Search results for: antibiotics detection

Authors: Uraib Sharaha, Guy Beck, Joseph Kapelushnik, Adam H. Agbaria, Itshak Lapidot, Shaul Mordechai, Ahmad Salman, Mahmoud Huleihel

Abstract:

Infectious diseases cause a significant burden on the public health and the economic stability of societies all over the world for several centuries. A reliable detection of the causative agent of infection is not possible based on clinical features, since some of these infections have similar symptoms, including fever, sneezing, inflammation, vomiting, diarrhea, and fatigue. Moreover, physicians usually encounter difficulties in distinguishing between viral and bacterial infections based on symptoms. Therefore, there is an ongoing need for sensitive, specific, and rapid methods for identification of the etiology of the infection. This intricate issue perplex doctors and researchers since it has serious repercussions. In this study, we evaluated the potential of the mid-infrared spectroscopic method for rapid and reliable identification of bacterial and viral infections based on simple peripheral blood samples. Fourier transform infrared (FTIR) spectroscopy is considered a successful diagnostic method in the biological and medical fields. Many studies confirmed the great potential of the combination of FTIR spectroscopy and machine learning as a powerful diagnostic tool in medicine since it is a very sensitive method, which can detect and monitor the molecular and biochemical changes in biological samples. We believed that this method would play a major role in improving the health situation, raising the level of health in the community, and reducing the economic burdens in the health sector resulting from the indiscriminate use of antibiotics. We collected peripheral blood samples from young 364 patients, of which 93 were controls, 126 had bacterial infections, and 145 had viral infections, with ages lower than18 years old, limited to those who were diagnosed with fever-producing illness. Our preliminary results showed that it is possible to determine the infectious agent with high success rates of 82% for sensitivity and 80% for specificity, based on the WBC data.

Keywords: infectious diseases, (FTIR) spectroscopy, viral infections, bacterial infections.

Procedia PDF Downloads 138

Authors: Ayadi Aya, Ghorbel Oussama, M. Obeid Abdulfattah, Abid Mohamed

Abstract:

Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

Keywords: bayesian networks, classification-based approaches, KPCA, neural networks, one-class SVM, outlier detection, wireless sensor networks

Procedia PDF Downloads 496

Authors: Mobarok Hossain Bhuyain

Abstract:

Video surveillance processing has attracted various security fields transforming it into one of the leading research fields. Today's demand for detection and tracking of human mobility for security is very useful for human security, such as in crowded areas. Accordingly, video surveillance technology has seen a rapid advancement in recent years, with algorithms analyzing the behavior of people under surveillance automatically. The main motivation of this research focuses on the detection and tracking of the elderly and socially vulnerable people in crowded areas. Degenerate people are a major health concern, especially for elderly people and socially vulnerable people. One major disadvantage of video surveillance is the need for continuous monitoring, especially in crowded areas. To assist the security monitoring live surveillance video, image processing, and artificial intelligence methods can be used to automatically send warning signals to the monitoring officers about elderly people and socially vulnerable people.

Keywords: human detection, target tracking, neural network, particle filter

Procedia PDF Downloads 166

Authors: Leandros A. Maglaras, Jianmin Jiang

Abstract:

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

Procedia PDF Downloads 552

Authors: Ali J. Ghandour, Houssam A. Krayem, Abedelkarim A. Jezzini

Abstract:

High-resolution satellite images and remote sensing can provide global information in a fast way compared to traditional methods of data collection. Under such high resolution, a road is not a thin line anymore. Objects such as cars and trees are easily identifiable. Automatic vehicles enumeration can be considered one of the most important applications in traffic management. In this paper, autonomous vehicle detection and classification approach in highway environment is proposed. This approach consists mainly of three stages: (i) first, a set of preprocessing operations are applied including soil, vegetation, water suppression. (ii) Then, road networks detection and delineation is implemented using built-up area index, followed by several morphological operations. This step plays an important role in increasing the overall detection accuracy since vehicles candidates are objects contained within the road networks only. (iii) Multi-level Otsu segmentation is implemented in the last stage, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Accuracy assessment analysis is conducted over different study areas to show the great efficiency of the proposed method, especially in highway environment.

