Search results for: human detection and identification

Authors: K. Padmavathi, K. Sri Ramakrishna

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This study describes a procedure for the detection of Left and Right Bundle Branch Block (LBBB and RBBB) ECG patterns using spectral Coherence(SC) technique and LM Neural Network. The Coherence function finds common frequencies between two signals and evaluate the similarity of the two signals. The QT variations of Bundle Blocks are observed in lead V1 of ECG. Spectral Coherence technique uses Welch method for calculating PSD. For the detection of normal and Bundle block beats, SC output values are given as the input features for the LMNN classifier. Overall accuracy of LMNN classifier is 99.5 percent. The data was collected from MIT-BIH Arrhythmia database.

Keywords: bundle block, SC, LMNN classifier, welch method, PSD, MIT-BIH, arrhythmia database

Procedia PDF Downloads 274

Authors: Emma K. Sales

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Durian is the flagship fruit of Mindanao and there is an abundance of several cultivars with many confusing identities/ names. The project was conducted to develop procedure for reliable and rapid detection and sorting of durian planting materials. Moreover, it is also aimed to establish specific genetic or DNA markers for routine testing and authentication of durian cultivars in question. The project developed molecular procedures for routine testing. SSR primers were also screened and identified for their utility in discriminating durian cultivars collected. Results of the study showed the following accomplishments; 1. Twenty (29) SSR primers were selected and identified based on their ability to discriminate durian cultivars, 2. Optimized and established standard procedure for identification and authentication of Durian cultivars 3. Genetic profile of durian is now available at Biotech Unit. Our results demonstrate the relevance of using molecular techniques in evaluating and identifying durian clones. The most polymorphic primers tested in this study could be useful tools for detecting variation even at the early stage of the plant especially for commercial purposes. The process developed combines the efficiency of the microsatellites development process with the optimization of non-radioactive detection process resulting in a user-friendly protocol that can be performed in two (2) weeks and easily incorporated into laboratories about to start microsatellite development projects. This can be of great importance to extend microsatellite analyses to other crop species where minimal genetic information is currently available. With this, the University can now be a service laboratory for routine testing and authentication of durian clones.

Keywords: DNA, SSR analysis, genotype, genetic diversity, cultivars

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Authors: Murali Aarthy, Sanjeev Kumar Singh

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High risk human papillomaviruses are highly associated with the carcinoma of the cervix and the other genital tumors. Cervical cancer develops through the multistep process in which increasingly severe premalignant dysplastic lesions called cervical intraepithelial neoplastic progress to invasive cancer. The oncoprotein E7 of human papillomavirus expressed in the lower epithelial layers drives the cells into S-phase creating an environment conducive for viral genome replication and cell proliferation. The replication of the virus occurs in the terminally differentiating epithelium and requires the activation of cellular DNA replication proteins. To date, no suitable drug molecule is available to treat HPV infection whereas identification of potential drug targets and development of novel anti-HPV chemotherapies with unique mode of actions are expected. Hence, our present study aimed to identify the potential inhibitors analogous to EGCG, a green tea molecule which is considered to be safe to use for mammalian systems. A 3D similarity search on the natural small molecule library from natural product database using EGCG identified 11 potential hits based on their similarity score. The structure based docking strategies were implemented in the potential hits and the key interacting residues of protein with compounds were identified through simulation studies and binding free energy calculations. The conformational changes between the apoprotein and the complex were analyzed with the simulation and the results demonstrated that the dynamical and structural effects observed in the protein were induced by the compounds and indicated the dominance to the oncoprotein. Overall, our study provides the basis for the structural insights of the identified potential hits and EGCG and hence, the analogous compounds identified can be potent inhibitors against the HPV 16 E7 oncoprotein.

