Search results for: hybrid genetic algorithms

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

Abstract:

Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

Procedia PDF Downloads 126

Authors: Saima Suleman, Kalsoom Sughra, Naeem Mahmood Ashraf

Abstract:

Mycobacterium tuberculosis is a communicable chronic illness. This disease is being highly focused by researchers as it is present approximately in one third of world population either in active or latent form. The genetic makeup of a person plays an important part in producing immunity against disease. And one important factor association is single nucleotide polymorphism of relevant gene. In this study, we have studied association between single nucleotide polymorphism of CD-209 gene (encode DC-SIGN receptor) and patients of tuberculosis. Dry lab (in silico) and wet lab (RFLP) analysis have been carried out. GWAS catalogue and GEO database have been searched to find out previous association data. No association study has been found related to CD-209 nsSNPs but role of CD-209 in pulmonary tuberculosis have been addressed in GEO database.Therefore, CD-209 has been selected for this study. Different databases like ENSEMBLE and 1000 Genome Project has been used to retrieve SNP data in form of VCF file which is further submitted to different software to sort SNPs into benign and deleterious. Selected SNPs are further annotated by using 3-D modeling techniques using I-TASSER online software. Furthermore, selected nsSNPs were checked in Gujrat and Faisalabad population through RFLP analysis. In this study population two SNPs are found to be associated with tuberculosis while one nsSNP is not found to be associated with the disease.

Keywords: association, CD209, DC-SIGN, tuberculosis

Procedia PDF Downloads 298

Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

Abstract:

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

Procedia PDF Downloads 239

Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

Abstract:

In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

Procedia PDF Downloads 287

Authors: Daniel Olshansky, Ramiro Rodrıguez Colmeiro

Abstract:

Relay Mining presents a scalable solution employing probabilistic mechanisms and crypto-economic incentives to estimate RPC volume usage, facilitating decentralized multitenant rate limiting. Network traffic from individual applications can be concurrently serviced by multiple RPC service providers, with costs, rewards, and rate limiting governed by a native cryptocurrency on a distributed ledger. Building upon established research in token bucket algorithms and distributed rate-limiting penalty models, our approach harnesses a feedback loop control mechanism to adjust the difficulty of mining relay rewards, dynamically scaling with network usage growth. By leveraging crypto-economic incentives, we reduce coordination overhead costs and introduce a mechanism for providing RPC services that are both geopolitically and geographically distributed.

Keywords: remote procedure call, crypto-economic, commit-reveal, decentralization, scalability, blockchain, rate limiting, token bucket

Procedia PDF Downloads 40

Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

Abstract:

The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

Procedia PDF Downloads 46

Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

Abstract:

Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: classification, data mining, spam filtering, naive bayes, decision tree

Procedia PDF Downloads 397

Authors: Ammar Alali, Mahmoud Abughaban

Abstract:

Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

Procedia PDF Downloads 206

Authors: Asma Hanif, A. I. K. Butt, Shabir Ahmad, Rahim Ud Din, Mustafa Inc

Abstract:

This paper is devoted to a study of the fuzzy fractional mathematical model reviewing the transmission dynamics of the infectious disease Covid-19. The proposed dynamical model consists of susceptible, exposed, symptomatic, asymptomatic, quarantine, hospitalized and recovered compartments. In this study, we deal with the fuzzy fractional model defined in Caputo’s sense. We show the positivity of state variables that all the state variables that represent different compartments of the model are positive. Using Gronwall inequality, we show that the solution of the model is bounded. Using the notion of the next-generation matrix, we find the basic reproduction number of the model. We demonstrate the local and global stability of the equilibrium point by using the concept of Castillo-Chavez and Lyapunov theory with the Lasalle invariant principle, respectively. We present the results that reveal the existence and uniqueness of the solution of the considered model through the fixed point theorem of Schauder and Banach. Using the fuzzy hybrid Laplace method, we acquire the approximate solution of the proposed model. The results are graphically presented via MATLAB-17.