Keywords: remote sensing, object identification, vehicle and road extraction, vehicle and road features-based classification

Procedia PDF Downloads 231

Authors: Abdulkader Helwan

Abstract:

Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

Procedia PDF Downloads 503

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

Abstract:

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

Procedia PDF Downloads 512

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

Abstract:

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

Procedia PDF Downloads 26

Authors: Cengiz Demir, Recep Keşli, Gülşah Aşık

Abstract:

Objective: This study was carried out with the purpose of defining by using the E test method and determining the antibiotic resistance profiles of Gram-negative anaerobic bacilli isolated from various clinical specimens obtained from patients with suspected anaerobic infections and referred to Medical Microbiology Laboratory of Afyon Kocatepe University, ANS Application and Research Hospital. Methods: Two hundred and seventy eight clinical specimens were examined for isolation of the anaerobic bacteria in Medical Microbiology Laboratory between the 1st November 2014 and 30th October 2015. Specimens were cultivated by using Scheadler agar that 5% defibrinated sheep blood added, and Scheadler broth. The isolated anaerobic Gram-negative bacilli were identified conventional methods and Vitek 2 (ANC ID Card, bioMerieux, France) cards. Antibiotic resistance rates against to penicillin G, clindamycin, cefoxitin, metronidazole, moxifloxacin, imipenem, meropenem, ertapenem and doripenem were determined with E-test method for each isolate. Results: Of the isolated twenty-eight anaerobic gram negative bacilli fourteen were identified as the B. fragilis group, 9 were Prevotella group, and 5 were Fusobacterium group. The highest resistance rate was found against penicillin (78.5%) and resistance rates against clindamycin and cefoxitin were found as 17.8% and 21.4%, respectively. Against to the; metronidazole, moxifloxacin, imipenem, meropenem, ertapenem and doripenem, no resistance was found. Conclusion: Since high rate resistance has been detected against to penicillin in the study penicillin should not be preferred in empirical treatment. Cefoxitin can be preferred in empirical treatment; however, carrying out the antibiotic sensitivity testing will be more proper and beneficial. No resistance was observed against carbapenem group antibiotics and metronidazole; so that reason, these antibiotics should be reserved for treatment of infectious caused by resistant strains in the future.

Keywords: anaerobic gram-negative bacilli, anaerobe, antibiotics and resistance profiles, e-test method

Procedia PDF Downloads 305

Authors: Evelyn Osehontue Uroro, Richard Bright, Jing Yang Quek, Krasimir Vasilev

Abstract:

Bacterial infections have been a challenge both in the public and private sectors. The colonization of bacteria often occurs in medical devices such as catheters, heart valves, respirators, and orthopaedic implants. When biomedical devices are inserted into patients, the deposition of macromolecules such as fibrinogen and immunoglobin on their surfaces makes it easier for them to be prone to bacteria colonization leading to the formation of biofilms. The formation of biofilms on medical devices has led to a series of device-related infections which are usually difficult to eradicate and sometimes cause the death of patients. These infections require surgical replacements along with prolonged antibiotic therapy, which would incur additional health costs. It is, therefore, necessary to prevent device-related infections by inhibiting the formation of biofilms using intelligent technology. Antibiotic resistance of bacteria is also a major threat due to overuse. Different antimicrobial agents have been applied to microbial infections. They include conventional antibiotics like rifampicin. The use of conventional antibiotics like rifampicin has raised concerns as some have been found to have hepatic and nephrotoxic effects due to overuse. Hence, there is also a need for proper delivery of these antibiotics. Different techniques have been developed to encapsulate and slowly release antimicrobial agents, thus reducing host cytotoxicity. Examples of delivery systems are solid lipid nanoparticles, hydrogels, micelles, and polymeric nanoparticles. The different ways by which drugs are released from polymeric nanoparticles include diffusion-based release, elution-based release, and chemical/stimuli-responsive release. Polymeric nanoparticles have gained a lot of research interest as they are basically made from biodegradable polymers. An example of such a biodegradable polymer is polycaprolactone (PCL). PCL degrades slowly by hydrolysis but is often sensitive and responsive to stimuli like enzymes to release encapsulants for antimicrobial therapy. This study presents the synthesis of PCL nanoparticles loaded with rifampicin and the on-demand release of rifampicin for treating staphylococcus aureus infections.