Keywords: EGCG, oncoprotein, molecular dynamics simulation, analogues

Procedia PDF Downloads 119

Authors: AlHanoof A. Alharbi, Fatima M. Alamoudi, Razan A. Albrahim, Sarah F. Alharbi, Abdullah M Almuhaideb, Norah A. Almubairik, Abdulrahman Alharby, Naya M. Nagy

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Recently, drones received a rapid interest in different industries worldwide due to its powerful impact. However, limitations still exist in this emerging technology, especially privacy violation. These aircrafts consistently threaten the security of entities by entering restricted areas accidentally or deliberately. Therefore, this research project aims to develop drone detection and prevention mechanism to protect the restricted area. Until now, none of the solutions have met the optimal requirements of detection which are cost-effectiveness, high accuracy, long range, convenience, unaffected by noise and generalization. In terms of prevention, the existing methods are focusing on impractical solutions such as catching a drone by a larger drone, training an eagle or a gun. In addition, the practical solutions have limitations, such as the No-Fly Zone and PITBULL jammers. According to our study and analysis of previous related works, none of the solutions includes detection and prevention at the same time. The proposed solution is a combination of detection and prevention methods. To implement the detection system, a passive radar will be used to properly identify the drone against any possible flying objects. As for the prevention, jamming signals and forceful safe landing of the drone integrated together to stop the drone’s operation. We believe that applying this mechanism will limit the drone’s invasion of privacy incidents against highly restricted properties. Consequently, it effectively accelerates drones‘ usages at personal and governmental levels.

Keywords: detection, drone, jamming, prevention, privacy, RF, radar, UAV

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Authors: M. Chňapek, M. Tomka, R. Peroutková, Z. Gálová

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Processes of plant breeding, testing and licensing of new varieties, patent protection in seed production, relations in trade and protection of copyright are dependent on identification, differentiation and characterization of plant genotypes. Therefore, we focused our research on utilization of wheat storage proteins as genetic markers suitable not only for differentiation of individual genotypes, but also for identification and characterization of their considerable properties. We analyzed a collection of 102 genotypes of bread wheat (Triticum aestivum L.), 41 genotypes of spelt wheat (Triticum spelta L.), and 35 genotypes of durum wheat (Triticum durum Desf.), in this study. Our results show, that genotypes of bread wheat and durum wheat were homogenous and single line, but spelt wheat genotypes were heterogenous. We observed variability of HMW-GS composition according to environmental factors and level of breeding and predict technological quality on the basis of Glu-score calculation.

Keywords: genotype identification, HMW-GS, wheat quality, polymorphism

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Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

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Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

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Authors: Yusuf Ulaş Kabukçu, Si̇nan Çeli̇k, Onur Salan, Mai̇de Altuntaş, Mert Can Dalkiran, Gökseni̇n Bozdağ, Metehan Bulut, Fati̇h Yaman

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This study presents a more compact implementation of the 2.4GHz MIT Coffee Can Doppler Radar for 2.6GHz operating frequency. The main difference of our prototype depends on the use of microstrip antennas which makes it possible to transport with a small robotic vehicle. We have designed our radar system with two different channels: Tx and Rx. The system mainly consists of Voltage Controlled Oscillator (VCO) source, low noise amplifiers, microstrip antennas, splitter, mixer, low pass filter, and necessary RF connectors with cables. The two microstrip antennas, one is element for transmitter and the other one is array for receiver channel, was designed, fabricated and verified by experiments. The system has two operation modes: speed detection and range detection. If the switch of the operation mode is ‘Off’, only CW signal transmitted for speed measurement. When the switch is ‘On’, CW is frequency-modulated and range detection is possible. In speed detection mode, high frequency (2.6 GHz) is generated by a VCO, and then amplified to reach a reasonable level of transmit power. Before transmitting the amplified signal through a microstrip patch antenna, a splitter used in order to compare the frequencies of transmitted and received signals. Half of amplified signal (LO) is forwarded to a mixer, which helps us to compare the frequencies of transmitted and received (RF) and has the IF output, or in other words information of Doppler frequency. Then, IF output is filtered and amplified to process the signal digitally. Filtered and amplified signal showing Doppler frequency is used as an input of audio input of a computer. After getting this data Doppler frequency is shown as a speed change on a figure via Matlab script. According to experimental field measurements the accuracy of speed measurement is approximately %90. In range detection mode, a chirp signal is used to form a FM chirp. This FM chirp helps to determine the range of the target since only Doppler frequency measured with CW is not enough for range detection. Such a FMCW Doppler radar may be used in border security of the countries since it is capable of both speed and range detection.

