Search results for: occupancy detection

Authors: Minakshi Mainaji Sonawane, Bharti W. Gawali, Sudhir Mendhekar, Ramesh R. Manza

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People's lives and health are severely impacted by skin diseases. A new study proposes an effective method for identifying the different forms of skin diseases. Image denoising is a technique for improving image quality after it has been harmed by noise. The proposed technique is based on the usage of the wavelet transform. Wavelet transform is the best method for analyzing the image due to the ability to split the image into the sub-band, which has been used to estimate the noise ratio at the noisy image. According to experimental results, the proposed method presents the best values for MSE, PSNR, and Entropy for denoised images. we can found in Also, by using different types of wavelet transform filters is make the proposed approach can obtain the best results 23.13, 20.08, 50.7 for the image denoising process

Keywords: MSE, PSNR, entropy, Gaussian filter, DWT

Procedia PDF Downloads 199

Authors: F. A. Al-Awadhi, D. Al-Hulail

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In this study, we applied the Bayesian hierarchical model to detect single and multiple change points for daily earthquake body wave magnitude. The change point analysis is used in both backward (off-line) and forward (on-line) statistical research. In this study, it is used with the backward approach. Different types of change parameters are considered (mean, variance or both). The posterior model and the conditional distributions for single and multiple change points are derived and implemented using BUGS software. The model is applicable for any set of data. The sensitivity of the model is tested using different prior and likelihood functions. Using Mb data, we concluded that during January 2002 and December 2003, three changes occurred in the mean magnitude of Mb in Kuwait and its vicinity.

Keywords: multiple change points, Markov Chain Monte Carlo, earthquake magnitude, hierarchical Bayesian mode

Procedia PDF Downloads 440

Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta

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Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.

Keywords: parkinson, gait, feature selection, bat algorithm

Procedia PDF Downloads 523

Authors: P. Revathi, T. Mrunalini, K. Padma Priya, P. Ramya, R. Saranya

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During the tough times of covid, those people who were hospitalized found it difficult to always convey what they wanted to or needed to the attendee. Sometimes the attendees might also not be there. In that case, the patients can use simple hand gestures to control electrical appliances (like its set it for a zero watts bulb)and three other gestures for voice note intimation. In this AI-based hand recognition project, NodeMCU is used for the control action of the relay, and it is connected to the firebase for storing the value in the cloud and is interfaced with the python code via raspberry pi. For three hand gestures, a voice clip is added for intimation to the attendee. This is done with the help of Google’s text to speech and the inbuilt audio file option in the raspberry pi 4. All the 5 gestures will be detected when shown with their hands via a webcam which is placed for gesture detection. A personal computer is used for displaying the gestures and for running the code in the raspberry pi imager.

Keywords: onion harvesting, automatic pluging, camera, raspberry pi

Procedia PDF Downloads 177

Authors: Caner Koc, Dilara Gerdan Koc, Mustafa Vatandas

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The fundamental components of autonomous agricultural robot design, such as having a working understanding of coordinates, correctly constructing the desired route, and sensing environmental elements, are the most important. A variety of sensors, hardware, and software are employed by agricultural robots to find these systems.These enable the fully automated driving system of an autonomous vehicle to simulate how a human-driven vehicle would respond to changing environmental conditions. To calculate the vehicle's motion trajectory using data from the sensors, this automation system typically consists of a sophisticated software architecture based on object detection and driving decisions. In this study, the software architecture of an autonomous agricultural vehicle is compared to the trajectory planning techniques.

Keywords: agriculture 5.0, computational intelligence, motion planning, trajectory planning

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Authors: Burak Yildirim, Muhsin Tunay Gencoglu

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This publication shows the effects of Microgrid (MG) integration on the power systems oscillating stability. Generated MG model power systems were applied to the IEEE 14 bus test system which is widely used in stability studies. Stability studies were carried out with the help of eigenvalue analysis over linearized system models. In addition, Hopf bifurcation point detection was performed to show the effect of MGs on the system loadability margin. In the study results, it is seen that MGs affect system stability positively by increasing system loadability margin and has a damper effect on the critical modes of the system and the electromechanical local modes, but they make the damping amount of the electromechanical interarea modes reduce.

