Search results for: rapid and specific detection

Authors: Moein Izadi, Ali Mohammadzadeh

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Detection of damaged parts of roads after earthquake is essential for coordinating rescuers. In this study, an approach is presented for the semi-automatic detection of damaged roads in a city using pre-event vector maps and both pre- and post-earthquake QuickBird satellite images. Damage is defined in this study as the debris of damaged buildings adjacent to the roads. Some spectral and texture features are considered for SVM classification step to detect damages. Finally, the proposed method is tested on QuickBird pan-sharpened images from the Bam City earthquake and the results show that an overall accuracy of 81% and a kappa coefficient of 0.71 are achieved for the damage detection. The obtained results indicate the efficiency and accuracy of the proposed approach.

Keywords: SVM classifier, disaster management, road damage detection, quickBird images

Procedia PDF Downloads 623

Authors: Mohsen Hasirian, Amir Shahab Shahabi

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Modern societies, marked by expansive Internet connectivity and the rise of e-commerce, are now integrated with digital platforms at an unprecedented level. The efficiency, speed, and accessibility of e-commerce have garnered a substantial consumer base. Against this backdrop, electronic banking has undergone rapid proliferation within the realm of online activities. However, this growth has inadvertently given rise to an environment conducive to illicit activities, notably electronic payment fraud, posing a formidable challenge to the domain of electronic banking. A pivotal role in upholding the integrity of electronic commerce and business transactions is played by electronic fraud detection, particularly in the context of credit cards which underscores the imperative of comprehensive research in this field. To this end, our study introduces an Attentional Bi-Stream Sequence Learner (AttBiSeL) framework that leverages attention mechanisms and recurrent networks. By incorporating bidirectional recurrent layers, specifically bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, the proposed model adeptly extracts past and future transaction sequences while accounting for the temporal flow of information in both directions. Moreover, the integration of an attention mechanism accentuates specific transactions to varying degrees, as manifested in the output of the recurrent networks. The effectiveness of the proposed approach in automatic credit card fraud classification is evaluated on the European Cardholders' Fraud Dataset. Empirical results validate that the hybrid architectural paradigm presented in this study yields enhanced accuracy compared to previous studies.

Keywords: credit card fraud, deep learning, attention mechanism, recurrent neural networks

Procedia PDF Downloads 34

Authors: Sarah A. Goodchild, Rachel Gao, Philip N. Bartlett

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A novel detection approach using immobilized DNA probes labeled with Anthraquinone (AQ) as an electrochemically active reporter moiety has been successfully developed as a new, simple, reliable method for the detection of DNA. This method represents a step forward in DNA detection as it can discriminate between multiple nucleotide polymorphisms within target DNA strands without the need for any additional reagents, reporters or processes such as melting of DNA strands. The detection approach utilizes single-stranded DNA probes immobilized on gold surfaces labeled at the distal terminus with AQ. The effective immobilization has been monitored using techniques such as AC impedance and Raman spectroscopy. Simple voltammetry techniques (Differential Pulse Voltammetry, Cyclic Voltammetry) are then used to monitor the reduction potential of the AQ before and after the addition of complementary strand of target DNA. A reliable relationship between the shift in reduction potential and the number of base pair mismatch has been established and can be used to discriminate between DNA from highly related pathogenic organisms of clinical importance. This indicates that this approach may have great potential to be exploited within biosensor kits for detection and diagnosis of pathogenic organisms in Point of Care devices.

Keywords: Anthraquinone, discrimination, DNA detection, electrochemical biosensor