Keywords: doppler radar, FMCW, range detection, speed detection

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Authors: Chang, Ya-Ju, Hsieh, Pei-Hsin, Wu, Jui-Chuang, Chen-Yang, Yui Whei

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A mesoporous silica material was prepared to apply to the lateral-flow immunochromatography for detecting a model biosample. The probe antibody is immobilized on the silica surface as the test line to capture its affinity antigen, which laterally flows through the chromatography strips. The antigen is labeled with nano-gold particles, such that the detection can be visually read out from the test line without instrument aids. The result reveals that the mesoporous material provides a vast area for immobilizing the detection probes. Biosening surfaces corresponding with a positive proportion of detection signals is obtained with the biosample loading.

Keywords: mesoporous silica, immunochromatography, lateral-flow strips, biosensors, nano-gold particles

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Authors: Hind Hassan Arruwaite

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Conceptual metaphor is a cognitive semantic tool that provides access to people's conceptual systems. The correlation in the human conceptual system surpasses limited time and specific cultures. The universal associations provide universal schemas that organize people's conceptualization of the world. The study aims to explore how the cultural associations used in conceptual metaphors create commonalities and harmony between people of the world. In the research methodology, the researcher implemented Critical Metaphor Analysis, Metaphor Candidate Identification and Metaphor Identification Procedure models to deliver qualitative and descriptive findings. The semantic tension was the key criterion in identifying metaphorically used words in the headlines. The research materials are the oil trade conceptual metaphors used in the headlines of Arab News and Saudi Gazette Newspapers. The data will be uploaded to the self-constructed corpus to examine electronic lists for identifying conceptual metaphors. The study investigates the types of conceptual metaphors used in the headlines of the newspapers, the cultural associations identified in the conceptual metaphors, and whether the identified cultural associations in conceptual metaphors create universal conceptual schemas. The study aligned with previous seminal works on conceptual metaphor theory in emphasizing the distinctive power of conceptual metaphors in exposing the cultural associations that unify people's perceptions. The correlation of people conceptualization provides universal schemas that involve elements of human sensorimotor experiences. The study contributes to exposing the shared cultural associations that ensure the commonality of all humankind's thinking mechanism.

Keywords: critical discourse analysis, critical metaphor analysis, conceptual metaphor theory, primary and specific metaphors, corpus-driven approach, universal associations, image schema, sensorimotor experience, oil trade

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Authors: Shilpa Bagade Poharkar

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The basis for traditional notion of security was the use of force to preserve vital interest which based on either realism or power politics. The modern approach to security extends beyond the traditional notions of security which focus on issues as development and respect for human rights. In global politics, the issue of human security plays a vital role in most of the policy matter. In modern era, the protection of human rights is now recognized as one of the main functions of any legitimate modern state. The research paper will explore the relationship between human rights and security. United Nations is facing major challenges like rampant poverty, refugee outflows, human trafficking, displacement, conflicts, terrorism, intra-inter ethnic conflicts, proliferation of small arms, genocide, piracy, climate change, health issues and so on. The methodology is observed in this paper is doctrinaire which includes analytical and descriptive comparative method. The hypothesis of the paper is the relationship between human rights and a goal of United Nations to attain peace and security. Although previous research has been done in this field but this research paper will try to find out the challenges in the human security through human rights in the contemporary world and will provide measures for it. The study will focus on the following research questions: What are the issues and challenges United Nations facing while advancing human security through human rights? What measures the international community would take for ensuring the protection of human rights while protecting state security and contribute in the attainment of goals of United Nations?

Keywords: human rights, human security, peace, security, United Nations

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Authors: Leandros A. Maglaras, Jianmin Jiang

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The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm.

