Search results for: neural networking algorithm

Authors: Tracy L. F. Worley

Abstract:

This presentation explores the environment that has limited leadership opportunities for Black women in cinema and advocates for autonomy among Black women filmmakers that is facilitated by strong internal and external networks and cooperative opportunities. Early images of African Americans in motion pictures were often conceptualized from the viewpoint of a White male director and depicted by White actors. The black film evolved in opposition to this context, leading to a Black film aesthetic. The oppositional context created in response to racist, misogynistic, and sexist representations in motion pictures sets the tone for female filmmakers of every hue – but especially for African American women. For them, the context of a male gaze, and for all intents and purposes, a White male gaze, forces them to create their own aesthetic. Theoretically, men and women, filmmakers and spectators have different perspectives across race, ethnicity, and gender. Two feminist theorists, bell hooks and Mary Ann Doane, suggest that female filmmakers are perceived as disparate from male filmmakers and that women, in general, are defined by what men see. Mary Ann Doane, a White feminist film theorist, has focused extensively on female spectatorship and women (White) in general as the object of the male gaze. Her discussion of the female body, male perception of it, and feminism in the motion picture industry support the suggestion that comprehending the organization and composition of Hollywood is critical to understanding women’s roles in the industry. Although much of her research addresses the silent film era and women’s roles then, Doane suggests that across cinematic periods, the theory assigned to “cinematic apparatus” is formulated within a context of sexuality. Men and women are viewed and treated differently in cinema (in front of and behind the camera), with women’s attractiveness and allure photographed specifically for the benefit of the “spectatorial desire” of the male gaze. Bell Hooks, an African American feminist writer and theorist with more than 30 published books and articles on race, gender, class, and culture in feminism and education, suggests that women can overcome the male gaze by using their “oppositional gaze” to transform reality and establish their own truth. She addresses gender within the context of race by acknowledging the realities faced by African American women and the fact that the feminist movement was never intended to include Black women. A grounded theory study led to the development of a leadership theory that explains why African American women are disproportionately represented in a mainstream motion picture leadership. The study helped to reveal the barriers to entry and illuminated potential strategies that African American female motion picture directors might pursue to reduce this inequity. Using semi-structured interviews as the primary means for data collection, the lived experiences of African American female directors and organizational leadership’s perceived role in the perpetuation of negative female imagery in major motion pictures led to the identification of support strategies for African American female motion picture directors that counter social stereotyping and validate the need for social networking in the mainstream.

Keywords: African American, cinema, directors, filmmaking, leadership, women

Procedia PDF Downloads 68

Authors: Vahid Nademi

Abstract:

In response to widely used wearable medical devices equipped with a continuous glucose monitor (CGM) and insulin pump, the advanced control methods are still demanding to get the full benefit of these devices. Unlike costly clinical trials, implementing effective insulin-glucose control strategies can provide significant contributions to the patients suffering from chronic diseases such as diabetes. This study deals with a key role of two-layer insulin-glucose regulator based on model-predictive-control (MPC) scheme so that the patient’s predicted glucose profile is in compliance with the insulin level injected through insulin pump automatically. It is achieved by iterative optimization algorithm which is called an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) solver for handling uncertainties due to unexpected variations in glucose-insulin values and body’s characteristics. The feasibility evaluation of the discussed control approach is also studied by means of numerical simulations of two case scenarios via measured data. The obtained results are presented to verify the superior and reliable performance of the proposed control scheme with no negative impact on patient safety.

Keywords: blood glucose monitoring, insulin pump, predictive control, optimization

Procedia PDF Downloads 136

Authors: Umit Cali

Abstract:

The integration of intermittent energy sources like wind farms into the electricity grid has become an important challenge for the utilization and control of electric power systems, because of the fluctuating behaviour of wind power generation. Wind power predictions improve the economic and technical integration of large amounts of wind energy into the existing electricity grid. Trading, balancing, grid operation, controllability and safety issues increase the importance of predicting power output from wind power operators. Therefore, wind power forecasting systems have to be integrated into the monitoring and control systems of the transmission system operator (TSO) and wind farm operators/traders. The wind forecasts are relatively precise for the time period of only a few hours, and, therefore, relevant with regard to Spot and Intraday markets. In this work predictive data mining techniques are applied to identify a statistical and neural network model or set of models that can be used to predict wind power output of large onshore and offshore wind farms. These advanced data analytic methods helps us to amalgamate the information in very large meteorological, oceanographic and SCADA data sets into useful information and manageable systems. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast allows grid operators to schedule economically efficient generation to meet the demand of electrical customers. This study is also dedicated to an in-depth consideration of issues such as the comparison of day ahead and the short-term wind power forecasting results, determination of the accuracy of the wind power prediction and the evaluation of the energy economic and technical benefits of wind power forecasting.

