Search results for: abnormal activity detection

Authors: Reem I. Altamimi, Geoff D. Skinner

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Extended periods engaged in sedentary behavior increases the risk of becoming overweight and/or obese which is linked to other health problems. Adding technology to the term ‘active living’ permits its inclusion in promoting and facilitating habitual physical activity. Technology can either act as a barrier to, or facilitate this lifestyle, depending on the chosen technology. Physical Activity Monitoring Technologies (PAMTs) are a popular example of such technologies. Different contemporary PAMTs have been evaluated based on customer reviews; however, there is a lack of published experimental research into the efficacy of PAMTs. This research aims to investigate the reliability of four PAMTs: two wristbands (Fitbit Flex and Jawbone UP), a waist-clip (Fitbit One), and a mobile application (iPhone Health Application) for recording a specific distance walked on a treadmill (1.5km) at constant speed. Physical activity tracking technologies are varied in their recordings, even while performing the same activity. This research demonstrates that Jawbone UP band recorded the most accurate distance compared to Fitbit One, Fitbit Flex, and iPhone Health Application.

Keywords: Fitbit, jawbone up, mobile tracking applications, physical activity tracking technologies

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Authors: Srinivasarao Kondaparla, Utsab Debnath, Awakash Soni, Vasantha Rao Dola, Manish Sinha, Kumkum Kumkum Srivastava, Sunil K. Puri, Seturam B. Katti

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In a focused exploration, we have designed synthesized and biologically evaluated chiral conjugated new chloroquine (CQ) analogs with substituted piperazines as antimalarial agents. In vitro as well as in vivo studies revealed that compound 7c showed potent activity [for in vitro IC₅₀= 56.98nM (3D7), 97.76nM (K1); for in vivo (up to at the dose of 12.5 mg/kg); SI = 3510] as a new lead of antimalarial agent. Other compounds 6b, 6d, 7d, 7h, 8c, 8d, 9a, and 9c are also showing moderate activity against CQ-sensitive (3D7) strain and superior activity against resistant (K1) strain of P. falciparum. Furthermore, we have carried out docking and 3D-QSAR studies of all in-house data sets (168 molecules) of chiral CQ analogs to explain the structure activity relationships (SAR). Our new findings specified the significance of H-bond interaction with the side chain of heme for biological activity. In addition, the 3D-QSAR study against 3D7 strain indicated the favorable and unfavorable sites of CQ analogs for incorporating steric, hydrophobic and electropositive groups to improve the antimalarial activity.

Keywords: piperazines, CQ-sensitive strain-3D7, in-vitro and in-vivo assay, docking, 3D-QSAR

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Authors: Yong Geun Yun, Jong Hui kim, Sang Ho Baik

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This study was carried out to develop a fermented dandelion (Tarazacum officinale) beverage using lactic acid bacteria with cinnamoyl esterase (CE) activity isolated from human feces. Lactic acid bacteria were screened based on bacterial survival ability in dandelion extract and CE activity. Dandelion extract fermented by Lactobacillus acidophilus F-46 (LA-F46) maintained approximately 105-106 log CFU/mL over an 8 days period. After fermented dandelion beverage (FDB) with LA-46 for 8 days at 37oC the pH was decreased from pH 7.0 to 3.5. Antioxidant activity by using DPPH radical scavenging activity of the prepared FDB was significantly increased compared to that of non-fermented dandelion beverage (NFDB). Moreover, CE activity was significantly enhanced during fermentation and showed the approximately 4.3 times increased concentration of caffeic acid up to 9.91 mg/100 mL after 8 days of incubation compared to NFDB. Therefore, it concluded that dandelion can be a good source for preparing a functional beverage and fermentation by LA-F46 enhanced the food functionality with enhanced caffeic acids.