Keywords: cyber-security, SCADA systems, OCSVM, intrusion detection

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Authors: Sudhanshu Sharma

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Lisinopril, 1-[N-{(s)-I-carboxy-3 phenyl propyl}-L-proline dehydrate is a lysine analog of enalaprilat, the active metabolite of enalapril. It is long-acting, non-sulhydryl angiotensin-converting enzyme (ACE) inhibitor that is used for the treatment of hypertension and congestive heart failure in daily dosage 10-80 mg. Pharmacological activity of lisinopril has been proved in various experimental and clinical studies. Owing to its importance and widespread use, efforts have been made towards the development of simple and reliable analytical methods. As per our literature survey, lisinopril in pharmaceutical formulations has been determined by various analytical methodologies like polaragraphy, potentiometry, and spectrophotometry, but most of these analytical methods are not too suitable for the Identification of lisinopril from clinical samples because of the interferences caused by the amino acids and amino groups containing metabolites present in biological samples. This report is an attempt in the direction of developing a simple and reliable method for on plate identification and quantification of lisinopril in pharmaceutical formulations as well as from human urine samples using silica gel H layers developed with a new mobile phase comprising of micellar solutions of N-cetyl-N, N, N-trimethylammonium bromide (CTAB). Micellar solutions have found numerous practical applications in many areas of separation science. Micellar liquid chromatography (MLC) has gained immense popularity and wider applicability due to operational simplicity, cost effectiveness, relatively non-toxicity and enhanced separation efficiency, low aggressiveness. Incorporation of aqueous micellar solutions as mobile phase was pioneered by Armstrong and Terrill as they accentuated the importance of TLC where simultaneous separation of ionic or non-ionic species in a variety of matrices is required. A peculiarity of the micellar mobile phases (MMPs) is that they have no macroscopic analogues, as a result the typical separations can be easily achieved by using MMPs than aqueous organic mobile phases. Previously MMPs were successfully employed in TLC based critical separations of aromatic hydrocarbons, nucleotides, vitamin K1 and K5, o-, m- and p- aminophenol, amino acids, separation of penicillins. The human urine analysis for identification of selected drugs and their metabolites has emerged as an important investigation tool in forensic drug analysis. Among all chromatographic methods available only thin layer chromatography (TLC) enables a simple fast and effective separation of the complex mixtures present in various biological samples and is recommended as an approved testing for forensic drug analysis by federal Law. TLC proved its applicability during successful separation of bio-active amines, carbohydrates, enzymes, porphyrins, and their precursors, alkaloid and drugs from urine samples.

Keywords: lisnopril, surfactant, chromatography, micellar solutions

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Authors: Su Mon Saw, Joe Rothnagel

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Short open reading frames (sORFs) located in 5’UTR of mRNAs are known as uORFs. Characterization of uORF-encoded peptides (uPEPs) i.e., a subset of short open reading frame encoded peptides (sPEPs) and their translation regulation lead to understanding of causes of genetic disease, proteome complexity and development of treatments. Existence of uORFs within cellular proteome could be detected by LC-MS/MS. The ability of uORF to be translated into uPEP and achievement of uPEP identification will allow uPEP’s characterization, structures, functions, subcellular localization, evolutionary maintenance (conservation in human and other species) and abundance in cells. It is hypothesized that a subset of sORFs are translatable and that their encoded sPEPs are functional and are endogenously expressed contributing to the eukaryotic cellular proteome complexity. This project aimed to investigate whether sORFs encode functional peptides. Liquid chromatography-mass spectrometry (LC-MS) and bioinformatics were thus employed. Due to probable low abundance of sPEPs and small in sizes, the need for efficient peptide enrichment strategies for enriching small proteins and depleting the sub-proteome of large and abundant proteins is crucial for identifying sPEPs. Low molecular weight proteins were extracted using SDS-PAGE from Human Embryonic Kidney (HEK293) cells and Strong Cation Exchange Chromatography (SCX) from secreted HEK293 cells. Extracted proteins were digested by trypsin to peptides, which were detected by LC-MS/MS. The MS/MS data obtained was searched against Swiss-Prot using MASCOT version 2.4 to filter out known proteins, and all unmatched spectra were re-searched against human RefSeq database. ProteinPilot v5.0.1 was used to identify sPEPs by searching against human RefSeq, Vanderperre and Human Alternative Open Reading Frame (HaltORF) databases. Potential sPEPs were analyzed by bioinformatics. Since SDS PAGE electrophoresis could not separate proteins <20kDa, this could not identify sPEPs. All MASCOT-identified peptide fragments were parts of main open reading frame (mORF) by ORF Finder search and blastp search. No sPEP was detected and existence of sPEPs could not be identified in this study. 13 translated sORFs in HEK293 cells by mass spectrometry in previous studies were characterized by bioinformatics. Identified sPEPs from previous studies were <100 amino acids and <15 kDa. Bioinformatics results showed that sORFs are translated to sPEPs and contribute to proteome complexity. uPEP translated from uORF of SLC35A4 was strongly conserved in human and mouse while uPEP translated from uORF of MKKS was strongly conserved in human and Rhesus monkey. Cross-species conserved uORFs in association with protein translation strongly suggest evolutionary maintenance of coding sequence and indicate probable functional expression of peptides encoded within these uORFs. Translation of sORFs was confirmed by mass spectrometry and sPEPs were characterized with bioinformatics.