Keywords: Eigenvalue analysis, microgrid, Hopf bifurcation, oscillatory stability

Procedia PDF Downloads 276

Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

Procedia PDF Downloads 218

Authors: Andrey V. Timofeev, Viktor M. Denisov

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A robust sequential nonparametric method is proposed for adaptation to background noise parameters for real-time. The distribution of background noise was modelled like to Huber contamination mixture. The method is designed to operate as an adaptation-unit, which is included inside a detection subsystem of an integrated multichannel monitoring system. The proposed method guarantees the given size of a nonasymptotic confidence set for noise parameters. Properties of the suggested method are rigorously proved. The proposed algorithm has been successfully tested in real conditions of a functioning C-OTDR monitoring system, which was designed to monitor railways.

Keywords: guaranteed estimation, multichannel monitoring systems, non-asymptotic confidence set, contamination mixture

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Authors: Yayun Hsu, Henry Horng-Shing Lu

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In this paper, a new method is proposed to extending the method of connected component labeling from processing binary images to multi-scale modeling of images. By using the adaptive threshold of multi-scale attributes, this approach minimizes the possibility of missing those important components with weak intensities. In addition, the computational cost of this approach remains similar to that of the typical approach of component labeling. Then, this methodology is applied to grain boundary detection and Drosophila Brain-bow neuron segmentation. These demonstrate the feasibility of the proposed approach in the analysis of challenging microscopy images for scientific discovery.

Keywords: microscopic image processing, scientific data mining, multi-scale modeling, data mining

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Authors: Ahmed Khalaf Reyad Raslan

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Cancer stem cells (CSCs) describe a class of pluripotent cancer cells that behave analogously to normal stem cells in their ability to differentiate into the spectrum of cell types observed in tumors. The de-differentiation processes, such as an epithelial-mesenchymal transition (EMT), are known to enhance cellular plasticity. Here, we demonstrate a new hypothesis to use hybrid superparamagnetic magnetite nanoparticles (Fe₃O₄)- graphene oxide functionalized surface with Collagen to target the cancer stem cell as an early detection tool for cancer. We think that with the use of magnetic resonance imaging (MRI) and the new hybrid system would be possible to track the cancer stem cells.

Keywords: hydrogel, alginate, reduced graphene oxide, collagen

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Authors: Chevonne Reynolds, Res Altwegg, Andrew Balmford, Claire N. Spottiswoode

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Modern agriculture has revolutionised the planet’s capacity to support humans, yet has simultaneously had a greater negative impact on biodiversity than any other human activity. Balancing the demand for food with the conservation of biodiversity is one of the most pressing issues of our time. Biodiversity-friendly farming (‘land sharing’), or alternatively, separation of conservation and production activities (‘land sparing’), are proposed as two strategies for mediating the trade-off between agriculture and biodiversity. However, there is much debate regarding the efficacy of each strategy, as this trade-off has typically been addressed by short term studies at fine spatial scales. These studies ignore processes that are relevant to biodiversity at larger scales, such as meta-population dynamics and landscape connectivity. Therefore, to better understand species response to agricultural land-use and provide evidence to underpin the planning of better production landscapes, we need to determine the merits of each strategy at larger scales. In South Africa, a remarkable citizen science project - the South African Bird Atlas Project 2 (SABAP2) – collates an extensive dataset describing the occurrence of birds at a 5-min by 5-min grid cell resolution. We use these data, along with fine-resolution data on agricultural land-use, to determine which strategy optimises the agriculture-biodiversity trade-off in a southern African context, and at a spatial scale never considered before. To empirically test this trade-off, we model bird species population density, derived for each 5-min grid cell by Royle-Nicols single-species occupancy modelling, against both the amount and configuration of different types of agricultural production in the same 5-min grid cell. In using both production amount and configuration, we can show not only how species population densities react to changes in yield, but also describe the production landscape patterns most conducive to conservation. Furthermore, the extent of both the SABAP2 and land-cover datasets allows us to test this trade-off across multiple regions to determine if bird populations respond in a consistent way and whether results can be extrapolated to other landscapes. We tested the land sparing/sharing trade-off for 281 bird species across three different biomes in South Africa. Overall, a higher proportion of species are classified as losers, and would benefit from land sparing. However, this proportion of loser-sparers is not consistent and varies across biomes and the different types of agricultural production. This is most likely because of differences in the intensity of agricultural land-use and the interactions between the differing types of natural vegetation and agriculture. Interestingly, we observe a higher number of species that benefit from agriculture than anticipated, suggesting that agriculture is a legitimate resource for certain bird species. Our results support those seen at smaller scales and across vastly different agricultural systems, that land sparing benefits the most species. However, our analysis suggests that land sparing needs to be implemented at spatial scales much larger than previously considered. Species persistence in agricultural landscapes will require the conservation of large tracts of land, and is an important consideration in developing countries, which are undergoing rapid agricultural development.