Keywords: renewable energy sources, wind power, forecasting, data mining, big data, artificial intelligence, energy economics, power trading, power grids

Procedia PDF Downloads 519

Authors: Mohsen S. Sajadieha, Danyar Molavia

Abstract:

In this paper, a truck scheduling problem is investigated at a two-touch cross-docking center with due dates for outbound trucks as a hard constraint. The objective is to minimize the total cost comprising penalty and delivery cost of delayed shipments. The sequence of unloading shipments is considered and is assumed that shipments are sent to shipping dock doors immediately after unloading and a First-In-First-Out (FIFO) policy is considered for loading the shipments. A mixed integer programming model is developed for the proposed model. Two meta-heuristic algorithms including genetic algorithm (GA) and variable neighborhood search (VNS) are developed to solve the problem in medium and large sized scales. The numerical results show that increase in due dates for outbound trucks has a crucial impact on the reduction of penalty costs of delayed shipments. In addition, by increase the due dates, the improvement in the objective function arises on average in comparison with the situation that the cross-dock is multi-touch and shipments are sent to shipping dock doors only after unloading the whole inbound truck.

Keywords: cross-docking, truck scheduling, fixed due date, door assignment

Procedia PDF Downloads 405

Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

Abstract:

This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

Procedia PDF Downloads 497

Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

Abstract:

Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies.

Keywords: runoff, roughness coefficient, PAR, WRM model

Procedia PDF Downloads 378

Authors: M. M. Qasaymeh, M. A. Khodeir

Abstract:

Subspace channel estimation methods have been studied widely. It depends on subspace decomposition of the covariance matrix to separate signal subspace from noise subspace. The decomposition normally is done by either Eigenvalue Decomposition (EVD) or Singular Value Decomposition (SVD) of the Auto-Correlation matrix (ACM). However, the subspace decomposition process is computationally expensive. In this paper, the multipath channel estimation problem for a Slow Frequency Hopping (SFH) system using noise space based method is considered. An efficient method to estimate multipath the time delays basically is proposed, by applying MUltiple Signal Classification (MUSIC) algorithm which used the null space extracted by the Rank Revealing LU factorization (RRLU). The RRLU provides accurate information about the rank and the numerical null space which make it a valuable tool in numerical linear algebra. The proposed novel method decreases the computational complexity approximately to the half compared with RRQR methods keeping the same performance. Computer simulations are also included to demonstrate the effectiveness of the proposed scheme.

Keywords: frequency hopping, channel model, time delay estimation, RRLU, RRQR, MUSIC, LS-ESPRIT

Procedia PDF Downloads 410

Authors: Sanjib Kr Pal, S. Bhattacharyya

Abstract:

Mixed convection of Cu-water nanofluid in an enclosure with thick wavy bottom wall has been investigated numerically. A co-ordinate transformation method is used to transform the computational domain into an orthogonal co-ordinate system. The governing equations in the computational domain are solved through a pressure correction based iterative algorithm. The fluid flow and heat transfer characteristics are analyzed for a wide range of Richardson number (0.1 ≤ Ri ≤ 5), nanoparticle volume concentration (0.0 ≤ ϕ ≤ 0.2), amplitude (0.0 ≤ α ≤ 0.1) of the wavy thick- bottom wall and the wave number (ω) at a fixed Reynolds number. Obtained results showed that heat transfer rate increases remarkably by adding the nanoparticles. Heat transfer rate is dependent on the wavy wall amplitude and wave number and decreases with increasing Richardson number for fixed amplitude and wave number. The Bejan number and the entropy generation are determined to analyze the thermodynamic optimization of the mixed convection.

Keywords: conjugate heat transfer, mixed convection, nano fluid, wall waviness

Procedia PDF Downloads 255

Authors: Elisa Coraggio, Dawei Han, Weiru Liu, Theo Tryfonas

Abstract:

Water is one of the most important resources for human society. The world is currently undergoing a wave of urban growth, and pollution problems are of a great impact. Monitoring water quality is a key task for the future of the environment and human species. In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for environmental monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the artificial intelligence algorithm. This study derives the methodology and demonstrates its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for the environment monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a new methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the Artificial Intelligence algorithm. This study derives the methodology and demonstrate its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.