Keywords: enzyme, Staphylococcus aureus, PCL, rifampicin

Procedia PDF Downloads 126

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

Abstract:

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

Procedia PDF Downloads 438

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

Abstract:

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

Procedia PDF Downloads 121

Authors: M. Dahiru, O. I. Enabulele

Abstract:

The research wish to investigate the occurrence of multidrug- resistant Acinetobacter, in carrot and estimate the role of carrot in its transmission, in a rapidly growing urban population. Thus, 50 carrot samples were collected from Jakara wastewater irrigation farms and analyzed on MacConkey agar and screened by Microbact 24E (Oxoid) and susceptibility of isolates tested against 10 commonly used antibiotics. Acinetobacter baumannii and A. lwoffii were isolated in 22.00% and 16% of samples respectively. Resistance to ceporex and penicillin of 36.36% and 27.27% in A. baumannii, and sensitivity to ofloxacin, pefloxacin, gentimycin and co-trimoxazole, were observed. However, for A. lwoffii apart from 37.50% resistance to ceporex, it was also resistant to all other drugs tested. There was a similarity in the resistant shown by A. baumannii and A. lwoffii to fluoroquinolones drugs and β- lactame drugs families in addition to between sulfonamide and animoglycoside demonstrated by A. lwoffii. Interestingly, when resistant similarities to different antibiotics were compared for A. baumannii and A. lwoffii as a whole, significant correlation was observed at P < 0.05 to CPX to NA (46.2%), and SXT to AU (52.6%) respectively, and high multi drug resistance (MDR) of 27.27% and 62.50% by A. baumannii and A. lwoffii respectively and overall MDR of 42.11% in all isolates. The occurrence of multidrug-resistance pathogen in carrot is a serious challenge to public health care, especially in a rapidly growing urban population where subsistence agriculture contributes greatly to urban livelihood and source of vegetables.

Keywords: urban agriculture, public health, fluoroquinolone, sulfonamide, multidrug-resistance

Procedia PDF Downloads 370

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 194

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

Procedia PDF Downloads 392

Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

Abstract:

There is a dramatic surge in the adoption of machine learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. With the application of learning methods in such diverse domains, artificial intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been on developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and three defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt machine learning techniques in security-critical areas such as the nuclear industry without rigorous testing since they may be vulnerable to adversarial attacks. While common defence methods can effectively defend against different attacks, none of the three considered can provide protection against all five adversarial attacks analysed.

Keywords: adversarial machine learning, attacks, defences, nuclear industry, crack detection

Procedia PDF Downloads 158

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

Procedia PDF Downloads 113

Authors: Dilip Saikia, Suparna Bhattacharjee, Nirab Adhikary

Abstract:

In this piece of work, we have presented the synthesis and characterization of CdTe/ZnS core/shell (CS) quantum dots (QD). CS QDs are used as a fluorescence probe to design a simple cost-effective and ultrasensitive sensor for the detection of toxic Hg(II) in an aqueous medium. Mercaptosuccinic acid (MSA) has been used as a capping agent for the synthesis CdTe/ZnS CS QD. Photoluminescence quenching mechanism has been used in the detection experiment of Hg(II). The designed sensing technique shows a remarkably low detection limit of about 1 picomolar (pM). Here, the CS QDs are synthesized by a simple one-pot aqueous method. The synthesized CS QDs are characterized by using advanced diagnostics tools such as UV-vis, Photoluminescence, XRD, FTIR, TEM and Zeta potential analysis. The interaction between CS QDs and the Hg(II) ions results in the quenching of photoluminescence (PL) intensity of QDs, via the mechanism of excited state electron transfer. The proposed mechanism is explained using cyclic voltammetry and zeta potential analysis. The designed sensor is found to be highly selective towards Hg (II) ions. The analysis of the real samples such as drinking water and tap water has been carried out and the CS QDs show remarkably good results. Using this simple sensing method we have designed a prototype low-cost electronic device for the detection of Hg(II) in an aqueous medium. The findings of the experimental results of the designed sensor is crosschecked by using AAS analysis.

Keywords: photoluminescence, quantum dots, quenching, sensor

Procedia PDF Downloads 266

Authors: Changhwan Choi, Yongwan Park

Abstract:

In this paper, we propose a method that allows faster and more accurate detection of traffic lights by a vision sensor during driving, DGPS is used to obtain physical location of a traffic light, extract from the image information of the vision sensor only the traffic light area at this location and ascertain if the sign is in operation and determine its form. This method can solve the problem in existing research where low visibility at night or reflection under bright light makes it difficult to recognize the form of traffic light, thus making driving unstable. We compared our success rate of traffic light recognition in day and night road environments. Compared to previous researches, it showed similar performance during the day but 50% improvement at night.

