and environment as their engineering applications are growing rapidly in recent years. In present work, we report the DNA immobilized quartz crystal microbalance (QCM) based sensor for detection of nano-toxic materials such as silver ions, Hg2+ etc. by using functionalization of quartz crystal with a target-specific DNA. Since the mass of a target material is comparable to that of an atom, the mass change caused by target binding to DNA on the quartz crystal is so small that it is practically difficult to detect the ions at low concentrations. In our study, we have demonstrated fast and in situ detection of nanotoxic materials using quartz crystal microbalance. We report the label-free and highly sensitive detection of silver ion for present case, which is a typical nano-toxic material by using QCM and silver-specific DNA. The detection is based on the measurement of frequency shift of Quartz crystal from constitution of the cytosine-Ag+-cytosine binding. It is shown that the silver-specific DNA measured frequency shift by QCM enables the capturing of silver ions below 100pM. The results suggest that DNA-based detection opens a new avenue for the development of a practical water-testing sensor.

Keywords: nano-toxic ions, quartz crystal microbalance, frequency shift, target-specific DNA

Procedia PDF Downloads 319