Keywords: Caputo fractional derivative, existence and uniqueness, gronwall inequality, Lyapunov theory

Procedia PDF Downloads 91

Authors: Tuhina-khatun, Mohamed Hanafi Musa, Mohd Rafii Yosup, Wong Mui Yun, Aktar-uz-Zaman, Mahbod Sahebi

Abstract:

Suppression subtractive hybridization (SSH) method is valuable tool for identifying differentially regulated genes in disease specific or tissue specific genes important for cellular growth and differentiation. It is a widely used method for separating DNA molecules that distinguish two closely related DNA samples. SSH is one of the most powerful and popular methods for generating subtracted cDNA or genomic DNA libraries. It is based primarily on a suppression polymerase chain reaction (PCR) technique and combines normalization and subtraction in a solitary procedure. The normalization step equalizes the abundance of DNA fragments within the target population, and the subtraction step excludes sequences that are common to the populations being compared. This dramatically increases the probability of obtaining low-abundance differentially expressed cDNAs or genomic DNA fragments and simplifies analysis of the subtracted library. SSH technique is applicable to many comparative and functional genetic studies for the identification of disease, developmental, tissue specific, or other differentially expressed genes, as well as for the recovery of genomic DNA fragments distinguishing the samples under comparison.

Keywords: suppression subtractive hybridization, differentially expressed genes, disease specific genes, tissue specific genes

Procedia PDF Downloads 417

Authors: Janice Cheong, Tan Seying

Abstract:

The growing years in adolescence are often a tumultuous time for both the individual and family; this is especially so for individuals with Autism Spectrum Disorder (ASD) and Social Communication Disorder (SCD). Tea Club, which is adapted from the PEERS® for Young Adults, seeks to address some of the social challenges faced by Singaporean adolescents with ASD/SCD while navigating social situations. Tea club (hybrid) consists of face-to-face sessions and virtual sessions. These sessions work with both the adolescent and their parents to tackle the individual's difficulties with social skills, empathy, and loneliness. Prior to the group intervention, both participants and their parents scored on the Test of Adolescent Social Skills Knowledge (TASSK) and Autism Spectrum Quotient (AQ), respectively. The session was spread across four months. At the end of the group based intervention, participants’ and parents’ scores were collected again and compared. Inputs on the programme and participant’s confidence in socialization were also gathered from both participants and their parents and looked at thematically. The findings highlight some of the challenges faced by teens with ASD in Singapore and the benefits of the intervention. Parental sentiments are also examined and discussed.

Keywords: adolescence autism, group intervention, social communication disorder, social skills

Procedia PDF Downloads 128

Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

Abstract:

For security purposes, it is important to detect passwords entered by unauthorized users. With traditional alphanumeric passwords, if the content of a password is acquired and correctly entered by an intruder, it is impossible to differentiate the password entered by the intruder from those entered by the authorized user because the password entries contain precisely the same character set. However, no two entries for the gesture-based passwords, even those entered by the person who created the password, will be identical. There are always variations between entries, such as the shape and length of each stroke, the location of each stroke, and the speed of drawing. It is possible that passwords entered by the unauthorized user contain higher levels of variations when compared with those entered by the authorized user (the creator). The difference in the levels of variations may provide cues to detect unauthorized entries. To test this hypothesis, we designed an empirical study, collected and analyzed the data with the help of machine-learning algorithms. The results of the study are significant.

Keywords: authentication, gesture-based passwords, shoulder-surfing attacks, usability

Procedia PDF Downloads 126

Authors: Ghada S. Moussa

Abstract:

Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

Procedia PDF Downloads 339

Authors: Sumaya Ismail, Aijaz Ahmad Rehi

Abstract:

Abstract The location tracking and positioning systems have technologically grown exponentially in recent decade. In particular, Global Position system (GPS) has become a universal norm to be a part of almost every software application directly or indirectly for the location based modules. However major drawback of GPS based system is their inability of working in indoor environments. Researchers are thus focused on the alternative technologies which can be used in indoor environments for a vast range of application domains which require indoor location tracking. One of the most popular technology used for indoor tracking is radio frequency identification (RFID). Due to its numerous advantages, including its cost effectiveness, it is considered as a technology of choice in indoor location tracking systems. To contribute to the emerging trend of the research, this paper proposes an application architecture of passive RFID tag based indoor location tracking system. For the proof of concept, a test bed will be developed to in this study. In addition, various indoor location tracking algorithms will be used to assess their appropriateness in the proposed application architecture.