Keywords: traffic light, intelligent vehicle, night, detection, DGPS

Procedia PDF Downloads 325

Authors: Sourav Sarkar, Manashjit Gogoi

Abstract:

In the evolving landscape of cancer diagnostics, optical biosensing has emerged as a promising tool due to its sensitivity and specificity. This study explores the potential of CdS/ZnS core-shell quantum dots (QDs) capped with 3-Mercaptopropionic acid (3-MPA), which aids in the linking chemistry of QDs to various cancer antibodies. The QDs, with their unique optical and electronic properties, have been integrated into the biosensor design. Their high quantum yield and size-dependent emission spectra have been exploited to improve the sensor’s detection capabilities. The study presents the design of this QD-enhanced optical biosensor. The use of these QDs can also aid multiplexed detection, enabling simultaneous monitoring of different cancer biomarkers. This innovative approach holds significant potential for advancing cancer diagnostics, contributing to timely and accurate detection. Future work will focus on optimizing the biosensor design for clinical applications and exploring the potential of QDs in other biosensing applications. This study underscores the potential of integrating nanotechnology and biosensing for cancer research, paving the way for next-generation diagnostic tools. It is a step forward in our quest for achieving precision oncology.

Keywords: quantum dots, biosensing, cancer, device

Procedia PDF Downloads 56

Authors: Leila Fozouni, Anahita Mazandarani

Abstract:

Background: Maternal infections are the most common cause of infections in infants, and the level of infection and its severity highly depends on the degree of colonization of the bacteria in the mother; so, the occurrence of aggressive diseases is not unpredictable in mothers with very high colonization. Group B Streptococcus is part of the normal flora of the gastrointestinal and genital tracts in women and is the leading cause of septicemia and meningitis in newborns. Today Zinc oxide nanoparticle is regarded as one of the most commonly used and safest nanoparticles for defeating Gram-positive and Gram-negative bacteria. This study aims to determine the antibacterial effects of Zinc oxide on the growth of drug-resistant group B Streptococcus strains isolated from pregnant women. Materials and Methods: This cross-sectional study was conducted on 150 pregnant women of 28–37 weeks admitted to seven hospitals and maternity wards in Golestan province, northeast of Iran. For bacterial identification, rectovaginal swabs were firstly inoculated to the Todd-Hewitt Broth and cultured in blood agar (containing 5% sheep blood). Then microbiologic and PCR methods were performed to detect group B Streptococci. Disk diffusion and broth microdilution tests were used to determine the bacterial susceptibility to antibiotics according to CLSI M100(2021) criteria. The antibacterial properties of Zinc oxide nanoparticles were evaluated using the agar well-diffusion method. Results: The prevalence of group B Streptococcus was 18% in pregnant women. Out of twenty-seven positive cultures, 62.96% were higher than thirty years old. Ninety percent and 45% of isolates were resistant to clindamycin and erythromycin, respectively, and susceptibility to cefazolin was 71%. In addition, susceptibility to ampicillin and penicillin were 74% and 55%, respectively. The results showed that 82% of erythromycin-resistant, 92% clindamycin-resistant, and 78% of cefazolin-resistant isolates were eliminated by zinc oxide nanoparticles at a concentration of 100 mg/L of the nanoparticle. Furthermore, ZnONPs could inhibit all drug-resistant isolates at a concentration of 200 mg/mL (MIC90 ≥ 200). Conclusion: Since the drug resistance of group B streptococci against various antibiotics is increasing, determining and investigating the drug-resistance pattern of this bacterium to different antibiotics in order to prevent arbitrary consumption of antibiotics by pregnant women and ultimately prevent Infant mortality seems necessary. Generally, ZnONPs showed a high antimicrobial effect, and it was revealed that the bactericide effect increases upon the increase in the concentration of the nanoparticle.

Keywords: group B beta-hemolytic streptococcus, pregnant women, zinc oxide nanoparticles, drug resistance

Procedia PDF Downloads 99

Authors: Ghodhbani Salah, Jemili Farah

Abstract:

With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.