Keywords: cinnamoyl esterase, dandelion, fermented beverage, lactic acid bacteria

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Authors: Mohamed M. Bumadian, D. James Gilmour

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Three bacterial species were isolated from the River Wye (Derbyshire, England) and identified using 16S rRNA gene sequencing as Serratia proteamaculans, Aeromonas hydrophila and Klebsiella pneumoniae. Respiration rates of the strains were measured in order to determine the metabolic activity under salt stress. The highest respiration rates of all three strains were found at 0.17 M and 0.5 M NaCl and then the respiration rate decreased with increasing concentrations of NaCl. In addition, the effect of increasing concentrations of NaCl on malate dehydrogenase activity was determined using cell-free extracts of the three strains. Malate dehydrogenase activity was stimulated at NaCl concentrations up to 0.5 M, and a small level of activity remained even at 3.5 M NaCl. The pH optimum of the malate dehydrogenase in cell-free extracts of all strains was higher than pH 7.5.

Keywords: fresh water, halotolerant pathogenic bacteria, 16S rRNA gene, cell-free extracts, respiration rates, malate dehydrogenase

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Authors: I. Tulay Cagatay

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Blue-green microalgae cyanobacteria, which are important photosynthetic organisms of aquatic ecosystems, are the primary sources of many bioactive compounds such as proteins, carbohydrates, lipids, vitamins and enzymes that can be used as antimicrobial and antiviral agents. Some of these organisms are nowadays used directly in the food, cosmetic and pharmaceutical industry, or in aquaculture and biotechnological approaches like biofuel or drug therapy. Finding the effective, environmental friendly chemotropic and antimicrobial agents to control fish pathogens are crucial in a country like Turkey which has a production capacity of about 240 thousand tons of cultured fish and has 2377 production farms and which is the second biggest producer in Europe. In our study, we tested the antimicrobial activity of cyanobacterium spp. against some fish pathogens Aeromonas hydrophila and Yersinia ruckeri that are important pathogens for rainbow trout farms. Agar disk diffusion test method was used for studying antimicrobial activity on pathogens. Both tested microorganisms have shown antimicrobial activity positively as the inhibition zones were 0.45 mm and 0.40 mm respectively.

Keywords: fish pathogen, cyanobacteria, antimicrobial activity, trout

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Authors: Omaima Jbara, Mohame Amine Omrani, Mounir Zrigui

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The detection of violent content in animated cartoons is anessential step toward safeguarding young audiences and promoting responsible media consumption. This study introduces an automated approach to identify violent scenes in cartoons using advanced object detection models. A custom dataset comprising 1,200 frames was curated from various animated sources, focusing on four key classes: Explosion, Blood, Fight, and Gunshot. Data augmentation techniques, including rotation, scaling, and color adjustments, expanded the dataset to 2,000 frames, enhancing diversity and model generalization. YOLO versions 8, 9, and 10 were trained and evaluated on this dataset. Among these, YOLOv9 achieved the highest performance with a mean Average Precision (mAP) of 94%, demonstrating superior accuracy and robustness. These findings highlight YOLOv9’s potential as a reliable tool for detecting violent content in animated media, contributing to the development of effective content moderation systems.

Keywords: cartoon violence detection, YOLO model, computer Vi sion, Real-time content analysis

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Authors: Dilip Saikia, Suparna Bhattacharjee, Nirab Adhikary

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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

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Authors: Changhwan Choi, Yongwan Park

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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

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Authors: Sourav Sarkar, Manashjit Gogoi

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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

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Authors: T. Ould Bellahcen, A. Amiri, I. Touam, F. Hmimid, A. El Amrani, M. Cherki