Keywords: RFID, GPS, indoor location tracking, application architecture, passive RFID tag

Procedia PDF Downloads 101

Authors: Purva Mishra, Rajesh Potlia, Kuljeet Singh Sandhu

Abstract:

The role of glycyl residues in the protein structure has lingered within the research community for the last several decades. Glycyl residue is the only amino acid that is achiral due to the lack of a side chain and can, therefore, exhibit Ramachandran conformations that are disallowed for L-amino acids. The structural and functional significance of glycyl residues with L-disallowed conformation, however, remains obscure. Through statistical analysis of various datasets, we found that the glycyls with L-disallowed conformations are over-represented at disease-associated sites and tend to be evolutionarily conserved. The mutations of L-disallowed glycyls tend to destabilize the native conformation, reduce protein solubility, and promote inter-molecular aggregations. We uncovered a structural motif referred to as “β-crescent” formed around the L-disallowed glycyl, which prevents β-sheet aggregation by disrupting the alternating pattern of β-pleats. The L-disallowed conformation of glycyls also holds predictive power to infer the pathogenic missense variants. Altogether, our observations highlight that the L-disallowed conformation of glycyls is selected to facilitate native folding and prevent inter-molecular aggregations. The findings may also have implications for designing more stable proteins and prioritizing the genetic lesions implicated in diseases.

Keywords: Ramachandran plot, β-sheet, protein stability, protein aggregation

Procedia PDF Downloads 45

Authors: Mahmoud M. Othman, Almoataz Y. Abdelaziz, Yasser G. Hegazy

Abstract:

This paper presents a load control strategy based on modification of the Big Bang Big Crunch optimization method. The proposed strategy aims to determine the optimal load to be controlled and the corresponding time of control in order to minimize the energy purchased from substation. The presented strategy helps the distribution network operator to rely on the renewable energy sources in supplying the system demand. The renewable energy sources used in the presented study are modeled using the diagonal band Copula method and sequential Monte Carlo method in order to accurately consider the multivariate stochastic dependence between wind power, photovoltaic power and the system demand. The proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder. Several case studies are done and the subsequent discussions show the effectiveness of the proposed algorithm.

Keywords: big bang big crunch, distributed generation, load control, optimization, planning

Procedia PDF Downloads 329

Authors: Mohammad Najmuddin Khan, Mohamad Khaleel Albalwi

Abstract:

The prevalence of obesity has risen dramatically in past several decades. Hormonal and genetic factors are rarely the cause of childhood obesity. Because obese adult may suffer life-long physical and emotional consequences, it is imperative to discuss prevention with parents during well-child examinations. Purpose of the study was to compare the serum lipid profile of obese and non-obese males. Twenty two male students were selected from Al-Jouf University. Their age ranged from 19 to 29. They were divided into groups. One group (N=15) having more than 20% fat was considered as obese group, another group (N=7) was considered as non-obese group. Fasting blood samples were analysed for blood cholesterol, triglycerides, low density lipoprotein cholesterol (LDL-C) and high density lipoprotein cholesterol (HDL-C). Independent test was applied to compare mean difference. In obese group, significantly higher cholesterol and triglycerides were observed. On the contrary, obese group had significantly lower HDL-C concentration than the non-obese group. The adult obese has relatively larger changes in serum lipids at any given level of obesity. On the average, higher amount of fat makes it more likely for an individual to be dyslipidemic and to express elements of the metabolic syndrome. Increased triglycerides level in obese impaired lipolysis which reduced the HDL-C concentrations.

Keywords: obesity, serum lipid profile, Al-Jouf, HDL, LDL

Procedia PDF Downloads 266

Authors: Mohammad Ahangari

Abstract:

Schizophrenia is a psychiatric disorder with symptoms that include cognitive deficits and nicotine has been suggested to have an effect on cognition. In recent years, the advents of Genome-Wide Association Studies(GWAS) has evolved our understanding about the genetic causes of complex disorders such as schizophrenia and studying the role of genome-wide significant genes could potentially lead to the development of new therapeutic agents for treatment of cognitive deficits in schizophrenia. The current study identified six Single Nucleotide Polymorphisms (SNP) from schizophrenia and smoking GWAS that are located on or in close proximity to the nicotinic receptor gene cluster (CHRN) and studied their association with cognition in an Irish sample of 1297 cases and controls using linear regression analysis. Further on, the interaction between CHRN gene cluster and Dopamine receptor D2 gene (DRD2) during working memory was investigated. The effect of these polymorphisms on nicotinic and dopaminergic neurotransmission, which is disrupted in schizophrenia, have been characterized in terms of their effects on memory, attention, social cognition and IQ as measured by a neuropsychological test battery and significant effects in two polymorphisms were found across global IQ domain of the test battery.