Procedia PDF Downloads 394

Authors: Parvin Samadi Pakchin, Reza Saber, Hossein Ghanbari, Yadollah Omidi

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Ovarian cancer is one of the leading cause of mortality among the gynecological malignancies, and it remains the one of the most prevalent cancer in females worldwide. Tumor markers are biochemical molecules in blood or tissues which can indicates cancers occurrence in the human body. So, the sensitive and specific detection of cancer markers typically recruited for diagnosing and evaluating cancers. Recently extensive research efforts are underway to achieve a simple, inexpensive and accurate device for detection of cancer biomarkers. Compared with conventional immunoassay techniques, electrochemical immunosensors are of great interest, because they are specific, simple, inexpensive, easy to handling and miniaturization. Moreover, in the past decade nanotechnology has played a crucial role in the development of biosensors. In this study, a signal-off electrochemical immunosensor for the detection of CA125 antigen has been developed using chitosan-gold nanoparticles (CS-AuNP) and multi-wall carbon nanotubes (MWCNT) composites. Toluidine blue (TB) is used as redox probe which is immobilized on the electrode surface. CS-AuNP is synthesized by a simple one step method that HAuCl4 is reduced by NH2 groups of chitosan. The CS-AuNP-MWCNT modified electrode has shown excellent electrochemical performance compared with bare Au electrode. MWCNTs and AuNPs increased electrochemical conductivity and accelerate electrons transfer between solution and electrode surface while excessive amine groups on chitosan lead to the effective loading of the biological material (CA125 antibody) and TB on the electrode surface. The electrochemical, immobilization and sensing properties CS-AuNP-MWCNT-TB modified electrodes are characterized by cyclic voltammetry, electrochemical impedance spectroscopy, differential pulse voltammetry and square wave voltammetry with Fe(CN)63−/4−as an electrochemical redox indicator.

Keywords: signal-off electrochemical biosensor, CA125, ovarian cancer, chitosan-gold nanoparticles

Procedia PDF Downloads 292

Authors: L. Sellami, D. Idoughi, P. F. Tiako

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Cloud computing provides infrastructure to the enterprise through the Internet allowing access to cloud services at anytime and anywhere. This pervasive aspect of the services, the distributed nature of data and the wide use of information make cloud computing vulnerable to intrusions that violate the security of the cloud. This requires the use of security mechanisms to detect malicious behavior in network communications and hosts such as intrusion detection systems (IDS). In this article, we focus on the detection of intrusion into the cloud sing IDSs. We base ourselves on client authentication in the computing cloud. This technique allows to detect the abnormal use of ubiquitous service and prevents the intrusion of cloud computing. This is an approach based on client authentication data. Our IDS provides intrusion detection inside and outside cloud computing network. It is a double protection approach: The security user node and the global security cloud computing.

Keywords: cloud computing, intrusion detection system, privacy, trust

Procedia PDF Downloads 325

Authors: Manal Kamel, Sara Maher, Hanan El Baz, Faten Salah, Omar Sayyouh, Zeinab Demerdash

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In the recent COVID 19 pandemic, experts of public health have emphasized testing, tracking infected people, and tracing their contacts as an effective strategy to reduce the spread of the virus. Development of rapid and sensitive diagnostic assays to replace reverse transcription polymerase chain reaction (RT-PCR) is mandatory..Our innovative test strip relying on the application of nanoparticles conjugated to recombinant nanobodies for SARS-COV-2 spike protein (S1) & angiotensin-converting enzyme 2 (that is responsible for the virus entry into host cells) for rapid detection of SARS-COV-2 spike protein (S1) in saliva or sputum specimens. Comparative tests with RT-PCR will be held to estimate the significant effect of using COVID 19 nanobodies for the first time in the development of lateral flow test strip. The SARS-CoV-2 S1 (3 ng of recombinant proteins) was detected by our developed LFIA in saliva specimen of COVID-19 Patients No cross-reaction was detected with Middle East respiratory syndrome coronavirus (MERS-CoV) or SARS- CoV antigens..Our developed system revealed 96 % sensitivity and 100% specificity for saliva samples compared to 89 % and 100% sensitivity and specificity for nasopharyngeal swabs. providing a reliable alternative for the painful and uncomfortable nasopharyngeal swab process and the complexes, time consuming PCR test. An increase in testing compliances to be expected.

Keywords: COVID 19, diagnosis, LFIA, nanobodies, ACE2

Procedia PDF Downloads 137

Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev

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In this paper approach to incoherent signal detection in multi-element antenna array are researched and modeled. Two types of useful signals with unknown wavefront were considered. First one is deterministic (Barker code), the second one is random (Gaussian distribution). The derivation of the sufficient statistics took into account the linearity of the antenna array. The performance characteristics and detecting curves are modeled and compared for different useful signals parameters and for different number of elements of the antenna array. Results of researches in case of some additional conditions can be applied to a digital communications systems.