Keywords: agriculture, birds, land sharing, land sparing

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Authors: Amdy Moustapha Drame, Ilya V. Germashev, E. A. Markushevskaya

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Among the main problems of medicine is breast cancer, from which a significant number of women around the world are constantly dying. Therefore, the detection of malignant breast tumors is an urgent task. For many years, various technologies for detecting these tumors have been used, in particular, in thermal imaging in order to determine different levels of breast cancer development. These periodic screening methods are a diagnostic tool for women and may have become an alternative to older methods such as mammography. This article proposes a model for the identification of malignant neoplasms of the mammary glands by the asymmetry of internal and external thermal imaging fields.

Keywords: asymmetry, breast cancer, tumors, deep learning, thermogram, convolutional transformation, classification

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Authors: Benoit Atge, Alice Dhersin, Oscar Da Silva Cacao, Beatrice Martinez, Dominique Ducint, Catherine Verdun-Esquer, Isabelle Baldi, Mathieu Molimard, Antoine Villa, Mireille Canal-Raffin

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Introduction: Healthcare workers' exposure to antineoplastic drugs (AD) is a burning issue for occupational medicine practitioners. Biological monitoring of occupational exposure (BMOE) is an essential tool for assessing AD contamination of healthcare workers. In addition to BMOE, surface sampling is a useful tool in order to understand how workers get contaminated, to identify sources of environmental contamination, to verify the effectiveness of surface decontamination way and to ensure monitoring of these surfaces. The objective of this work was to develop a complete tool including a kit for surface sampling and a quantification analytical method for AD traces detection. The development was realized with the three following criteria: the kit capacity to sample in every professional environment (healthcare services, veterinaries, etc.), the detection of very low AD traces with a validated analytical method and the easiness of the sampling kit use regardless of the person in charge of sampling. Material and method: AD mostly used in term of quantity and frequency have been identified by an analysis of the literature and consumptions of different hospitals, veterinary services, and home care settings. The kind of adsorbent device, surface moistening solution and mix of solvents for the extraction of AD from the adsorbent device have been tested for a maximal yield. The AD quantification was achieved by an ultra high-performance liquid chromatography method coupled with tandem mass spectrometry (UHPLC-MS/MS). Results: With their high frequencies of use and their good reflect of the diverse activities through healthcare, 15 AD (cyclophosphamide, ifosfamide, doxorubicin, daunorubicin, epirubicin, 5-FU, dacarbazin, etoposide, pemetrexed, vincristine, cytarabine, methothrexate, paclitaxel, gemcitabine, mitomycin C) were selected. The analytical method was optimized and adapted to obtain high sensitivity with very low limits of quantification (25 to 5000ng/mL), equivalent or lowest that those previously published (for 13/15 AD). The sampling kit is easy to use, provided with a didactic support (online video and protocol paper). It showed its effectiveness without inter-individual variation (n=5/person; n= 5 persons; p=0,85; ANOVA) regardless of the person in charge of sampling. Conclusion: This validated tool (sampling kit + analytical method) is very sensitive, easy to use and very didactic in order to control the chemical risk brought by AD. Moreover, BMOE permits a focal prevention. Used in routine, this tool is available for every intervention of occupational health.