Keywords: cognition, dopamine, GWAS, nicotine, schizophrenia, SNPs

Procedia PDF Downloads 321

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 125

Authors: Ronak Khosravi, Mahmood Abbasi Layegh, Siamak Haghipour, Avin Esmaili

Abstract:

In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function.

Keywords: coefficient features, relevance vector machines, spectral features, support vector machines, temporal features

Procedia PDF Downloads 302

Authors: M. Ben Jrad, A. Belhaj Mohamed, S. Riahi, I. Bouazizi, M. Saidi, M. Soussi

Abstract:

The generative potential, depositional environment, thermal maturity and oil seeps of the organic-rich Bou Dabbous Formation (Ypresian) from the thrust belt northwestern Tunisia, were determined by Rock Eval and molecular analyses. The paleo-tectonic units in the area show some similarities with equivalent facies in Mediterranean Sea and Sicilian. The Bou Dabbous Formation displays variable source rock characteristics through the various units Tellian and Numidian nappes Units. Organic matter contents and petroleum potentials are fair to high (reaching 1.95% and 6 kg of HC/t of rock respectively) marine type II kerogen. An increasing SE-NW maturity gradient is well documented in the study area. The Bou Dabbous organic-rich facies are marginally mature stage in the Tellian Unit (Kasseb domain), whilst they are mature-late mature stage within Nefza-Ain Allega tectonic windows. A long and north of Cap Serrat-Ghardimaou Master Fault these facies are overmature. Oil/Oil and Oil/source rock correlation, based on biomarker and carbon isotopic composition, shows a positive genetic correlation between the oil seeps and Bou Dabbous source rock.

Keywords: biomarkers, Bou Dabbous Formation, Northern Tunisia, source rock

Procedia PDF Downloads 471

Authors: Bashir Abdu Muzakkari, Mohamad Afendee Mohamad, Mohd Fadzil Abdul Kadir

Abstract:

Wireless Sensor Networks (WSNs) is an aggregation of several tiny, low-cost sensor nodes, spatially distributed to monitor physical or environmental status. WSN is constantly changing because of the rapid technological advancements in sensor elements such as radio, battery and operating systems. The Medium Access Control (MAC) protocols remain very vital in the WSN because of its role in coordinating communication amongst the sensors. Other than battery consumption, packet collision, network lifetime and latency are factors that largely depend on WSN MAC protocol and these factors have been widely treated in recent days. In this paper, we survey some latest proposed WSN Contention-based, Scheduling-based and Hybrid MAC protocols while presenting an examination, correlation of advantages and limitations of each protocol. Concentration is directed towards investigating the treatment of Quality of Service (QoS) performance metrics within these particular protocols. The result shows that majority of the protocols leaned towards energy conservation. We, therefore, believe that other performance metrics of guaranteed QoS such as latency, throughput, packet loss, network and bandwidth availability may play a critical role in the design of future MAC protocols for WSNs.

Keywords: WSN, QoS, energy consumption, MAC protocol

Procedia PDF Downloads 379

Authors: Mhaned Oubounyt, Jan Baumbach

Abstract:

Differences in co-expression networks between two or multiple cells (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN (Gene Correlation Network) and GRN (Gene Regulatory Networks) pipeline, including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, co-regulatory networks, and drug-gene interactions. In an example case, we illustrate how SCANet can be applied to identify regulatory drivers behind a cytokine storm associated with mortality in patients with acute respiratory illness. SCANet is available as a free, open-source, and user-friendly Python package that can be easily integrated into systems biology pipelines.

Keywords: single-cell, co-expression networks, drug-gene interactions, co-regulatory networks

Procedia PDF Downloads 123

Authors: M. Suresh Kumar, K. Ramani

Abstract:

This paper proposed the harmonic elimination of Cascaded H-Bridge Multi-Level Inverter by using Selective Harmonic Elimination-Pulse Width Modulation method programmed with Particle Swarm Optimization algorithm. PSO method determine proficiently the required switching angles to eliminate low order harmonics up to the 11th order from the inverter output voltage waveform while keeping the magnitude of the fundamental harmonics at the desired value. Results demonstrate that the proposed method does efficiently eliminate a great number of specific harmonics and the output voltage is resulted in minimum Total Harmonic Distortion. The results shown that the PSO algorithm attain successfully to the global solution faster than other algorithms.