Keywords: antenna array, detection curves, performance characteristics, quadrature processing, signal detection

Procedia PDF Downloads 406

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

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

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

Procedia PDF Downloads 415

Authors: Andrey V. Timofeev

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A method is proposed for stable detection of seismoacoustic sources in C-OTDR systems that guarantee given upper bounds for probabilities of type I and type II errors. Properties of the proposed method are rigorously proved. The results of practical applications of the proposed method in a real C-OTDR-system are presented in this.

Keywords: guaranteed detection, C-OTDR systems, change point, interval estimation

Procedia PDF Downloads 257

Authors: Fadime Eroglu

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Acanthamoeba genus belongs to kingdom protozoa, and it is known as free-living amoebae. Acanthamoeba genus has been isolated from human bodies, swimming pools, bottled mineral water, contact lens solutions, dust, and soil. The members of the genus Acanthamoeba causes Acanthamoeba Keratitis which is a painful sight-threatening disease of the eyes. In recent years, the prevalence of Acanthamoeba keratitis has been high rate reported. The eight different Acanthamoeba species are known to be effective in Acanthamoeba keratitis. These species are Acanthamoeba castellanii, Acanthamoeba polyphaga, Acanthamoeba griffini, Acanthamoeba hatchetti, Acanthamoeba culbertsoni and Acanhtamoeba rhysodes. The conventional diagnosis of Acanthamoeba Keratitis has relied on cytological preparations and growth of Acanthamoeba in culture. However molecular methods such as real-time PCR has been found to be more sensitive. The real-time PCR has now emerged as an effective method for more rapid testing for the diagnosis of infectious disease in decade. Therefore, a real-time PCR assay for the detection of Acanthamoeba keratitis and Acanthamoeba species have been developed in this study. The 18S rRNA sequences from Acanthamoeba species were obtained from National Center for Biotechnology Information and sequences were aligned with MEGA 6 programme. Primers and probe were designed using Custom Primers-OligoPerfectTMDesigner (ThermoFisherScientific, Waltham, MA, USA). They were also assayed for hairpin formation and degree of primer-dimer formation with Multiple Primer Analyzer ( ThermoFisherScientific, Watham, MA, USA). The eight different ATCC Acanthamoeba species were obtained, and DNA was extracted using the Qiagen Mini DNA extraction kit (Qiagen, Hilden, Germany). The DNA of Acanthamoeba species were analyzed using newly designed primer and probe set in real-time PCR assay. The early definitive laboratory diagnosis of Acanthamoeba Keratitis and the rapid initiation of suitable therapy is necessary for clinical prognosis. The results of the study have been showed that new primer and probes could be used for detection and distinguish for Acanthamoeba species. These new developing methods are helpful for diagnosis of Acanthamoeba Keratitis.

Keywords: Acathamoeba Keratitis, Acanthamoeba species, fast diagnosis, Real-Time PCR

Procedia PDF Downloads 121

Authors: Ayhan Kucukmanisa, Orhan Akbulut, Oguzhan Urhan

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Nowadays, advanced driver assistance systems (ADAS) have become popular, since they enable safe driving. Lane detection is a vital step for ADAS. The performance of the lane detection process is critical to obtain a high accuracy lane departure warning system (LDWS). Challenging factors such as road cracks, erosion of lane markings, weather conditions might affect the performance of a lane detection system. In this paper, 1-D weighted filter based on row filtering to detect lane marking is proposed. 2-D input image is filtered by 1-D weighted filter considering four-pixel values located symmetrically around the center of candidate pixel. Performance evaluation is carried out by two metrics which are true positive rate (TPR) and false positive rate (FPR). Experimental results demonstrate that the proposed approach provides better lane marking detection accuracy compared to the previous methods while providing real-time processing performance.