Keywords: artificial intelligence, hydroinformatics, numerical modelling, smart cities, water quality

Procedia PDF Downloads 189

Authors: Ermias A. Tegegn, Million Meshesha

Abstract:

Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

Procedia PDF Downloads 142

Authors: Mohd Ali Abbas Zaidi, Meenu Sachdeva, Jonaki Sen

Abstract:

In the vertebrate embryo, the forebrain anlagen develops from the anterior-most region of the neural tube which is the precursor of the central nervous system (CNS). The roof plate located at the dorsal midline region of the forebrain anlagen, acts as a source of several secreted molecules involved in patterning and morphogenesis of the forebrain. One such key morphogenetic event is the invagination of the forebrain roof plate which results in separation of the single forebrain vesicle into two cerebral hemispheres. Retinoic acid (RA) signaling plays a key role in this process. Blocking RA signaling at the dorsal forebrain midline inhibits dorsal invagination and results in the absence of certain key features of this region, such as thinning of the neuroepithelium and a lowering of cell proliferation. At present we are investigating the possibility of other signaling pathways acting in concert with RA signaling to regulate this process. We have focused on BMP signaling, which we found to be active in a mutually exclusive domain to that of RA signaling within the roof plate. We have also observed that there is a change in BMP signaling activity on modulation of RA signaling indicating an antagonistic relationship between the two. Moreover, constitutive activation of BMP signaling seems to completely inhibit thinning and partially affect invagination, leaving the lowering of cell proliferation in the midline unaffected. We are employing in-silico modeling as well as molecular manipulations to investigate the relative contribution if any, of regional differences in rates of cell proliferation and thinning of the neuroepithelium towards the process of invagination. We have found expression of certain cell adhesion molecules in forebrain roof-plate whose mRNA localization across the thickness of neuroepithelium is influenced by Bmp and RA signaling, giving regional rigidity to roof plate and assisting invagination. We also found expression of certain cytoskeleton modifiers in a localized small domains in invaginating forebrain roof plate suggesting that midline invagination is under control of many factors.

Keywords: bone morphogenetic signaling, cytoskeleton, cell adhesion molecules, forebrain roof plate, retinoic acid signaling

Procedia PDF Downloads 156

Authors: Ashwag O. Maghraby, Nida N. Khan, Hosnia A. Ahmed, Ghufran N. Brohi, Hind F. Assouli, Jawaher S. Melibari

Abstract:

The Arabic language is one of the most important languages. Learning it is so important for many people around the world because of its religious and economic importance and the real challenge lies in practicing it without grammatical or syntactical mistakes. This research focused on detecting and correcting the syntactic mistakes of Arabic syntax according to their position in the sentence and focused on two of the main syntactical rules in Arabic: Dual and Plural. It analyzes each sentence in the text, using Stanford CoreNLP morphological analyzer and machine-learning approach in order to detect the syntactical mistakes and then correct it. A prototype of the proposed system was implemented and evaluated. It uses support vector machine (SVM) algorithm to detect Arabic grammatical errors and correct them using the rule-based approach. The prototype system has a far accuracy 81%. In general, it shows a set of useful grammatical suggestions that the user may forget about while writing due to lack of familiarity with grammar or as a result of the speed of writing such as alerting the user when using a plural term to indicate one person.

Keywords: Arabic language acquisition and learning, natural language processing, morphological analyzer, part-of-speech

Procedia PDF Downloads 154

Authors: Joaquim de Almeida Dultra, Daiana Cristina Pereira Santana, Fátima Karoline Alves Araújo Dultra, Liliane Akemi Kawano Shibasaki, Mariana Machado Mendes de Carvalho, Ieda Margarida Crusoé Rocha Rebello

Abstract:

Defects originating from mandibular resections can cause significant functional impairment and facial disharmony, and they have complex rehabilitation. The aim of this report is to demonstrate the authors' experience facing challenging rehabilitation after mandibular resection in a patient with ameloblastoma. Clinical and surgical steps are described simultaneously, highlighting the adaptation of the final fixed prosthesis, reported in an unprecedented way in the literature. A 37-year-old male patient was seen after a sports accident, where a pathological fracture in the symphysis and left mandibular body was identified, where a large radiolucent lesion was found. The patient underwent resection, bone graft, distraction osteogenesis, rehabilitation with dental implants, prosthesis, and finally, orofacial harmonization, in an interval of six years. Rehabilitation should consider the patient's needs individually and should have as the main objective to provide similar aesthetics and function to that present before the disease. We also emphasize the importance of interdisciplinary work during the course of rehabilitation.