Keywords: bioinformatics, HEK293 cells, liquid chromatography-mass spectrometry, ProteinPilot, Strong Cation Exchange Chromatography, SDS-PAGE, sPEPs

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Authors: Maciej Dudek, Robert Kruszewski, Janusz Kudla, Konrad Walczyk

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In this paper, we study how taxation of bequests affects human capital formation in the long term and short term horizon. Our underlying model is an overlapping generation model (OLG) with some degree of altruism on the part of the ancestors' generation towards their descendants. We ask the question in three separate frameworks. First, we study a simple one-sector model where a proxy of human capital is wage income. It the steady-state -for CRRA utility function and human capital produced with non-decreasing returns -the taxation of bequests is neutral to the accumulation of human capital. In the second framework, neutrality applies to the growth rates of human capital, physical capital, and consumption. In this case, taxation increases the level of bequests, leading to a lower value of current consumption. Finally in we consider two periods model instead of infinite horizon model as long as the tax revenue is at least partially rebated back to the public, the fraction of human capital engaged in the process of formation of human capital increases with the tax rate on bequests. In other words, taxation of bequests is partially offset by an increase in human capital formation. Higher human capital allows the future generation to earn higher wages, and today's generation can find it optimal to endow the future generation with more human capital when taxation is imposed on physical capital transferred to the next generation.

Keywords: taxation, bequests, policy, human capital

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Authors: Elif Tugce Aksun Tumerkan

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Although seafood is an important part of human diet and categorized highly traded food industry internationally, it is remain overlooked generally in the global food security aspect. Food product authentication is the main interest in the aim of both avoids commercial fraud and to consider the risks that might be harmful to human health safety. In recent years, with increasing consumer demand for regarding food content and it's transparency, there are some instrumental analyses emerging for determining food fraud depend on some analytical methodologies such as proteomic and metabolomics. While, fish and seafood consumed as fresh previously, within advanced technology, processed or packaged seafood consumption have increased. After processing or packaging seafood, morphological identification is impossible when some of the external features have been removed. The main fish and seafood quality-related issues are the authentications of seafood contents such as mislabelling products which may be contaminated and replacement partly or completely, by lower quality or cheaper ones. For all mentioned reasons, truthful consistent and easily applicable analytical methods are needed for assurance the correct labelling and verifying of seafood products. DNA-barcoding methods become popular robust that used in taxonomic research for endangered or cryptic species in recent years; they are used for determining food traceability also. In this review, when comparing the other proteomic and metabolic analysis, DNA-based methods are allowing a chance to identification all type of food even as raw, spiced and processed products. This privilege caused by DNA is a comparatively stable molecule than protein and other molecules. Furthermore showing variations in sequence based on different species and founding in all organisms, make DNA-based analysis more preferable. This review was performed to clarify the main advantages of using DNA-barcoding for determining seafood fraud among other techniques.

Keywords: DNA-barcoding, genetic analysis, food fraud, mislabelling, packaged seafood

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Authors: Sergey Sorokin, Irina Sorokina, Alexander Yazenin

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We present a solution to the Maxmin u/E parameters estimation problem of possibility distributions in m-dimensional case. Our method is based on geometrical approach, where minimal area enclosing ellipsoid is constructed around the sample. Also we demonstrate that one can improve results of well-known algorithms in fuzzy model identification task using Maxmin u/E parameters estimation.