Keywords: lane marking filter, lane detection, ADAS, LDWS

Procedia PDF Downloads 194

Authors: Kyung Jin Kim, Jiwon Kwak, Jae-Hoon Lee, Soo Suk Lee

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MicroRNAs (miRNA) are a class of endogenous, single-stranded, small, and non-protein coding RNA molecules typically 20-25 nucleotides long. They are thought to regulate the expression of other genes in a broad range by binding to 3’- untranslated regions (3’-UTRs) of specific mRNAs. The detection of miRNAs is very important for understanding of the function of these molecules and in the diagnosis of variety of human diseases. However, detection of miRNAs is very challenging because of their short length and high sequence similarities within miRNA families. So, a simple-to-use, low-cost, and highly sensitive method for the detection of miRNAs is desirable. In this study, we demonstrate a novel bi-directional extension (BDE) assay. In the first step, a specific linear RT primer is hybridized to 6-10 base pairs from the 3’-end of a target miRNA molecule and then reverse transcribed to generate a cDNA strand. After reverse transcription, the cDNA was hybridized to the 3’-end which is BDE sequence; it played role as the PCR template. The PCR template was amplified in an SYBR green-based quantitative real-time PCR. To prove the concept, we used human brain total RNA. It could be detected quantitatively in the range of seven orders of magnitude with excellent linearity and reproducibility. To evaluate the performance of BDE assay, we contrasted sensitivity and specificity of the BDE assay against a commercially available poly (A) tailing method using miRNAs for let-7e extracted from A549 human epithelial lung cancer cells. The BDE assay displayed good performance compared with a poly (A) tailing method in terms of specificity and sensitivity; the CT values differed by 2.5 and the melting curve showed a sharper than poly (A) tailing methods. We have demonstrated an innovative, cost-effective BDE assay that allows improved sensitivity and specificity in detection of miRNAs. Dynamic range of the SYBR green-based RT-qPCR for miR-145 could be represented quantitatively over a range of 7 orders of magnitude from 0.1 pg to 1.0 μg of human brain total RNA. Finally, the BDE assay for detection of miRNA species such as let-7e shows good performance compared with a poly (A) tailing method in terms of specificity and sensitivity. Thus BDE proves a simple, low cost, and highly sensitive assay for various miRNAs and should provide significant contributions in research on miRNA biology and application of disease diagnostics with miRNAs as targets.

Keywords: bi-directional extension (BDE), microRNA (miRNA), poly (A) tailing assay, reverse transcription, RT-qPCR

Procedia PDF Downloads 166

Authors: Shiyin He, Zheng Huang

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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.

Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet

Procedia PDF Downloads 192

Authors: Zhengyu Yan, Yan Yu, Jianqiu Chen

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A novel sensing system has been designed for naphthalene detection based on the quenched fluorescence signal of CdS quantum dots. The fluorescence intensity of the system reduced significantly after adding CdS quantum dots to the water pollution model because of the fluorescent static quenching f mechanism. Herein, we have demonstrated the facile methodology can offer a convenient and low analysis cost with the recovery rate as 97.43%-103.2%, which has potential application prospect.

Keywords: CdS quantum dots, modification, detection, naphthalene

Procedia PDF Downloads 493

Authors: El Sayed A. Sharara, A. Tsuji, K. Terada

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Call centers have been expanding and they have influence on activation in various markets increasingly. A call center’s work is known as one of the most demanding and stressful jobs. In this paper, we propose the fatigue detection system in order to detect burnout of call center agents in the case of a neck pain and upper back pain. Our proposed system is based on the computer vision technique combined skin color detection with the Viola-Jones object detector. To recognize the gesture of hand poses caused by stress sign, the YCbCr color space is used to detect the skin color region including face and hand poses around the area related to neck ache and upper back pain. A cascade of clarifiers by Viola-Jones is used for face recognition to extract from the skin color region. The detection of hand poses is given by the evaluation of neck pain and upper back pain by using skin color detection and face recognition method. The system performance is evaluated using two groups of dataset created in the laboratory to simulate call center environment. Our call center agent burnout detection system has been implemented by using a web camera and has been processed by MATLAB. From the experimental results, our system achieved 96.3% for upper back pain detection and 94.2% for neck pain detection.