Keywords: intrusion detection system, alarm filtering, ANT class, ant clustering, intruders’ behaviors, false alarms

Procedia PDF Downloads 403

Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

Abstract:

In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

Procedia PDF Downloads 357

Authors: Kristina Karapetyan, Flora Tkhruni, Tsovinar Balabekyan, Arevik Israyelyan, Tatyana Khachatryan

Abstract:

The ability of the lactic acid bacteria (LAB) to prevent and cure a variety of diseases, their protective role against infections and colonization of pathogenic microorganisms in the digestive tract, has lead to the coining of the term probiotics or pro-life. LAB inhibiting the growth of pathogenic and food spoilage microorganisms, maintaining the nutritive quality and improving the shelf life of foods. They have also been used as flavor and texture producers. Enterococcus strains have been used for treatment of diseases such as diarrhea or antibiotic associated diarrhea, inflammatory pathologies that affect colon such as irritable bowel syndrome, or immune regulation, diarrhea caused by antibiotic treatments. The obtaining and investigation of biological properties of proteinoceous antibiotics, on the basis of probiotic LAB shown, that bacteriocins, metabiotics, and peptides of LAB represent bactericides have a broad range of activity and are excellent candidates for development of new prophylactic and therapeutic substances to complement or replace conventional antibiotics. The genotyping by 16S rRNA sequencing for LAB were used. Cell free culture broth (CFC) broth was purified by the Gel filtration method on the Sephadex Superfine G 25 resin. Antimicrobial activity was determined by spot-on-lawn method and expressed in arbitrary units (AU/ml). The diversity of multidrug-resistance (MDR) of pathogenic strains to antibiotics, most widely used for treatment of human diseases in the Republics of Armenia and Nagorno Karabakh were examined. It was shown, that difference of resistance of pathogens to antibiotics depends on their isolation sources. The influences of partially purified antimicrobial preparations (AMP), obtained from the different strains of Enterococcus genus on the growth of MDR pathogenic bacteria were investigated. It was shown, that bacteriocin containing partially purified preparations, obtained from different strains of Enterococcus faecium and durans species, possess bactericidal or bacteriostatic activity against antibiotic resistant intestinal, spoilage and food-borne pathogens such as Listeria monocytogenes, Staphylococcus aureus, E. coli, and Salmonella. Endemic strains of LAB, isolated from Matsoni made from donkey, buffalo and goat milk, shown broad spectrum of activity against food spoiling microorganisms, moulds and fungi, such as Salmonella sp., Esherichia coli, Aspergillus and Penicillium species. Highest activity against MDR pathogens shown bacteria, isolated from goat milk products. High stability of the investigated strains of the genus Enerococcus, isolated from samples of matsun from different regions of Nagorno-Karabakh (NKR) to the antibiotics was shown. The obtained data show high stability of the investigated different strains of the genus Enerococcus. The high genetic diversity in Enterococcus group suggests adaptations for specific mutations in different environments. Thus, endemic strains of LAB are able to produce bacteriocins with high and different inhibitory activity against broad spectrum of microorganisms isolated from different sources and belong to different taxonomic group. Prospect of the use of certain antimicrobial preparations against pathogenic strains is obvious. These AMP can be applied for long term use against different etiology antibiotic resistant pathogens for prevention or treatment of infectional diseases as an alternative to antibiotics.

Keywords: antimicrobial biopreparation, endemic lactic acid bacteria, intra-species diversity, multidrug resistance of pathogens

Procedia PDF Downloads 310

Authors: Zhang Shuqi, Liu Dan

Abstract:

For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

Procedia PDF Downloads 104

Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

Abstract:

The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

Procedia PDF Downloads 146

Authors: Ashwani Kumar

Abstract:

Abstract- This study presents an analytical modeling approach for analyzing the drain current behavior in Tunnel Field-Effect Transistor (TFET) biosensors used for the detection of DNA biomolecules. The proposed model focuses on elucidating the relationship between the drain current and the presence of DNA biomolecules, taking into account the impact of various device parameters and biomolecule characteristics. Through comprehensive analysis, the model offers insights into the underlying mechanisms governing the sensing performance of TFET biosensors, aiding in the optimization of device design and operation. A non-local tunneling model is incorporated with other essential models to accurately trace the simulation and modeled data. An experimental validation of the model is provided, demonstrating its efficacy in accurately predicting the drain current response to DNA biomolecule detection. The sensitivity attained from the analytical model is compared and contrasted with the ongoing research work in this area.