Keywords: surface contamination, sampling kit, analytical method, sensitivity

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Authors: M. A. Ben Messaoud, A. Bouzid, N. Ellouze

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Speech Segmentation is the measure of the change point detection for partitioning an input speech signal into regions each of which accords to only one speaker. In this paper, we apply two features based on multi-scale product (MP) of the clean speech, namely the spectral centroid of MP, and the zero crossings rate of MP. We focus on multi-scale product analysis as an important tool for segmentation extraction. The multi-scale product is based on making the product of the speech wavelet transform coefficients at three successive dyadic scales. We have evaluated our method on the Keele database. Experimental results show the effectiveness of our method presenting a good performance. It shows that the two simple features can find word boundaries, and extracted the segments of the clean speech.

Keywords: multiscale product, spectral centroid, speech segmentation, zero crossings rate

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Authors: Teguh Sugiarto, Amir Mohamadian Amiri

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Background: This study aims to explore if there is fraud in the company's financial report distribution using the number first digit Lucas, Fibonacci and Benford. Research methods: In this study, the author uses a number model contained in the first digit of the model Lucas, Fibonacci and Benford, to make a distinction between implementation by using the scale above and below 5%, the rate of occurrence of a difference against the digit number contained on Lucas, Fibonacci and Benford. If there is a significant difference above and below 5%, then the process of follow-up and detection of occurrence of fraud against the financial statements can be made. Findings: From research that has been done can be concluded that the number of frequency levels contained in the financial statements of PT Bank BRI Tbk in a year in the same conscientious results for model Lucas, Fibonacci and Benford.

Keywords: Lucas, Fibonacci, Benford, first digit

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Authors: Amrane Meriem, Oukid Salyha

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The presence of defects or bugs in software can lead to costly setbacks, operational inefficiencies, and compromised user experiences. The integration of Machine Learning(ML) techniques has emerged to predict and preemptively address software defects. ML represents a proactive strategy aimed at identifying potential anomalies, errors, or vulnerabilities within code before they manifest as operational issues. By analyzing historical data, such as code changes, feature im- plementations, and defect occurrences. This en- ables development teams to anticipate and mitigate these issues, thus enhancing software quality, reducing maintenance costs, and ensuring smoother user interactions. In this work, we used a recommendation system to improve the performance of ML models in terms of predicting the code severity and effort estimation.

Keywords: software engineering, machine learning, bugs detection, effort estimation

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Authors: Mehrdad Nouri Khajavi, Gollamhassan Payganeh, Mohsen Fallah Tafti

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Fault diagnosis is an important aspect of maintaining rotating machinery health and increasing productivity. Many researches has been done in this regards. Many faults such as unbalance, misalignment, looseness, bearing faults, etc. have been considered and diagnosed with different techniques. Most of the researches in fault diagnosis of rotating machinery deal with single fault. Where as in reality faults usually occur simultaneously and it is, therefore, necessary to recognize them at the same time. In this research, two of the most common faults namely unbalance and misalignment have been considered simultaneously with different intensity and then identified and classified with the use of Multi-Layer Perception Neural Network (MLPNN). Processed Vibration signals are used as the input to the MLPNN, and the class of mixed unbalancy, and misalignment is the output of the NN.

Keywords: unbalance, parallel misalignment, combined faults, vibration signals

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Authors: Mahbube Eghtesad

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For a number of years, the problem of simultaneous detection and tracking of a target has been one of the most relevant and challenging issues in a wide variety of military and civilian systems. We develop methods for detecting and tracking a target using an orthogonal frequency division multiplexing (OFDM) based radar. As a preliminary step we introduce the target trajectory and Gaussian noise model in discrete time form. Then resorting to match filter and Kalman filter we derive a detector and target tracker. After that we propose an OFDM radar in order to achieve further improvement in tracking performance. The motivation for employing multiple frequencies is that the different scattering centers of a target resonate differently at each frequency. Numerical examples illustrate our analytical results, demonstrating the achieved performance improvement due to the OFDM signaling method.