Keywords: call center agents, fatigue, skin color detection, face recognition

Procedia PDF Downloads 295

Authors: Yu Guan, Song Lu, Wei Yuan, Heping Zhang

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Gas fire extinguishing system is widely used due to its cleanliness and efficiency, and since its spray will be affected by many factors such as convection and obstacles in jetting region, so in order to evaluate its effectiveness, detecting concentration distribution in the jetting area is indispensable, which is commonly achieved by aspirating concentration detection technique. During the concentration measurement, the response time of detector is a very important parameter, especially for those fire-extinguishing systems with rapid gas dispersion. Long response time will not only underestimate its concentration but also prolong the change of concentration with time. Therefore it is necessary to analyze the factors influencing the response time. In the paper, an aspirating concentration detection method was introduced, which is achieved by using a small critical nozzle and a laminar flowmeter, and because of the response time is mainly related to the gas transport process from sampling site to the sensor, the effects of exhaust pipe size, gas flow rate, and gas concentration on its response time were analyzed. During the research, Bromotrifluoromethane (CBrF₃) was used. The effect of the sampling tube was investigated with different length of 1, 2, 3, 4 and 5 m (5mm in pipe diameter) and different pipe diameter of 3, 4, 5, 6 and 8 mm (3m in length). The effect of gas flow rate was analyzed by changing the throat diameter of the critical nozzle with 0.5, 0.682, 0.75, 0.8, 0.84 and 0.88 mm. The effect of gas concentration on response time was studied with the concentration range of 0-25%. The result showed that the response time increased with the increase of both the length and diameter of the sampling pipe, and the effect of length on response time was linear, but for the effect of diameter, it was exponential. It was also found that as the throat diameter of critical nozzle increased, the response time reduced a lot, in other words, gas flow rate has a great influence on response time. For the effect of gas concentration, the response time increased with the increase of the CBrF₃ concentration, and the slope of the curve was reduced.

Keywords: aspirating concentration detection, fire extinguishing, gaseous agent, response time

Procedia PDF Downloads 271

Authors: Preston Nabors, Nasseh Tabrizi

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Portable Document Format (PDF) files have gained significant popularity for sharing and distributing documents due to their universal compatibility. However, the widespread use of PDF files has made them attractive targets for cybercriminals, who exploit vulnerabilities to deliver malware and compromise the security of end-user systems. This paper reviews notable contributions in PDF malware detection, including static, dynamic, signature-based, and hybrid analysis. It presents a comprehensive examination of PDF malware detection techniques, focusing on the emerging threat of adversarial sampling and the need for robust defense mechanisms. The paper highlights the vulnerability of machine learning classifiers to evasion attacks. It explores adversarial sampling techniques in PDF malware detection to produce mimicry and reverse mimicry evasion attacks, which aim to bypass detection systems. Improvements for future research are identified, including accessible methods, applying adversarial sampling techniques to malicious payloads, evaluating other models, evaluating the importance of features to malware, implementing adversarial defense techniques, and conducting comprehensive examination across various scenarios. By addressing these opportunities, researchers can enhance PDF malware detection and develop more resilient defense mechanisms against adversarial attacks.

Keywords: adversarial attacks, adversarial defense, adversarial machine learning, intrusion detection, PDF malware, malware detection, malware detection evasion

Procedia PDF Downloads 40

Authors: Maysam Mard-Soltani, Mohamad Javad Rasaee, Saeed Khalili, Abdol Karim Sheikhi, Mehdi Hedayati