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The antioxidant activity of three extracts (water, lipidic and ethanolic) prepared from the microalgae Spirulina platensis isolated from Moroccan lake, using 2, 2 diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis ethylbenzthiazoline-6-sulfonic acid (ABTS) radical assay, was studied and compared. The obtained results revealed that the IC₅₀ found using DPPH were lower than that of ABTS for all extracts from these planktonic blue-green algae. The high levels of phenolic and flavonoid content were found in the ethanolic extract 0,33 ± 0,01 mg GAE/g dw and 0,21 ± 0,01 mg quercetin/g dw respectively. In addition, using DPPH, the highest activity with IC₅₀ = 0,449 ± 0,083 mg/ml, was found for the ethanolic extract, followed by that of lipidic extract (IC₅₀ = 0,491 ± 0,059 mg/ml). The lowest activity was for the aqueous extract (IC₅₀ = 4,148 ± 0,132 mg/ml). For ABTS, the highest activity was observed for the lipidic extract with IC₅₀ = 0,740 ± 0,012 mg/ml, while, the aqueous extract recorded the lowest activity (IC₅₀ = 6,914 ± 0, 0067 mg/ml). A moderate activity was showed for the ethanolic extract (IC₅₀ = 5,852 ± 0, 0171 mg/ml). It can be concluded from this first study that Spirulina platensis extracts show an interesting antioxidant and antiradicals properties suggesting that this alga could be used as a potential source of antioxidants. A qualitative and quantitative analysis of polyphenol and flavonoids in the extracts using HPLC is in progress so as to study the correlation between the antioxidant activity and chemical composition.

Keywords: Spirulina platensis, antioxidant, DPPH, ABTS

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Authors: Ghodhbani Salah, Jemili Farah

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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

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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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

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Authors: A. Degale Desta, Tamirat Kebamo

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Background: Recent advances in computer vision and pattern recognition have significantly improved activity recognition through video analysis, particularly with the application of Deep Convolutional Neural Networks (CNNs). One-stage detectors now enable efficient video-based recognition by simultaneously predicting object categories and locations. Such advancements are highly relevant in educational settings where CCTV surveillance could automatically monitor academic activities, enhancing security and classroom management. However, current datasets and recognition systems lack the specific focus on campus environments necessary for practical application in these settings.Objective: This study aims to address this gap by developing a dataset and testing an automated activity recognition system specifically tailored for educational campuses. The EthioCAD dataset was created to capture various classroom activities and teacher-student interactions, facilitating reliable recognition of academic activities using deep learning models. Method: EthioCAD, a novel video-based dataset, was created with a design science research approach to encompass teacher-student interactions across three domains and 18 distinct classroom activities. Using the Roboflow AI framework, the data was processed, with 4.224 KB of frames and 33.485 MB of images managed for frame extraction, labeling, and organization. The Ultralytics YOLOv8 model was then implemented within Google Colab to evaluate the dataset’s effectiveness, achieving high mean Average Precision (mAP) scores. Results: The YOLOv8 model demonstrated robust activity recognition within campus-like settings, achieving an mAP50 of 90.2% and an mAP50-95 of 78.6%. These results highlight the potential of EthioCAD, combined with YOLOv8, to provide reliable detection and classification of classroom activities, supporting automated surveillance needs on educational campuses. Discussion: The high performance of YOLOv8 on the EthioCAD dataset suggests that automated activity recognition for surveillance is feasible within educational environments. This system addresses current limitations in campus-specific data and tools, offering a tailored solution for academic monitoring that could enhance the effectiveness of CCTV systems in these settings. Conclusion: The EthioCAD dataset, alongside the YOLOv8 model, provides a promising framework for automated campus activity recognition. This approach lays the groundwork for future advancements in CCTV-based educational surveillance systems, enabling more refined and reliable monitoring of classroom activities.

Keywords: deep CNN, EthioCAD, deep learning, YOLOv8, activity recognition

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Authors: Zhang Shuqi, Liu Dan

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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

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

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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

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Authors: Ashwani Kumar

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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

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Authors: Valeria Velasco, Ana M. Bonilla, José L. Vergara, Alcides Lofa, Jorge Campos, Pedro Rojas-García