Keywords: ameloblastoma, mandibular reconstruction, distraction osteogenesis, dental implants. dental prosthesis, implant-supported, treatment outcome

Procedia PDF Downloads 114

Authors: Yong Chen

Abstract:

To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

Procedia PDF Downloads 120

Authors: Zhang Tianci, Ding Meng, Zuo Hongfu, Zeng Lina, Sun Zejun

Abstract:

Automation of airport operations can greatly improve ground movement efficiency. In this paper, we study the speed profile design problem for advanced airport ground movement control and guidance. The problem is constrained by the surface four-dimensional trajectory generated in taxi planning. A decomposed approach of two stages is presented to solve this problem efficiently. In the first stage, speeds are allocated at control points which ensure smooth speed profiles can be found later. In the second stage, detailed speed profiles of each taxi interval are generated according to the allocated control point speeds with the objective of minimizing the overall fuel consumption. We present a swarm intelligence based algorithm for the first-stage problem and a discrete variable driven enumeration method for the second-stage problem since it only has a small set of discrete variables. Experimental results demonstrate the presented methodology performs well on real world speed profile design problems.

Keywords: airport ground movement, fuel consumption, particle swarm optimization, smoothness, speed profile design

Procedia PDF Downloads 583

Authors: Thiago Ribeiro De Alencar, Jacyro Gramulia Junior, Patricia Teixeira Leite Asano

Abstract:

The area of the electricity sector that deals with energy needs by the hydropower and thermoelectric in a coordinated way is called Planning Operating Hydrothermal Power Systems. The aim of this area is to find a political operative to provide electrical power to the system in a specified period with minimization of operating cost. This article proposes a computational tool for solving the planning problem. In addition, this article will be introducing a methodology to find new transfer points between reservoirs increasing energy production in hydroelectric power plants cascade systems. The computational tool proposed in this article applies: i) genetic algorithms to optimize the water transfer and operation of hydroelectric plants systems; and ii) Ant Colony algorithm to find the trajectory with the least energy pumping for the construction of pipes transfer between reservoirs considering the topography of the region. The computational tool has a database consisting of 35 hydropower plants and 41 reservoirs, which are part of the southeastern Brazilian system, which has been implemented in an individualized way.

Keywords: ant colony system, genetic algorithms, hydroelectric, hydrothermal systems, optimization, water transfer between rivers

Procedia PDF Downloads 326

Authors: Hongliang Zhang

Abstract:

The use of chance procedures and randomizers in poetry-writing can be traced back to surrealist works, which, by appealing to Sigmund Freud's theories, were still logocentrism. In the 1960s, random permutation and combination were extensively used by the Oulipo, John Cage and Jackson Mac Low, which further deconstructed the metaphysical presence of writing. Today, the randomly-generated digital poetry has emerged as a genre of cybertext which should be co-authored by readers. At the same time, the classical theories have now been updated by cybernetics and media theories. N· Katherine Hayles put forward the concept of ‘the floating signifiers’ by Jacques Lacan to be the ‘the flickering signifiers’ , arguing that the technology per se has become a part of the textual production. This paper makes a historical review of the computer-generated poetry in the perspective of semiotics, emphasizing that the randomly-generated digital poetry which hands over the dual tasks of both interpretation and writing to the readers demonstrates the intervention of media technology in literature. With the participation of computerized algorithm and programming languages, poems randomly generated by computers have not only blurred the boundary between encoder and decoder, but also raises the issue of human-machine. It is also a significant feature of the cybertext that the productive process of the text is full of randomness.