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Production of specific antibody responses against hTSH is a cumbersome process due to the high identity between the hTSH and the other members of the glycoprotein hormone family (FSH, LH and HCG) and the high identity between the human hTSH and host animals for antibody production. Therefore, two polyclonal antibodies were purified against two recombinant proteins. Four possible ELISA tests were designed based on these antibodies. These ELISA tests were checked against hTSH and other glycoprotein hormones, and their sensitivity and specificity were assessed. Bioinformatics tools were used to analyze the immunological properties. After the immunogen region selection from hTSH protein, c terminal of B hTSH was selected and applied. Two recombinant genes, with these cut pieces (first: two repeats of C terminal of B hTSH, second: tetanous toxin+B hTSH C terminal), were designed and sub-cloned into the pET32a expression vector. Standard methods were used for protein expression, purification, and verification. Thereafter, immunizations of the white New Zealand rabbits were performed and the serums of them were used for antibody titration, purification and characterization. Then, four ELISA tests based on two antibodies were employed to assess the hTSH and other glycoprotein hormones. The results of these assessments were compared with standard amounts. The obtained results indicated that the desired antigens were successfully designed, sub-cloned, expressed, confirmed and used for in vivo immunization. The raised antibodies were capable of specific and sensitive hTSH detection, while the cross reactivity with the other members of the glycoprotein hormone family was minimum. Among the four designed tests, the test in which the antibody against first protein was used as capture antibody, and the antibody against second protein was used as detector antibody did not show any hook effect up to 50 miu/l. Both proteins have the ability to induce highly sensitive and specific antibody responses against the hTSH. One of the antibody combinations of these antibodies has the highest sensitivity and specificity in hTSH detection.

Keywords: hTSH, bioinformatics, protein expression, cross reactivity

Procedia PDF Downloads 190

Authors: Kumar Dron Shrivastav, Ankan Mukherjee Das, Arti Taneja, Harpreet Singh, Priya Ranjan, Rajiv Janardhanan

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Cervical cancer is one of the most common cancer among women worldwide which can be cured if detected early. Manual pathology which is typically utilized at present has many limitations. The current gold standard for cervical cancer diagnosis is exhaustive and time-consuming because it relies heavily on the subjective knowledge of the oncopathologists which leads to mis-diagnosis and missed diagnosis resulting false negative and false positive. To reduce time and complexities associated with early diagnosis, we require an interactive diagnostic tool for early detection particularly in developing countries where cervical cancer incidence and related mortality is high. Incorporation of digital pathology in place of manual pathology for cervical cancer screening and diagnosis can increase the precision and strongly reduce the chances of error in a time-specific manner. Thus, we propose a robust and interactive cervical cancer image analysis and diagnostic tool, which can categorically process both histopatholgical and cytopathological images to identify abnormal cells in the least amount of time and settings with minimum resources. Furthermore, incorporation of a set of specific parameters that are typically referred to for identification of abnormal cells with the help of open source software -’R’ is one of the major highlights of the tool. The software has the ability to automatically identify and quantify the morphological features, color intensity, sensitivity and other parameters digitally to differentiate abnormal from normal cells, which may improve and accelerate screening and early diagnosis, ultimately leading to timely treatment of cervical cancer.

Keywords: cervical cancer, early detection, digital Pathology, screening

Procedia PDF Downloads 178

Authors: Abdul Ghaffar Memon, Xiao Hong Zhou, Yunpeng Xing, Ruoyu Wang, Miao He

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Due to deleterious environmental and health effects of the Hg²⁺ ions, various online, detection methods apart from the traditional analytical tools have been developed by researchers. Biosensors especially, label, label-free, colorimetric and optical sensors have advanced with sensitive detection. However, there remains a gap of ultrasensitive quantification as noise interact significantly especially in the AuNP based label-free colorimetry. This study reported an amplification strategy using Exo-III enzyme for target recycling of Hg²⁺ ions in a T-rich hairpin loop metallobase label-free colorimetric nanosensor with an improved sensitivity using unmodified gold nanoparticles (uGNPs) as an indicator. The two T-rich metallobase hairpin loop structures as 5’- CTT TCA TAC ATA GAA AAT GTA TGT TTG -3 (HgS1), and 5’- GGC TTT GAG CGC TAA GAA A TA GCG CTC TTT G -3’ (HgS2) were tested in the study. The thermodynamic properties of HgS1 and HgS2 were calculated using online tools (http://biophysics.idtdna.com/cgi-bin/meltCalculator.cgi). The lab scale synthesized uGNPs were utilized in the analysis. The DNA sequence had T-rich bases on both tails end, which in the presence of Hg²⁺ forms a T-Hg²⁺-T mismatch, promoting the formation of dsDNA. Later, the Exo-III incubation enable the enzyme to cleave stepwise mononucleotides from the 3’ end until the structure become single-stranded. These ssDNA fragments then adsorb on the surface of AuNPs in their presence and protect AuNPs from the induced salt aggregation. The visible change in color from blue (aggregation stage in the absence of Hg²⁺) and pink (dispersion state in the presence of Hg²⁺ and adsorption of ssDNA fragments) can be observed and analyzed through UV spectrometry. An ultrasensitive quantitative nanosensor employing Exo-III assisted target recycling of mercury ions through label-free colorimetry with nanomolar detection using uGNPs have been achieved and is further under the optimization to achieve picomolar range by avoiding the influence of the environmental matrix. The proposed strategy will supplement in the direction of uGNP based ultrasensitive, rapid, onsite, label-free colorimetric detection.