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Staphylococcus aureus is both commensal bacterium and opportunistic pathogen that can cause different diseases in humans and can rapidly develop antimicrobial resistance. Since this bacterium has the ability to colonize the nares and skin of humans and animals, there is a risk of contamination of food in different steps of the food chain supply. Emerging strains have been detected in food-producing animals and meat, such as methicillin-resistant S. aureus (MRSA). The aim of this study was to determine the prevalence and oxacillin susceptibility of S. aureus in the pork chain supply in Chile and to suggest some natural antimicrobials for control. A total of 487 samples were collected from pigs (n=332), carcasses (n=85), and retail pork meat (n=70). Presumptive S. aureus colonies were isolated by selective enrichment and culture media. The confirmation was carried out by biochemical testing (Api® Staph) and molecular technique PCR (detection of nuc and mecA genes, associated with S. aureus and methicillin resistance, respectively). The oxacillin (β-lactam antibiotic that replaced methicillin) susceptibility was assessed by minimum inhibitory concentration (MIC) using the Epsilometer test (Etest). A preliminary assay was carried out to test thymol, carvacrol, oregano essential oil (Origanum vulgare L.), Maqui or Chilean wineberry extract (Aristotelia chilensis (Mol.) Stuntz) as anti-staphylococcal agents using the disc diffusion method at different concentrations. The overall prevalence of S. aureus in the pork chain supply reached 33.9%. A higher prevalence of S. aureus was determined in carcasses (56.5%) than in pigs (28.3%) and pork meat (32.9%) (P ≤ 0.05). The prevalence of S. aureus in pigs sampled at farms (40.6%) was higher than in pigs sampled at slaughterhouses (23.3%) (P ≤ 0.05). The contamination of no packaged meat with S. aureus (43.1%) was higher than in packaged meat (5.3%) (P ≤ 0.05). The mecA gene was not detected in S. aureus strains isolated in this study. Two S. aureus strains exhibited oxacillin resistance (MIC ≥ 4µg/mL). Anti-staphylococcal activity was detected in solutions of thymol, carvacrol, and oregano essential oil at all concentrations tested. No anti-staphylococcal activity was detected in Maqui extract. Finally, S. aureus is present in the pork chain supply in Chile. Although the mecA gene was not detected, oxacillin resistance was found in S. aureus and could be attributed to another resistance mechanism. Thymol, carvacrol, and oregano essential oil could be used as anti-staphylococcal agents at low concentrations. Research project Fondecyt No. 11140379.

Keywords: antimicrobials, mecA gen, nuc gen, oxacillin susceptibility, pork meat

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Authors: Bo Liu, Qingshan Kong, Weiqing Huang

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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

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Authors: Akhad Ibrokhimov

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Update of preventive medicine has exacerbated the problem of cause-and-effect relationship between the presence of herpetic stomatitis (HS) and periodontal disease. Comprehensive survey of children with herpetic stomatitis, according to WHO equirements, on the territory of Tashkent years was conducted. Objective: To analyze the prevalence and intensity of periodontal tissue diseases in children with herpetic stomatitis. Materials and methods. Dental disease in Tashkent was studied in 156 children with herpetic stomatitis, as a control, the incidence of dental studied in 153 children of comparable age and sex never without a history of herpetic stomatitis. Results and discussion. The study revealed that 42,86 ± 13,23% of children with Herpetic stomatitis in the age group 6 years, 1 month - 10 years suffered from periodontal disease, the incidence of periodontal disease in the control group was 14,29 ± 9,35% (R≥0 05) corresponding to the frequency of detection of sextants with bleeding and tartar was equal to 35,71 ± 12,80% vs. 7,14 ± 6,88% (R≥0,05) and 14,29 ± 9,35% against 7 14 ± 6,88% (R≥0,05). Status of periodontal tissues was assessed in age groups 6 years, 1 month - 10 years and 10 years, 1 month - 15 years. The intensity of periodontal lesions observed at the level of 1,79 ± 0,06 vs. 0,66 ± 0,03 (P ≤ 0,05) affected sextant, including sextants with bleeding 1,62 ± 0,07 vs. 0.65 ± 0 , 03 (P ≤ 0,05) and sextants tartar - 0,17 ± 0,008 vs. 0,10 ± 0,008 (P ≤ 0,05). At age 10 years, 1 month - 15 years, a higher prevalence of signs of periodontal lesion was identified in patients with table of contents in 80,00 ± 12,65% of cases versus 30,00 ± 14,49% (P ≤ 0,05), and prevailed bleeding gums 70,00 ± 14,49% against 20,00 ± 11,83% (p ≤ 0.05), tartar was diagnosed respectively in 30,00 ± 14,49% against 10,00 ± 9,48% (R≥0,05) surveyed.