Keywords: matched filter, target trashing, OFDM radar, Kalman filter

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Authors: Azadeh Farzaneh, Mohammad Reza Abdi, A. Quaranta, Matteo Dalla Palma, Seyedshahram Mortazavi

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We report on the synthesis of cesium-iodide nanoparticles using sol-gel technique. The structural properties of CsI nanoparticles were characterized by X-ray diffraction and Scanning Electron Microscope (SEM) Also, optical properties were followed by optical absorption and UV–vis fluorescence. Intense photoluminescence is also observed, with some spectral tuning possible with ripening time getting a range of emission photon wavelength approximately from 366 to 350 nm. The size effect on CsI luminescence leads to an increase in scintillation light yield, a redshift of the emission bands of the on_center and off_center self_trapped excitons (STEs) and an increase in the contribution of the off_center STEs to the net intrinsic emission yield. The energy transfer from the matrix to CsI nanoparticles is a key characteristic for scintillation detectors. So the scintillation spectra to alpha particles of sample were monitored.

Keywords: nanoparticles, luminescence, sol gel, scintillator

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Authors: Slimane Ouhmad, Abdellah Halimi

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In this work, we have applied neural networks method MLP type to a database from an array of six sensors for the detection of three toxic gases. As the choice of the number of hidden layers and the weight values has a great influence on the convergence of the learning algorithm, we proposed, in this article, a mathematical formulation to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases on the one hand, and optimize the computation time on the other hand, the comparison to other results achieved in this case.

Keywords: MLP Neural Network, back-propagation, number of neurons in the hidden layer, identification, computing time

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Authors: Haidy Weliam Megaly Gouda

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Dermatology is often a neglected specialty in low-resource settings despite the high morbidity associated with skin disease. This becomes even more significant when associated with HIV infection, as dermatological conditions are more common and aggressive in HIV-positive patients. African countries have the highest HIV infection rates, and skin conditions are frequently misdiagnosed and mismanaged because of a lack of dermatological training and educational material. The frequent lack of diagnostic tests in the African setting renders basic clinical skills all the more vital. This project aimed to improve the diagnosis and treatment of skin disease in the HIV population in a district hospital in Malawi. A basic dermatological clinical tool was developed and produced in collaboration with local staff and based on available literature and data collected from clinics. The aim was to improve diagnostic accuracy and provide guidance for the treatment of skin disease in HIV-positive patients. A literature search within Embassy, Medline and Google Scholar was performed and supplemented through data obtained from attending 5 Antiretroviral clinics. From the literature, conditions were selected for inclusion in the resource if they were described as specific, more prevalent, or extensive in the HIV population or have more adverse outcomes if they develop in HIV patients. Resource-appropriate treatment options were decided using Malawian Ministry of Health guidelines and textbooks specific to African dermatology. After the collection of data and discussion with local clinical and pharmacy staff, a list of 15 skin conditions was included, and a booklet was created using the simple layout of a picture, a diagnostic description of the disease and treatment options. Clinical photographs were collected from local clinics (with full consent of the patient) or from the book ‘Common Skin Diseases in Africa’ (permission granted if fully acknowledged and used in a not-for-profit capacity). This tool was evaluated by the local staff alongside an educational teaching session on skin disease. This project aimed to reduce uncertainty in diagnosis and provide guidance for appropriate treatment in HIV patients by gathering information into one practical and manageable resource. To further this project, we hope to review the effectiveness of the tool in practice.

Keywords: prevalence and pattern of skin diseases, impact on quality of life, rural Nepal, interventions, quality switched ruby laser, skin color river blindness, clinical signs, circularity index, grey level run length matrix, grey level co-occurrence matrix, local binary pattern, object detection, ring detection, shape identification

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Authors: Ahmad B. Musamih, Ahmad A. Albloushi, Ahmed H. Alshemeili, Abdulaziz Y. Alfili, Ala A. Hussien

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This paper proposes a universal wireless battery charger design for portable electronic devices. As the number of portable electronics devices increases, the demand for more flexible and reliable charging techniques is becoming more urgent. A wireless battery charger differs from a traditional charger in the way the power transferred to the battery. In the latter, the power is transferred through electrical wires that connect the charger terminals to the battery terminals, while in the former; the power is transferred by induction without electrical connections. With a detection algorithm that detects the battery size and chemistry, the proposed charger will be able to accommodate a wide range of applications, and will allow a more flexible and reliable option to most of today’s portable electronics.