Keywords: colorimetric, Exo-III, gold nanoparticles, Hg²⁺ detection, label-free, signal amplification

Procedia PDF Downloads 312

Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

Procedia PDF Downloads 241

Authors: Lina, Arlends Chris, Bagus Mulyawan, Agus B. Dharmawan

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Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.

Keywords: color overlapping windows, image stitching, leukocyte detection, white blood cell detection

Procedia PDF Downloads 312

Authors: Moustapha H. Ibrahim, Dahir Abdourahman

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This document presents a short-circuit fault detection process in a photovoltaic (PV) system. The proposed method is developed in MATLAB/Simulink. It determines whatever the size of the installation number of the short circuit module. The proposed algorithm indicates the presence or absence of an abnormality on the power of the PV system through measures of hourly global irradiation, power output, and ambient temperature. In case a fault is detected, it displays the number of modules in a short circuit. This fault detection method has been successfully tested on two different PV installations.

Keywords: PV system, short-circuit, fault detection, modelling, MATLAB-Simulink

Procedia PDF Downloads 232

Authors: Padmini S. Navalgund, Manasi Naik, Ujwala Patil

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Road hump is a semi-cylindrical elevation on the road made across specific locations of the road. The vehicle needs to maneuver the hump by reducing the speed to avoid car damage and pass over the road hump safely. Road Humps on road surfaces, if identified in advance, help to maintain the security and stability of vehicles, especially in adverse visibility conditions, viz. night scenarios. We have proposed a deep learning architecture adaptation by implementing the MISH activation function and developing a new classification loss function called "Effective Focal Loss" for Indian road humps detection in adverse light scenarios. We captured images comprising of marked and unmarked road humps from two different types of cameras across South India to build a heterogeneous dataset. A heterogeneous dataset enabled the algorithm to train and improve the accuracy of detection. The images were pre-processed, annotated for two classes viz, marked hump and unmarked hump. The dataset from these images was used to train the single-stage object detection algorithm. We utilised an algorithm to synthetically generate reduced visible road humps scenarios. We observed that our proposed framework effectively detected the marked and unmarked hump in the images in clear and ad-verse light environments. This architectural adaptation sets up an option for early detection of Indian road humps in reduced visibility conditions, thereby enhancing the autonomous driving technology to handle a wider range of real-world scenarios.

Keywords: Indian road hump, reduced visibility condition, low light condition, adverse light condition, marked hump, unmarked hump, YOLOv9

Procedia PDF Downloads 28

Authors: Jaime Martín-de-Nicolás, David Mata-Moya, Nerea del-Rey-Maestre, Pedro Gómez-del-Hoyo, María-Pilar Jarabo-Amores

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Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions and sea state can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. A fast CFAR ship detector based on a robust statistical modeling of sea clutter with respect to sea states in SAR images is used. In this paper, the minimum SNR required to obtain a given detection probability with a given false alarm rate for any sea state is determined. A Gaussian target model using real SAR data is considered. Results show that SNR does not depend heavily on the class considered. Provided there is some variation in the backscattering of targets in SAR imagery, the detection probability is limited and a post-processing stage based on morphology would be suitable.