Keywords: possibility distribution, parameters estimation, Maxmin u\E estimator, fuzzy model identification

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Authors: Shin Je Lee, Go Bong Choi, Jeong Cheol Seo, Jong Min Lee, Gibaek Lee

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Water pipe network is installed underground and once equipped; it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate or pressure. The transient model describing water flow in pipelines is presented and simulated using Matlab. The fault situations such as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the better fault detection performance.

Keywords: fault detection, water pipeline model, fast Fourier transform, discrete wavelet transform

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Authors: H. Aldewachi, M. Hines, M. McCulloch, N. Woodroofe, P. Gardiner

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DPP-IV is an enzyme whose expression is affected in a variety of diseases, therefore, has been identified as possible diagnostic or prognostic marker for various tumours, immunological, inflammatory, neuroendocrine, and viral diseases. Recently, DPP-IV enzyme has been identified as a novel target for type II diabetes treatment where the enzyme is involved. There is, therefore, a need to develop sensitive and specific methods that can be easily deployed for the screening of the enzyme either as a tool for drug screening or disease marker in biological samples. A variety of assays have been introduced for the determination of DPP-IV enzyme activity using chromogenic and fluorogenic substrates, nevertheless these assays either lack the required sensitivity especially in inhibited enzyme samples or displays low water solubility implying difficulty for use in vivo samples in addition to labour and time-consuming sample preparation. In this study, novel strategies based on exploiting the high extinction coefficient of gold nanoparticles (GNPs) are investigated in order to develop fast, specific and reliable enzymatic assay by investigating synthetic peptide sequences containing a DPP IV cleavage site and coupling them to GNPs. The DPP IV could be detected by colorimetric response of peptide capped GNPs (P-GNPS) that could be monitored by a UV-visible spectrophotometer or even naked eyes, and the detection limit could reach 0.01 unit/ml. The P-GNPs, when subjected to DPP IV, showed excellent selectivity compared to other proteins (thrombin and human serum albumin) , which led to prominent colour change. This provided a simple and effective colorimetric sensor for on-site and real-time detection of DPP IV.

Keywords: gold nanoparticles, synthetic peptides, colorimetric detection, DPP-IV enzyme

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Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Merih Şimşek, Recep Keşli, Özgül Çetinkaya, Cengiz Demir, Adem Aslan

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Bacteremia is a clinical entity with high morbidity and mortality rates when immediate diagnose, or treatment cannot be achieved. Microorganisms which can cause sepsis or bacteremia are easily isolated from blood cultures. Fifty-five positive blood cultures were included in this study. Microorganisms in 55 blood cultures were isolated by conventional microbiological methods; afterwards, microorganisms were defined in terms of the phenotypic aspects by the Vitek-2 system. The same microorganisms in all blood culture samples were defined in terms of genotypic aspects again by Multiplex-PCR DNA Low-Density Microarray System. At the end of the identification process, the DNA microarray system’s success in identification was evaluated based on the Vitek-2 system. The Vitek-2 system and DNA Microarray system were able to identify the same microorganisms in 53 samples; on the other hand, different microorganisms were identified in the 2 blood cultures by DNA Microarray system. The microorganisms identified by Vitek-2 system were found to be identical to 96.4 % of microorganisms identified by DNA Microarrays system. In addition to bacteria identified by Vitek-2, the presence of a second bacterium has been detected in 5 blood cultures by the DNA Microarray system. It was identified 18 of 55 positive blood culture as E.coli strains with both Vitek 2 and DNA microarray systems. The same identification numbers were found 6 and 8 for Acinetobacter baumanii, 10 and 10 for K.pneumoniae, 5 and 5 for S.aureus, 7 and 11 for Enterococcus spp, 5 and 5 for P.aeruginosa, 2 and 2 for C.albicans respectively. According to these results, DNA Microarray system requires both a technical device and experienced staff support; besides, it requires more expensive kits than Vitek-2. However, this method should be used in conjunction with conventional microbiological methods. Thus, large microbiology laboratories will produce faster, more sensitive and more successful results in the identification of cultured microorganisms.