Keywords: cybertext, digital poetry, poetry generator, semiotics

Procedia PDF Downloads 175

Authors: Md. Nurul Alom Siddikqe

Abstract:

The purpose of this intervention was to describe how the marginalized people protect their rights and increase their self-dignity and self-esteem among brothel-based sex workers in 6 cities which are the victim of trafficked who came from different periphery areas Bangladesh. Eventually the sex workers are tortured by the pimp, clients, Msahi (so called guardian of bonded sex workers), Babu (So called husband) highly discriminated, vulnerable and stigmatized due to their occupation, movement, behavior and activities, which has got social disapproval. However, stigma, discrimination and violation of human rights not only bar them to access legal services, education of their kids, health, movement of outside of brothel, deprived of funeral after death, but also make them inaccessible due to their invisibility. Conducted an assessment among brothel-based sex workers setup to know their knowledge on human rights and find out their harassment and violence in their community. Inspired them to think about to be united and also assisted them to formation of self help group (SHG). Developed capacity of the SHG and developed leadership of its members through different trainings like administrative, financial management, public speaking and resource mobilization. Developed strategy to enhance the capacity of SHG so that they can collectively claim their rights and develop strategic partnership and network with the relevant service provider’s for restoring all sorts of rights. Conducted meeting with stakeholder including duty bearers, civil society organizations, media people and local government initiatives. Developed Networking with human rights commission, local elite, religious leaders and form human right watch committees at community level. Organized rally and observed national and international days along with government counterparts. By utilizing the project resources the members of SHG became capable to raise their collective voices against violence, discrimination and stigma as well as protected them from insecurity. The members of SHG have been participating in social program/event the SHG got membership of district level NGO coordination meeting through invitation from Deputy Commissioner, Civil Surgeon and Social welfare office of Government of Bangladesh. The Law Enforcement Agency is ensuring safety and security and the education department of government enrolled their children in primary level education. The Government provided land for grave yard after death for the Muslim sex workers and same for the other religious group. The SHGs are registered with government respective authorities. The SHGs are working with support from different development partners and implementing different projects sometime as consortium leaders. Opportunity created to take the vocational training from the government reputed department. The harassment by the clients reduced remarkably, babu, Mashi and other counterparts recognized the sex workers rights and ensure security with government counterpart access increased in legal, health and education. Indications are that the brothel based sex workers understood about their rights and became capable of ensuring their security through working under the self-help groups meaningfully.

Keywords: brothel, discrimination, harassment, stigma

Procedia PDF Downloads 359

Authors: Hyeon Su Kim, Sungjin Park, Hae Ryung Chang, Hae Rim Jung, Young Zoo Ahn, Yon Hui Kim, Seungyoon Nam

Abstract:

Ependymoma, one of the most common parenchymal spinal cord tumor, represents 3-6% of all CNS tumor. Especially intracranial ependymomas, which are more frequent in childhood, have a more poor prognosis and more malignant than spinal ependymomas. Although there are growing needs to understand pathogenesis, detailed molecular understanding of pathogenesis remains to be explored. A cancer cell is composed of complex signaling pathway networks, and identifying interaction between genes and/or proteins are crucial for understanding these pathways. Therefore, we explored each ependymoma in terms of differential expressed genes and signaling networks. We used Microsoft Excel™ to manipulate microarray data gathered from NCBI’s GEO Database. To analyze and visualize signaling network, we used web-based PATHOME algorithm and Cytoscape. We show HOX family and NEFL are down-regulated but SCL family is up-regulated in cerebrum and posterior fossa cancers over a spinal cancer, and JAK/STAT signaling pathway and Chemokine signaling pathway are significantly different in the both intracranial ependymoma comparing to spinal ependymoma. We are considering there may be an age-dependent mechanism under different histological pathogenesis. We annotated mutation data of each gene subsequently in order to find potential target genes.

Keywords: systems biology, ependymoma, deg, network analysis

Procedia PDF Downloads 301

Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

Abstract:

In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

Procedia PDF Downloads 109

Authors: Ramesh K. Sunkaria, Puneeta Marwaha

Abstract:

Nonlinear methods of heart rate variability (HRV) analysis are becoming more popular. It has been observed that complexity measures quantify the regularity and uncertainty of cardiovascular RR-interval time series. In the present work, SampEn has been evaluated in healthy Normal Sinus Rhythm (NSR) male and female subjects for different data lengths and tolerance level r. It is demonstrated that SampEn is small for higher values of tolerance r. Also SampEn value of healthy female group is higher than that of healthy male group for short data length and with increase in data length both groups overlap each other and it is difficult to distinguish them. The SampEn gives inaccurate results by assigning higher value to female group, because male subject have more complex HRV pattern than that of female subjects. Therefore, this traditional algorithm exhibits higher complexity for healthy female subjects than for healthy male subjects, which is misleading observation. This may be due to the fact that SampEn do not account for multiple time scales inherent in the physiologic time series and the hidden spatial and temporal fluctuations remains unexplored.