Keywords: efficiency, magnetically-coupled resonators, resonance frequency, wireless power transfer

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Authors: Tony R. Walker, N. Devin MacAskill, Andrew Thalhiemer

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Sydney Harbour, Nova Scotia has long been subject to effluent and atmospheric inputs of metals, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs) from a large coking operation and steel plant that operated in Sydney for nearly a century until closure in 1988. Contaminated effluents from the industrial site resulted in the creation of the Sydney Tar Ponds, one of Canada’s largest contaminated sites. Since its closure, there have been several attempts to remediate this former industrial site and finally, in 2004, the governments of Canada and Nova Scotia committed to remediate the site to reduce potential ecological and human health risks to the environment. The Sydney Tar Ponds and Coke Ovens cleanup project has become the most prominent remediation project in Canada today. As an integral part of remediation of the site (i.e., which consisted of solidification/stabilization and associated capping of the Tar Ponds), an extensive multiple media environmental effects program was implemented to assess what effects remediation had on the surrounding environment, and, in particular, harbour sediments. Additionally, longer-term natural sediment recovery rates of select contaminants predicted for the harbour sediments were compared to current conditions. During remediation, potential contributions to sediment quality, in addition to remedial efforts, were evaluated which included a significant harbour dredging project, propeller wash from harbour traffic, storm events, adjacent loading/unloading of coal and municipal wastewater treatment discharges. Two sediment sampling methodologies, sediment grab and gravity corer, were also compared to evaluate the detection of subtle changes in sediment quality. Results indicated that overall spatial distribution pattern of historical contaminants remains unchanged, although at much lower concentrations than previously reported, due to natural recovery. Measurements of sediment indicator parameter concentrations confirmed that natural recovery rates of Sydney Harbour sediments were in broad agreement with predicted concentrations, in spite of ongoing remediation activities. Overall, most measured parameters in sediments showed little temporal variability even when using different sampling methodologies, during three years of remediation compared to baseline, except for the detection of significant increases in total PAH concentrations noted during one year of remediation monitoring. The data confirmed the effectiveness of mitigation measures implemented during construction relative to harbour sediment quality, despite other anthropogenic activities and the dynamic nature of the harbour.

Keywords: contaminated sediment, monitoring, recovery, remediation

Procedia PDF Downloads 223

Authors: Ewelina Wisnik, Zsolt Regdon, Kinga Chmielewska, Laszlo Virag, Agnieszka Robaszkiewicz