Keywords: vestibular surface, abnormal abrasion, composites, prosthesis

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Authors: Agoston Restas

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Introduction: This paper describes many initiatives and shows also practical examples which happened recently using Unmanned Aerial Systems (UAS) to support disaster management. Since the operation of manned aircraft at disasters is usually not only expensive but often impossible to use as well, in many cases managers fail to use the aerial activity. UAS can be an alternative moreover cost-effective solution for supporting disaster management. Methods: This article uses thematic division of UAS applications; it is based on two key elements, one of them is the time flow of managing disasters, other is its tactical requirements. Logically UAS can be used like pre-disaster activity, activity immediately after the occurrence of a disaster and the activity after the primary disaster elimination. Paper faces different disasters, like dangerous material releases, floods, earthquakes, forest fires and human-induced disasters. Research used function analysis, practical experiments, mathematical formulas, economic analysis and also expert estimation. Author gathered international examples and used own experiences in this field as well. Results and discussion: An earthquake is a rapid escalating disaster, where, many times, there is no other way for a rapid damage assessment than aerial reconnaissance. For special rescue teams, the UAS application can help much in a rapid location selection, where enough place remained to survive for victims. Floods are typical for a slow onset disaster. In contrast, managing floods is a very complex and difficult task. It requires continuous monitoring of dykes, flooded and threatened areas. UAS can help managers largely keeping an area under observation. Forest fires are disasters, where the tactical application of UAS is already well developed. It can be used for fire detection, intervention monitoring and also for post-fire monitoring. In case of nuclear accident or hazardous material leakage, UAS is also a very effective or can be the only one tool for supporting disaster management. Paper shows some efforts using UAS to avoid human-induced disasters in low-income countries as part of health cooperation.

Keywords: disaster management, floods, forest fires, Unmanned Aerial Systems

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Authors: Nisha Dhariwal, Anupama Sharma

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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

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Authors: Samira Taleb, Sakina Aoun, Slimane Ziani, Zoheir Mentouri, Adel Boudiaf

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Rolling is a pressure treatment technique that modifies the shape of steel ingots or billets between rotating rollers. During this process, defects may form on the surface of the rolled sheets and are likely to affect the performance and quality of the finished product. In our study, we developed a method for detecting surface hole defects using a pixel classification approach. This work includes several steps. First, we performed image preprocessing to delimit areas with and without hole defects on the sheet image. Then, we developed the histograms of each area to generate the gray level membership intervals of the pixels that characterize each area. As we noticed an intersection between the characteristics of the gray level intervals of the images of the two areas, we finally performed a learning step based on a series of detection tests to refine the membership intervals of each area, and to choose the defect detection criterion in order to optimize the recognition of the surface hole.

Keywords: classification, defect, surface, detection, hole

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Authors: Shahadut Hossain, Jacek Wesolowski, Zahirul Hoque

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In many epidemiological and environmental studies covariate measurements are subject to the detection limit. In most applications, covariate measurements are usually truncated from below which is known as left-truncation. Because the measuring device, which we use to measure the covariate, fails to detect values falling below the certain threshold. In regression analyses, it causes inflated bias and inaccurate mean squared error (MSE) to the estimators. This paper suggests a response-based regression calibration method to correct the deleterious impact introduced by the covariate detection limit in the estimators of the parameters of simple logistic regression model. Compared to the maximum likelihood method, the proposed method is computationally simpler, and hence easier to implement. It is robust to the violation of distributional assumption about the covariate of interest. In producing correct inference, the performance of the proposed method compared to the other competing methods has been investigated through extensive simulations. A real-life application of the method is also shown using data from a population-based case-control study of non-Hodgkin lymphoma.