Keywords: heart rate variability, normal sinus rhythm group, RR interval time series, sample entropy

Procedia PDF Downloads 282

Authors: Ting Gao, Mingyue He

Abstract:

Reconfigurable intelligent surfaces (RISs) are expected to be an important part of next-generation wireless communication networks due to their potential to reduce the hardware cost and energy consumption of millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) technology. However, owing to the lack of signal processing abilities of the RIS, the perfect channel state information (CSI) in RIS-assisted communication systems is difficult to acquire. In this paper, the uplink channel estimation for mmWave systems with a hybrid active/passive RIS architecture is studied. Specifically, a deep learning-based estimation scheme is proposed to estimate the channel between the RIS and the user. In particular, the sparse structure of the mmWave channel is exploited to formulate the channel estimation as a sparse reconstruction problem. To this end, the proposed approach is derived to obtain the distribution of non-zero entries in a sparse channel. After that, the channel is reconstructed by utilizing the least-squares (LS) algorithm and compressed sensing (CS) theory. The simulation results demonstrate that the proposed channel estimation scheme is superior to existing solutions even in low signal-to-noise ratio (SNR) environments.

Keywords: channel estimation, reconfigurable intelligent surface, wireless communication, deep learning

Procedia PDF Downloads 154

Authors: Yin Zhang, Kai Qiao, Xiyang Zhi, Jinnan Gong, Jianming Hu

Abstract:

In order to effectively detect aerial targets over long distances, an optimal matching design method of space-based optical payload is proposed. Firstly, main factors affecting optical detectability of small targets under complex environment are analyzed based on the full link of a detection system, including band center, band width and spatial resolution. Then a performance characterization model representing the relationship between image signal-to-noise ratio (SCR) and the above influencing factors is established to describe a detection system. Finally, an optimal matching design example is demonstrated for a typical aerial target by simulating and analyzing its SCR under different scene clutter coupling with multi-scale characteristics, and the optimized detection band and spatial resolution are presented. The method can provide theoretical basis and scientific guidance for space-based detection system design, payload specification demonstration and information processing algorithm optimization.

Keywords: space-based detection, aerial targets, optical system design, detectability characterization

Procedia PDF Downloads 169

Authors: Benseghir Omar, Bahmed Mohamed

Abstract:

In this work, we presented a numerical study of the phenomenon of heat transfer through the laminar, incompressible and steady mixed convection in a closed square cavity with the left vertical wall of the cavity is subjected to a warm temperature, while the right wall is considered to be cold. The horizontal walls are assumed adiabatic. The governing equations were discretized by finite volume method on a staggered mesh and the SIMPLER algorithm was used for the treatment of velocity-pressure coupling. The numerical simulations were performed for a wide range of Reynolds numbers 1, 10, 100, and 1000 numbers are equal to 0.01,0.1 Richardson, 0.5,1 and 10.The analysis of the results shows a flow bicellular (two cells), one is created by the speed of the fan placed in the inner cavity, one on the left is due to the difference between the temperatures right wall and the left wall. Knowledge of the intensity of each of these cells allowed us to get an original result. And the values obtained from each of Nuselt convection which allow to know the rate of heat transfer in the cavity.Finally we find that there is a significant influence on the position of the fan on the heat transfer (Nusselt evolution) for values of Reynolds studied and for low values of Richardson handed this influence is negligible for high values of the latter.

Keywords: thermal transfer, mixed convection, square cavity, finite volume method

Procedia PDF Downloads 433

Authors: Tamanna Goyal, Divya Bansal, Sanjeev Sofat

Abstract:

In world-threatening terrorist attacks, where early detection, distinction, and prediction are effective diagnosis techniques and for functionally accurate and precise analysis of terrorism data, there are so many data mining & statistical approaches to assure accuracy. The computational extraction of derived patterns is a non-trivial task which comprises specific domain discovery by means of sophisticated algorithm design and analysis. This paper proposes an approach for similarity extraction by obtaining the useful attributes from the available datasets of terrorist attacks and then applying feature selection technique based on the statistical impurity measures followed by clustering techniques on the basis of similarity measures. On the basis of degree of participation of attributes in the rules, the associative dependencies between the attacks are analyzed. Consequently, to compute the similarity among the discovered rules, we applied a weighted similarity measure. Finally, the rules are grouped by applying using hierarchical clustering. We have applied it to an open source dataset to determine the usability and efficiency of our technique, and a literature search is also accomplished to support the efficiency and accuracy of our results.