Keywords: microarray, Vitek-2, blood culture, bacteremia

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Authors: Vichada Chokesikarin

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We propose that the ability to create the business success requires Adversity Quotient (AQ) and Human Capital (HC). The aims of the present study are to investigate adversity quotient, human capital and entrepreneurial success of accommodation entrepreneurs in Pranakorn, Bangkok and to examine the relationship between AQ, HC and Entrepreneurial Success. The participants of this study were 112 entrepreneurs in accommodation business in the Khao San/Grand Palace, the location nearby demonstration area in 2014. Specifically, we focus on higher adversity which provides a measure of one’s perceived capacity to prevail in the face of adversity and the effects of human capital on success. Results indicated that there is significant relationship between human capital and entrepreneurial success, while adversity quotient was found to partially mediate the entrepreneurial success. Moreover, our findings showed that the human capital -experience and skills- are more important than adversity quotient. This suggests that the entrepreneurial success should rely on their skill and experiences.

Keywords: accommodation business, adversity quotient, entrepreneurial success, human capital

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

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This study aimed to propose, a different architecture of a Path Planning using the NECMOP. where several nonlinear objective functions must be optimized in a conflicting situation. The ability to detect and avoid collision is very important for mobile intelligent machines. However, many artificial vision systems are not yet able to quickly and cheaply extract the wealth information. 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. This article represents a comprehensive algorithm that determine through the AMAXNET network a measure (a mini-maximum point) in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: path planning, collision detection, convex polyhedron, neural network

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Authors: Zixian Zhang, Shanliang Yao, Zile Huang, Zhaodong Wu, Xiaohui Zhu, Yong Yue, Jieming Ma

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Unmanned Surface Vehicles (USVs) are valuable due to their ability to perform dangerous and time-consuming tasks on the water. Object detection tasks are significant in these applications. However, inherent challenges, such as the complex distribution of obstacles, reflections from shore structures, water surface fog, etc., hinder the performance of object detection of USVs. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. MMW radar is complementary to vision sensors, providing robust environmental information. The radar 3D point cloud is transferred to 2D radar pseudo image to unify radar and vision information format by utilizing the point transformer. We propose a multi-source object detection network (RV-YOLOX )based on radar-vision fusion for inland waterways environment. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions.

Keywords: inland waterways, YOLO, sensor fusion, self-attention

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Authors: Pratibha Srivastava, Ayyamperumal Jeyaprakash, Gary Steck, Jason Stanley, Leroy Whilby

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Exotic, invasive tephritid fruit flies (Diptera: Tephritidae) are a major threat to fruit and vegetable industries in the United States. The establishment of pest fruit fly in the agricultural industries and produce severe ecological and economic impacts on agricultural diversification and trade. Detection and identification of these agricultural pests in a timely manner will facilitate the possibility of eradication from newly invaded areas. Identification of larval stages to species level is difficult, but is required to determine pest loads and their pathways into the United States. The aim of this study is the New World genus, Anastrepha which includes pests of major economic importance. Mitochondrial cytochrome c oxidase I (COI) gene sequences were amplified from Anastrepha fraterculus specimens collected from South America (Ecuador and Peru). Phylogenetic analysis was performed to characterize the Anastrepha fraterculus complex at a molecular level. During phylogenetics analysis numerous nuclear mitochondrial pseudogenes (numts) were discovered in different specimens. The numts are nonfunctional copies of the mtDNA present in the nucleus and are easily coamplified with the mitochondrial COI gene copy by using conserved universal primers. This is problematic for DNA Barcoding, which attempts to characterize all living organisms by using the COI gene. This study is significant for national quarantine use, as morphological diagnostics to separate larvae of the various members remain poorly developed.