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Apart from protecting genome, PARP1 has been documented to regulate many intracellular processes inter alia gene transcription by physically interacting with chromatin bound proteins and by their ADP-ribosylation. Our recent findings indicate that expression of PARP1 decreases during the differentiation of human CD34+ hematopoietic stem cells to monocytes as a consequence of differentiation-associated cell growth arrest and formation of E2F4-RBL2-HDAC1-SWI/SNF repressive complex at the promoter of this gene. Since the RBL2 complexes repress genes in a E2F-dependent manner and are widespread in the genome in G0 arrested cells, we asked (a) if RBL2 directly contributes to defining monocyte phenotype and function by targeting gene promoters and (b) if RBL2 controls gene transcription indirectly by repressing PARP1. For identification of genes controlled by RBL2 and/or PARP1,we used primer libraries for surface receptors and TLR signaling mediators, genes were silenced by siRNA or shRNA, analysis of gene promoter occupation by selected proteins was carried out by ChIP-qPCR, while statistical analysis in GraphPad Prism 5 and STATISTICA, ChIP-Seq data were analysed in Galaxy 2.5.0.0. On the list of 28 genes regulated by RBL2, we identified only four solely repressed by RBL2-E2F4-HDAC1-BRM complex. Surprisingly, 24 out of 28 emerged genes controlled by RBL2 were co-regulated by PARP1 in six different manners. In one mode of RBL2/PARP1 co-operation, represented by MAP2K6 and MAPK3, PARP1 was found to associate with gene promoters upon RBL2 silencing, which was previously shown to restore PARP1 expression in monocytes. PARP1 effect on gene transcription was observed only in the presence of active EP300, which acetylated gene promoters and activated transcription. Further analysis revealed that PARP1 binding to MA2K6 and MAPK3 promoters enabled recruitment of EP300 in monocytes, while in proliferating cancer cell lines, which actively transcribe PARP1, this protein maintained EP300 at the promoters of MA2K6 and MAPK3. Genome-wide analysis revealed a similar distribution of PARP1 and EP300 around transcription start sites and the co-occupancy of some gene promoters by PARP1 and EP300 in cancer cells. Here, we described a new RBL2/PARP1/EP300 axis which controls gene transcription regardless of the cell type. In this model cell, cycle-dependent transcription of PARP1 regulates expression of some genes repressed by RBL2 upon cell cycle limitation. Thus, RBL2 may indirectly regulate transcription of some genes by controlling the expression of EP300-recruiting PARP1. Acknowledgement: This work was financed by Polish National Science Centre grants nr DEC-2013/11/D/NZ2/00033 and DEC-2015/19/N/NZ2/01735. L.V. is funded by the National Research, Development and Innovation Office grants GINOP-2.3.2-15-2016-00020 TUMORDNS, GINOP-2.3.2-15-2016-00048-STAYALIVE and OTKA K112336. AR is supported by Polish Ministry of Science and Higher Education 776/STYP/11/2016.

Keywords: retinoblastoma transcriptional co-repressor like 2 (RBL2), poly(ADP-ribose) polymerase 1 (PARP1), E1A binding protein p300 (EP300), monocytes

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Authors: Judita Konečná, Dagmar Halo, Martin Matoulek

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Background: Personality pathology does not have to be a contraindication nor an obstacle in obesity treatment, or eventually, surgical treatment. Detection of specific maladaptive personality traits can help us understand the manner of behavior leading to obesity as well as to address the treatment better. Objective: Using The Personality Inventory for DSM-5 (PID-5) in combination with clinical interviews with the goal of gaining a psychological evaluation to set the treatment procedure. Data was collected from more than 400 patients to detect differences in constellations of maladaptive personality traits based on BMI, DM2 and gender. Conclusions: Besides the fact that a psychological evaluation can help address the treatment better, analyses showed that it is also useful to detect specific groups of patients. Implications for clinical practice are discussed, as well as recommendations for group education programs based on quantitative research.

Keywords: bariatric surgery, obesity, personality traits, PID-5, treatment

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Authors: Khaled Mohammed

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This paper presents novel face recognition and identification approaches for the real-time attendance management problem in large companies/factories and government institutions. The proposed uses the Minimum Ratio (MR) approach for employee identification. Capturing the authentic face variability from a sequence of video frames has been considered for the recognition of faces and resulted in system robustness against the variability of facial features. Experimental results indicated an improvement in the performance of the proposed system compared to the Previous approaches at a rate between 2% to 5%. In addition, it decreased the time two times if compared with the Previous techniques, such as Extreme Learning Machine (ELM) & Multi-Scale Structural Similarity index (MS-SSIM). Finally, it achieved an accuracy of 99%.

Keywords: attendance management system, face detection and recognition, live face recognition, minimum ratio

Procedia PDF Downloads 141

Authors: David Oluigbo, Erik Hemberg, Nathan Shwatal, Wenqi Ding, Yin Yuan, Susanna Mierau