Keywords: association rules, clustering, similarity measure, statistical approaches

Procedia PDF Downloads 322

Authors: Hadj Sahraoui Omar, Kebir Lahcen Wahib, Bennia Ahmed

Abstract:

Interferometric Synthetic Aperture Radar (InSAR) coherence is a crucial parameter for accurately monitoring ground deformation and environmental changes. However, coherence can be degraded by various factors such as temporal decorrelation, atmospheric disturbances, and geometric misalignments, limiting the reliability of InSAR measurements (Omar Hadj‐Sahraoui and al. 2019). To address this challenge, we propose an innovative hybrid approach that combines artificial intelligence (AI) with advanced filtering techniques to optimize interferometric coherence in InSAR data. Specifically, we introduce a Convolutional Neural Network (CNN) integrated with the Lee filter to enhance the performance of radar interferometry. This hybrid method leverages the strength of CNNs to automatically identify and mitigate the primary sources of decorrelation, while the Lee filter effectively reduces speckle noise, improving the overall quality of interferograms. We develop a deep learning-based model trained on multi-temporal and multi-frequency SAR datasets, enabling it to predict coherence patterns and enhance low-coherence regions. This hybrid CNN-SAR with Lee filtering significantly reduces noise and phase unwrapping errors, leading to more precise deformation maps. Experimental results demonstrate that our approach improves coherence by up to 30% compared to traditional filtering techniques, making it a robust solution for challenging scenarios such as urban environments, vegetated areas, and rapidly changing landscapes. Our method has potential applications in geohazard monitoring, urban planning, and environmental studies, offering a new avenue for enhancing InSAR data reliability through AI-powered optimization combined with robust filtering techniques.

Keywords: CNN-SAR, Lee Filter, hybrid optimization, coherence, InSAR phase unwrapping, speckle noise reduction

Procedia PDF Downloads 14

Authors: Hediyeh Talebi, Shokoofeh Ghiam, Changiz Eslahchi

Abstract:

Traditionally, Alzheimer’s disease research has focused on genes with significant fold changes, potentially neglecting subtle but biologically important alterations. Our study introduces an integrative approach that highlights genes crucial to underlying biological processes, regardless of their fold change magnitude. Alzheimer's Single-cell RNA-seq data related to the peripheral blood mononuclear cells (PBMC) was extracted from the Gene Expression Omnibus (GEO). After quality control, normalization, scaling, batch effect correction, and clustering, differentially expressed genes (DEGs) were identified with adjusted p-values less than 0.05. These DEGs were categorized based on cell-type, resulting in four datasets, each corresponding to a distinct cell type. To distinguish between cells from healthy individuals and those with Alzheimer's, an adversarial autoencoder with a classifier was employed. This allowed for the separation of healthy and diseased samples. To identify the most influential genes in this classification, the weight matrices in the network, which includes the encoder and classifier components, were multiplied, and focused on the top 20 genes. The analysis revealed that while some of these genes exhibit a high fold change, others do not. These genes, which may be overlooked by previous methods due to their low fold change, were shown to be significant in our study. The findings highlight the critical role of genes with subtle alterations in diagnosing Alzheimer's disease, a facet frequently overlooked by conventional methods. These genes demonstrate remarkable discriminatory power, underscoring the need to integrate biological relevance with statistical measures in gene prioritization. This integrative approach enhances our understanding of the molecular mechanisms in Alzheimer’s disease and provides a promising direction for identifying potential therapeutic targets.

Keywords: alzheimer's disease, single-cell RNA-seq, neural networks, blood biomarkers

Procedia PDF Downloads 67

Authors: M. P. Dandekar, A. P. Bharne, P. T. Borkar, D. M. Kokare, N. K. Subhedar

Abstract:

Ethanol ingestion by the mother ensue adverse consequences for her offspring. Herein, we examine the behavioral phenotype and neural substrate of the offspring of the mother on ethanol. Female rats were fed with ethanol-containing liquid diet from 8 days prior of conception and continued till 25 days post-parturition to coincide with weaning. Behavioral changes associated with anxiety, depression and learning and memory were assessed in the offspring, after they attained adulthood (day 85), using elevated plus maze (EPM), forced swim (FST) and novel object recognition tests (NORT), respectively. The offspring of the alcoholic mother, compared to those of the pair-fed mother, spent significantly more time in closed arms of EPM and showed more immobility time in FST. Offspring at the age of 25 and 85 days failed to discriminate between novel versus familiar object in NORT, thus reflecting anxiogenic, depressive and amnesic phenotypes. Neuropeptide cocaine- and amphetamine-regulated transcript peptide (CART) is known to be involved in central effects of ethanol and hence selected for the current study. Twenty-five days old pups of the alcoholic mother showed significant augmentation in CART-immunoreactivity in the cells of Edinger-Westphal (EW) nucleus and lateral hypothalamus. However, a significant decrease in CART-immunoreactivity was seen in nucleus accumbens shell (AcbSh), lateral part of bed nucleus of the stria terminalis (BNSTl), locus coeruleus (LC), hippocampus (CA1, CA2 and CA3), and arcuate nucleus (ARC) of the pups and/or adults offspring. While no change in the CART-immunoreactive fibers of AcbSh and BNSTl, CA2 and CA3 was noticed in the 25 days old pups, the CART-immunoreactive cells in EW and paraventricular nucleus (PVN), and fibers in the central nucleus of amygdala of 85 days old offspring remained unaffected. We suggest that the endogenous CART system in these discrete areas, among other factors, may be a causal to the abnormalities in the next generation of an alcoholic mother.

Keywords: anxiety, depression, CART, ethanol, immunocytochemistry

Procedia PDF Downloads 395

Authors: Sadia Basar, Naila Habib, Awais Adnan

Abstract:

The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

Procedia PDF Downloads 376

Authors: T. Heiman, D. Olenik-Shemesh

Abstract:

Over the past decade, with the rapid growth of electronic communication, the internet and, in particular, social networking has become an inseparable part of people's daily lives. Along with its benefits, a new type of online aggression has emerged, defined as cyber bullying, a form of interpersonal aggressive behavior that takes place through electronic means. Cyber-bullying is characterized by repetitive behavior over time of maladaptive authority and power usage using computers and cell phones via sending insulting messages and hurtful pictures. Preliminary findings suggest that the prevalence of involvement in cyber-bullying among higher education students varies between 10 and 35%. As to date, universities are facing an uphill effort in trying to restrain online misbehavior. As no studies examined the relationships between cyber-bullying involvement with personal aspects, and its impacts on academic achievement and work functioning, this present study examined the nature of cyber-bullying involvement among 1,052 undergraduate students (mean age = 27.25, S.D = 4.81; 66.2% female), coping with, as well as the effects of social support, perceived self-efficacy, well-being, and body-perception, in relation to cyber-victimization. We assume that students in higher education are a vulnerable population and at high risk of being cyber-victims. We hypothesize that social support might serve as a protective factor and will moderate the relationships between the socio-emotional variables and the occurrence of cyber- victimization. The findings of this study will present the relationships between cyber-victimization and the social-emotional aspects, which constitute risk and protective factors. After receiving approval from the Ethics Committee of the University, a Google Drive questionnaire was sent to a random sample of students, studying in the various University study centers. Students' participation was voluntary, and they completed the five questionnaires anonymously: Cyber-bullying, perceived self-efficacy, subjective well-being, social support and body perception. Results revealed that 11.6% of the students reported being cyber-victims during last year. Examining the emotional and behavioral reactions to cyber-victimization revealed that female emotional and behavioral reactions were significantly greater than the male reactions (p < .001). Moreover, females reported on a significant higher social support compared to men; male reported significantly on a lower social capability than female; and men's body perception was significantly more positive than women's scores. No gender differences were observed for subjective well-being scale. Significant positive correlations were found between cyber-victimization and fewer friends, lower grades, and work ineffectiveness (r = 0.37- .40, p < 0 .001). The results of the Hierarchical regression indicated significantly that cyber-victimization can be predicted by lower social support, lower body perception, and gender (female), that explained 5.6% of the variance (R2 = 0.056, F(5,1047) = 12.47, p < 0.001). The findings deepen our understanding of the students' involvement in cyber-bullying, and present the relationships of the social-emotional and academic aspects on cyber-victim students. In view of our findings, higher education policy could help facilitate coping with cyber-bullying incidents, and student support units could develop intervention programs aimed at reducing cyber-bullying and its impacts.

Keywords: academic and personal factors, cyber-victimization, social support, higher education

Procedia PDF Downloads 290