Keywords: biosensor, double-gate TFET, DNA detection, drain current modeling, sensitivity

Procedia PDF Downloads 57

Authors: Bo Liu, Qingshan Kong, Weiqing Huang

Abstract:

With the rapid development of modern communication, diagnosing the fiber-optic quality and faults in real-time is widely focused. In this paper, a Labview-based system is proposed for fiber-optic faults detection. The wavelet threshold denoising method combined with Empirical Mode Decomposition (EMD) is applied to denoise the optical time domain reflectometer (OTDR) signal. Then the method based on Gabor representation is used to detect events. Experimental measurements show that signal to noise ratio (SNR) of the OTDR signal is improved by 1.34dB on average, compared with using the wavelet threshold denosing method. The proposed system has a high score in event detection capability and accuracy. The maximum detectable fiber length of the proposed Labview-based system can be 65km.

Keywords: empirical mode decomposition, events detection, Gabor transform, optical time domain reflectometer, wavelet threshold denoising

Procedia PDF Downloads 122

Authors: H. Alijani, M. Jabari, S. Matroodi, H. Zolqarnein, A. Sharafi, I. Zamani

Abstract:

Actinomycetes, Gram-positive bacteria, are interesting as a main producer of secondary metabolites and are important industrially and pharmaceutically. The marine environment is a potential source for new actinomycetes, which can provide novel bioactive compounds and industrially important enzymes. The aims of this study were to isolate and identify novel actinomycetes from Persian Gulf sediments and screen these isolates for the production of secondary metabolites, especially antibiotics, Using phylogenetic (16S rRNA gene sequence), morphological and biochemical analyses. 15 different actinomycete strains from Persian Gulf sediments at a depth of 5-10 m were identified. DNA extraction was done using Cinnapure DNA Kit. PCR amplification of 16S rDNA gene was performed using F27 and R1492 primers. Phylogenetic tree analysis was performed using the MEGA 6 software. Most of the isolated strains belong to the genus namely Streptomyces (14), followed by Nocardiopsis (1). Antibacterial assay of the isolates supernatant was performed using a standard disc diffusion assay with replication (n=3). The results of disk diffusion assay showed that most active strain against Proteus volgaris and Bacillus cereus was AMJ1 (16.46±0.2mm and 13.78±0.2mm, respectively), against Salmonella sp. AMJ7 was the most effective strain (10.13±0.2mm), and AMJ1 and AHA5 showed more inhibitory activity against Escherichia coli (8.04±0.02 mm and 8.2±0.03 ). The AMJ6 strain showed best antibacterial activity against Klebsiella sp. (8.03±0.02mm). Antifungal activity of AMJ2 showed that it was most active strain against complex (16.05±0.02mm) and against Aspergillus flavus strain AMJ1 was most active strain (16.4±0.2mm) and highest antifungal activity against Trichophyton mentagrophytes, Microsporum gyp serum and Candida albicans, were shown by AHA1 (21.03±0.02mm), AHA3 and AHA7 (18±0.03mm), AMJ6 (21.03±0.2mm) respectively. Our results revealed that the marine actinomycetes of Persian Gulf sediments were potent source of novel antibiotics and bioactive compounds and indicated that the antimicrobial metabolites were extracellular. Most of the secondary metabolites and antibiotics are extracellular in nature and extracellular products of actinomycetes show potent antimicrobial activities.

Keywords: antibacterial activity, antifungal activity, marine actinomycetes, Persian Gulf

Procedia PDF Downloads 297

Authors: Nisha Dhariwal, Anupama Sharma

Abstract:

The synthesis of cellulose nanofibrils quaternized with 3‐chloro‐2‐hydroxypropyltrimethylammonium chloride (CHPTAC) in NaOH/urea aqueous solution has been reported. Xylenol Orange (XO) has been used as an indicator for selective detection of Sn (II) ions, by its immobilization on quaternized cellulose membrane. The effects of pH, reagent concentration and reaction time on the immobilization of XO have also been studied. The linear response, limit of detection, and interference of other metal ions have also been studied and no significant interference has been observed. The optical chemical sensor displayed good durability and short response time with negligible leaching of the reagent.

Keywords: cellulose, chemical sensor, heavy metal ions, indicator immobilization

Procedia PDF Downloads 300