Keywords: environmental exposure, detection limit, left truncation, bias, ad-hoc substitution

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: decision tree, feature selection, intrusion detection system, support vector machine

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Authors: M. P. Casiraghi, C. M. Quintella, P. Almeida

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Paraffinic oils were submitted to microbial action. The microorganisms consisted of bacteria of the genera Pseudomonas sp and Bacillus lincheniforms. The alterations in interfacial tension were determined using a tensometer and applying the hanging drop technique at room temperature (299 K ±275 K). The alteration in the constitution of the paraffins was evaluated by means of gas chromatography. The microbial activity was observed to reduce interfacial tension by 54 to 78%, as well as consuming the paraffins C19 to C29 and producing paraffins C36 to C44. The LIFirr technique made it possible to determine the microbial action quickly.

Keywords: paraffins, biosurfactants, LIFirr, microbial activity

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Authors: Leah Ning

Abstract:

This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

Procedia PDF Downloads 92

Authors: Zhen Peng, Baifeng Wu

Abstract:

Modern computing technology enters the era of parallel computing with the trend of sustainable and scalable parallelism. Single Instruction Multiple Data (SIMD) is an important way to go along with the trend. It is able to gather more and more computing ability by increasing the number of processor cores without the need of modifying the program. Meanwhile, in the field of scientific computing and engineering design, many computation intensive applications are facing the challenge of increasingly large amount of data. Data parallel computing will be an important way to further improve the performance of these applications. In this paper, we take the accurate collision detection in building information modeling as an example. We demonstrate a model for constructing a data parallel algorithm. According to the model, a complex object is decomposed into the sets of simple objects; collision detection among complex objects is converted into those among simple objects. The resulting algorithm is a typical SIMD algorithm, and its advantages in parallelism and scalability is unparalleled in respect to the traditional algorithms.

Keywords: data parallelism, collision detection, single instruction multiple data, building information modeling, continuous scalability

Procedia PDF Downloads 291

Authors: Ahmad Akrad, Rabia Sehab, Fadi Alyoussef

Abstract:

Nowadays, induction machines are widely used in industry thankful to their advantages comparing to other technologies. Indeed, there is a big demand because of their reliability, robustness and cost. The objective of this paper is to deal with diagnosis, detection and isolation of faults in a three-phase induction machine. Among the faults, Inter-turn short-circuit fault (ITSC), current sensors fault and single-phase open circuit fault are selected to deal with. However, a fault detection method is suggested using residual errors generated by the root mean square (RMS) of phase currents. The application of this method is based on an asymmetric nonlinear model of Induction Machine considering the winding fault of the three axes frame state space. In addition, current sensor redundancy and sensor fault detection and isolation (FDI) are adopted to ensure safety operation of induction machine drive. Finally, a validation is carried out by simulation in healthy and faulty operation modes to show the benefit of the proposed method to detect and to locate with, a high reliability, the three types of faults.

Keywords: induction machine, asymmetric nonlinear model, fault diagnosis, inter-turn short-circuit fault, root mean square, current sensor fault, fault detection and isolation

Procedia PDF Downloads 201

Authors: Srinidhi. A, Naveed Ahmed, Twinkle Hareendran, Vriksha Prakash

Abstract:

This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models.

Keywords: image processing, machine learning applications, template matching, emotion detection

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Authors: F. Sallem, B. Dahhou, A. Kamoun

Abstract:

In this work, the main problem considered is the detection and the isolation of the actuator fault. A new formulation of the linear system is generated to obtain the conditions of the actuator fault diagnosis. The proposed method is based on the representation of the actuator as a subsystem connected with the process system in cascade manner. The designed formulation is generated to obtain the conditions of the actuator fault detection and isolation. Detectability conditions are expressed in terms of the invertibility notions. An example and a comparative analysis with the classic formulation illustrate the performances of such approach for simple actuator fault diagnosis by using the linear model of nuclear reactor.

Keywords: actuator fault, Fault detection, left invertibility, nuclear reactor, observability, parameter intervals, system inversion

Procedia PDF Downloads 406