Keywords: tephritid, Anastrepha fraterculus, COI, numts

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

Abstract:

Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

Procedia PDF Downloads 185

Authors: Jean-Pierre Dubois, Jihad S. Daba, H. Karam, J. Abdallah

Abstract:

Tsunami early detection and warning systems have proved to be of ultimate importance, especially after the destructive tsunami that hit Japan in March 2012. Such systems are crucial to inform the authorities of any risk of a tsunami and of the degree of its danger in order to make the right decision and notify the public of the actions they need to take to save their lives. The purpose of this research is to enhance existing tsunami detection and warning systems. We first propose an automated and miniaturized model of an early tsunami detection and warning system. The model for the operation of a tsunami warning system is simulated using the data acquisition toolbox of Matlab and measurements acquired from specified internet pages due to the lack of the required real-life sensors, both seismic and hydrologic, and building a graphical user interface for the system. In the second phase of this work, we implement various satellite image filtering schemes to enhance the acquired synthetic aperture radar images of the tsunami affected region that are masked by speckle noise. This enables us to conduct a post-tsunami damage extent study and calculate the percentage damage. We conclude by proposing improvements to the existing telecommunication infrastructure of existing warning tsunami systems using a migration to IP-based networks and fiber optics links.

Keywords: detection, GIS, GSN, GTS, GPS, speckle noise, synthetic aperture radar, tsunami, wiener filter

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Authors: Gerhardt Boukes, Maryna Van De Venter, Sharlene Govender

Abstract:

Macrofungi have been used for the past two thousand years in Asian countries, and more recently in Western countries, for their medicinal properties. Biological activities include antimicrobial, antioxidant, anti-inflammatory, antidiabetic, anticancer and immunomodulatory to name a few. Several biologically active compounds have been identified and isolated. Macrofungal research in Africa is poorly documented and to the best of our knowledge non-existent. South Africa has a rich macrofungal biodiversity, which includes endemic and exotic macrofungal species. Ethanolic extracts of 13 macrofungal species, including mushrooms, bracket fungi and puffballs, were prepared and screened for cytotoxicity against a panel of seven cell lines, including A549 (human lung adenocarcinoma), HeLa (human cervical adenocarcinoma), HT-29 (human colorectal adenocarcinoma), MCF7 (human breast adenocarcinoma), MIA PaCa-2 (human pancreatic ductal adenocarcinoma), PC-3 (human prostate adenocarcinoma) and Vero (African green monkey kidney epithelial) cells using MTT. Cell lines were chosen according to the most prevalent cancer types affecting males and females in South Africa and globally, and the mutations they contain. Preliminary results have shown that three of the macrofungal genera, i.e. Fomitopsis, Gymnopilus and Pycnoporus, have shown cytotoxic activity, ranging between IC50 ~20 and 200 µg/mL. The molecular mechanism of action contributing to cell death investigated and being investigated include apoptosis (i.e. DNA cell cycle arrest, caspase-3 activation and mitochondrial membrane potential), autophagy (i.e. acridine orange and LC3B staining) and ER stress (i.e. thioflavin T staining and caspase-12) in the presence of melphalan, chloroquine and thapsigargin/tuncamycin as positive controls, respectively. The genus, Pycnoporus, has shown the best cytotoxicity of the three macrofungal genera. Future work will focus on the identification and isolation of novel active compounds and elucidating the mechanism/s of action.

Keywords: cancer, cytotoxicity, macrofungi, mechanism/s of action

Procedia PDF Downloads 239

Authors: Hu Ding, Kai Liu, Guoan Tang

Abstract:

The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

Procedia PDF Downloads 210

Authors: Manpreet Kaur

Abstract:

When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

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Authors: Akila Murali, Prithika Manivel

Abstract:

Disasters are defined as a serious disruption of the functioning of a community or a society, which involves widespread human, material, economic or environmental impacts. The number of people suffering food crisis as a result of natural disasters has tripled in the last thirty years. The economic losses due to natural disasters have shown an increase with a factor of eight over the past four decades, caused by the increased vulnerability of the global society, and also due to an increase in the number of weather-related disasters. Efficient disaster detection and alerting systems could reduce the loss of life and properties. In the event of a disaster, another important issue is a good search and rescue system with high levels of precision, timeliness and safety for both the victims and the rescuers. Wireless Sensor Networks technology has the capability of quick capturing, processing, and transmission of critical data in real-time with high resolution. This paper studies the capacity of sensors and a Wireless Sensor Network to collect, collate and analyze valuable and worthwhile data, in an ordered manner to help with disaster management.

Keywords: alerting systems, disaster detection, Ad Hoc network, WSN technology

Procedia PDF Downloads 401