Keywords: SAR, generalized gamma distribution, detection curves, radar detection

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Authors: M. Khouil, N. Saber, M. Mestari

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In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: collision identification, fixed time, convex polyhedra, neural network, AMAXNET

Procedia PDF Downloads 425

Authors: Sriraman Kothuri, Komal Teja Mattupalli

Abstract:

In this paper we proposed a novel algorithm for recognizing hulls in a hand written digits. This is an extension to the work on “Digit Recognition Using Freeman Chain code”. In order to find out the hulls in a user given digit it is necessary to follow three steps. Those are pre-processing, Boundary Extraction and at last apply the Hull Detection system in a way to attain the better results. The detection of Hull Regions is mainly intended to increase the machine learning capability in detection of characters or digits. This can also extend this in order to get the hull regions and their intensities in Black Holes in Space Exploration.

Keywords: chain code, machine learning, hull regions, hull recognition system, SASK algorithm

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

Abstract:

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

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

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Authors: Shalini Menon, K. Girish Kumar

Abstract:

Norepinephrine (NE) is one of the naturally occurring catecholamines which act both as a neurotransmitter and a hormone. Catecholamine levels are used for the diagnosis and regulation of phaeochromocytoma, a neuroendocrine tumor of the adrenal medulla. The development of simple, rapid and cost-effective sensors for NE still remains a great challenge. Herein, a dual-channel sensor has been developed for the determination of NE. A mixture of AgNO₃, NaOH, NH₃.H₂O and cetrimonium bromide in appropriate concentrations was taken as the working solution. To the thoroughly vortexed mixture, an appropriate volume of NE solution was added. After a particular time, the fluorescence and absorbance were measured. Fluorescence measurements were made by exciting at a wavelength of 400 nm. A dual-channel optical sensor has been developed for the colorimetric as well as the fluorimetric determination of NE. Metal enhanced fluorescence property of nanoparticles forms the basis of the fluorimetric detection of this assay, whereas the appearance of brown color in the presence of NE leads to colorimetric detection. Wide linear ranges and sub-micromolar detection limits were obtained using both the techniques. Moreover, the colorimetric approach was applied for the determination of NE in synthetic blood serum and the results obtained were compared with the classic high-performance liquid chromatography (HPLC) method. Recoveries between 97% and 104% were obtained using the proposed method. Based on five replicate measurements, relative standard deviation (RSD) for NE determination in the examined synthetic blood serum was found to be 2.3%. This indicates the reliability of the proposed sensor for real sample analysis.

Keywords: norepinephrine, colorimetry, fluorescence, silver nanoparticles

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Authors: Wanwisa Deenin, Abdulhadee Yakoh, Chahya Kreangkaiwal, Orawon Chailapakul, Kanitha Patarakul, Sudkate Chaiyo

Abstract:

LipL32 is an outer membrane protein present only on pathogenic Leptospira species, which are the causative agent of leptospirosis. Leptospirosis symptoms are often misdiagnosed with other febrile illnesses as the clinical manifestations are non-specific. Therefore, an accurate diagnostic tool for leptospirosis is indeed critical for proper and prompt treatment. Typical diagnosis via serological assays is generally performed to assess the antibodies produced against Leptospira. However, their delayed antibody response and complicated procedure are undoubtedly limited the practical utilization especially in primary care setting. Here, we demonstrate for the first time an early-stage detection of LipL32 by an integrated lateral-flow immunoassay with electrochemical readout (eLFIA). A ferrocene trace tag was monitored via differential pulse voltammetry operated on a smartphone-based device, thus allowing for on-field testing. Superior performance in terms of the lowest detectable limit of detection (LOD) of 8.53 pg/mL and broad linear dynamic range (5 orders of magnitude) among other sensors available thus far was established. Additionally, the developed test strip provided a straightforward yet sensitive approach for diagnosis of leptospirosis using the collected human sera from patients, in which the results were comparable to the real-time polymerase chain reaction technique.

Keywords: leptospirosis, electrochemical detection, lateral flow immunosensor, point-of-care testing, early-stage detection

Procedia PDF Downloads 96