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Introduction: Calcium imaging is an established technique in neuroscience research for detecting activity in neural networks. Bursts of action potentials in neurons lead to transient increases in intracellular calcium visualized with fluorescent indicators. Manual identification of cell bodies and their contours by experts typically takes 10-20 minutes per calcium imaging recording. Our aim, therefore, was to design an automated pipeline to facilitate and optimize calcium imaging data analysis. Our pipeline aims to accelerate cell body and contour identification and production of graphical representations reflecting changes in neuronal calcium-based fluorescence. Methods: We created a Python-based pipeline that uses OpenCV (a computer vision Python package) to accurately (1) detect neuron contours, (2) extract the mean fluorescence within the contour, and (3) identify transient changes in the fluorescence due to neuronal activity. The pipeline consisted of 3 Python scripts that could both be easily accessed through a Python Jupyter notebook. In total, we tested this pipeline on ten separate calcium imaging datasets from murine dissociate cortical cultures. We next compared our automated pipeline outputs with the outputs of manually labeled data for neuronal cell location and corresponding fluorescent times series generated by an expert neuroscientist. Results: Our results show that our automated pipeline efficiently pinpoints neuronal cell body location and neuronal contours and provides a graphical representation of neural network metrics accurately reflecting changes in neuronal calcium-based fluorescence. The pipeline detected the shape, area, and location of most neuronal cell body contours by using binary thresholding and grayscale image conversion to allow computer vision to better distinguish between cells and non-cells. Its results were also comparable to manually analyzed results but with significantly reduced result acquisition times of 2-5 minutes per recording versus 10-20 minutes per recording. Based on these findings, our next step is to precisely measure the specificity and sensitivity of the automated pipeline’s cell body and contour detection to extract more robust neural network metrics and dynamics. Conclusion: Our Python-based pipeline performed automated computer vision-based analysis of calcium image recordings from neuronal cell bodies in neuronal cell cultures. Our new goal is to improve cell body and contour detection to produce more robust, accurate neural network metrics and dynamic graphs.

Keywords: calcium imaging, computer vision, neural activity, neural networks

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Authors: Smita Pallavi, Raj Ratn Pranesh, Sumit Kumar

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Information representation as tables is compact and concise method that eases searching, indexing, and storage requirements. Extracting and cloning tables from parsable documents is easier and widely used; however, industry still faces challenges in detecting and extracting tables from OCR (Optical Character Recognition) documents or images. This paper proposes an algorithm that detects and extracts multiple tables from OCR document. The algorithm uses a combination of image processing techniques, text recognition, and procedural coding to identify distinct tables in the same image and map the text to appropriate the corresponding cell in dataframe, which can be stored as comma-separated values, database, excel, and multiple other usable formats.

Keywords: table extraction, optical character recognition, image processing, text extraction, morphological transformation

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Authors: Mustapha Aminu Bagiwa, Ainuddin Wahid Abdul Wahab, Mohd Yamani Idna Idris, Suleman Khan

Abstract:

Video source device identification has become a problem of concern in numerous domains especially in multimedia security and digital investigation. This is because videos are now used as evidence in legal proceedings. Source device identification aim at identifying the source of digital devices using the content they produced. However, due to affordable processing tools and the influx in digital content generating devices, source device identification is still a major problem within the digital forensic community. In this paper, we discuss source device identification for digital videos by identifying techniques that were proposed in the literature for model or specific device identification. This is aimed at identifying salient open challenges for future research.

Keywords: video forgery, source camcorder, device identification, forgery detection

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Authors: A. Boveiri Monji, H. Yousefnia, S. Zolghadri, B. Salimi

Abstract:

The main goal of this paper was to make use of green reagents as a substitute of perilous synthetic reagents and organic solvents for spectrophotometric determination of hafnium(IV). The extracts taken from six different kinds of tree leaves including Acer negundo, Ficus carica, Cerasus avium, Chimonanthus, Salix babylonica and Pinus brutia, were applied as green reagents for the experiments. In 6-M hydrochloric acid, hafnium reacted with the reagent to form a yellow product and showed maximum absorbance at 421 nm. Among tree leaves, Chimonanthus showed satisfactory results with a molar absorptivity value of 0.61 × 10⁴ l mol^-1 cm^-1 and the method was linear in the 0.3-9 µg mL ^-1 concentration range. The detection limit value was 0.064 µg mL^-1. The proposed method was simple, low cost, clean, and selective.

Keywords: hafnium, spectrophotometric determination, synthetic reagents, tree leaves

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