Keywords: multi-level inverter, Selective Harmonic Elimination Pulse Width Modulation (SHEPWM), Particle Swarm Optimization (PSO), Total Harmonic Distortion (THD)

Procedia PDF Downloads 587

Authors: Sagir M. Yusuf, Chris Baber

Abstract:

We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

Procedia PDF Downloads 128

Authors: Baoshan Wei, Shuai Han, Xing Zhang

Abstract:

Local obstacle avoidance is critical for mobile robot navigation. It is a challenging task to ensure path optimality and safety in cluttered environments. We proposed an Environment Aware Dynamic Window Approach in this paper to cope with the issue. The method integrates environment characterization into Dynamic Window Approach (DWA). Two strategies are proposed in order to achieve the integration. The local goal strategy guides the robot to move through openings before approaching the final goal, which solves the local minima problem in DWA. The adaptive control strategy endows the robot to adjust its state according to the environment, which addresses path safety compared with DWA. Besides, the evaluation shows that the path generated from the proposed algorithm is safer and smoother compared with state-of-the-art algorithms.

Keywords: adaptive control, dynamic window approach, environment aware, local obstacle avoidance, mobile robots

Procedia PDF Downloads 137

Authors: A. Biran, P. Sobhe Bidari, K. Raahemifar

Abstract:

Diabetic Retinopathy (DR) is an eye disease that leads to blindness. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness; hence, many automated algorithms have been proposed to extract hemorrhages and exudates. In this paper, an automated algorithm is presented to extract hemorrhages and exudates separately from retinal fundus images using different image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Since Optic Disc is the same color as the exudates, it is first localized and detected. The presented method has been tested on fundus images from Structured Analysis of the Retina (STARE) and Digital Retinal Images for Vessel Extraction (DRIVE) databases by using MATLAB codes. The results show that this method is perfectly capable of detecting hard exudates and the highly probable soft exudates. It is also capable of detecting the hemorrhages and distinguishing them from blood vessels.

Keywords: diabetic retinopathy, fundus, CHT, exudates, hemorrhages

Procedia PDF Downloads 259

Authors: Andreas Hammer

Abstract:

Six peculiar architecture designs from the Frankfurt University will be discussed within this paper and their future potential of the adaptable and solar thin-film sheets implemented facades will be shown acting and reacting on climate/solar changes of their specific sites. The different aspects, as well as limitations with regard to technical and functional restrictions, will be named. The design process for a “multi-purpose building”, a “high-rise building refurbishment” and a “biker’s lodge” on the river Rheine valley, has been critically outlined and developed step by step from an international studentship towards an overall energy strategy, that firstly had to push the design to a plus-energy building and secondly had to incorporate bionic aspects into the building skins design. Both main parameters needed to be reviewed and refined during the whole design process. Various basic bionic approaches have been given [e.g. solar ivyᵀᴹ, flectofinᵀᴹ or hygroskinᵀᴹ, which were to experiment with, regarding the use of bendable photovoltaic thin film elements being parts of a hybrid, kinetic façade system.

Keywords: bionic and bioclimatic design, climate adaptive building shells [CABS], energy-strategy, harvesting façade, high-efficiency building skin, photovoltaic in building skins, plus-energy-buildings, solar gain, sustainable building concept

Procedia PDF Downloads 414

Authors: Sujay Kotwale, Ramasubba Reddy M.

Abstract:

Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

Procedia PDF Downloads 97

Authors: Ghazanfar Ali, Fahad N Almajhdi

Abstract:

This study provides data on the viral diagnosis and molecular epidemiology of influenza A(H3N2) virus isolated in Riyadh, Saudi Arabia. Nasopharyngeal aspirates from 80 clinically infected patients in the peak of the 2010-2011 winter seasons were processed for viral diagnosis by RT-PCR. Sequencing of entire HA and NA genes of representative isolates and molecular epidemiological analysis were performed. A total of 06 patients were positive for influenza A, B and respiratory syncytial viruses by RT-PCR assays; out of these only one sample was positive for influenza A(H3N2) by RT-PCR. Phylogenetic analysis of the HA and NA gene sequences showed identities higher than 99-98.8 % in both genes. They were also similar to reference isolates in HA sequences (99 % identity) and in NA sequences (99 % identity). Amino acid sequences predicted for the HA gene were highly identical to reference strains. The NA amino acid substitutions identified did not include the oseltamivir-resistant H275Y substitution. Conclusion: Viral isolation and RT-PCR together were useful for diagnosis of the influenza A (H3N2) virus. Variations in HA and NA sequences are similar to those identified in worldwide reference isolates and no drug resistance was found.

Keywords: influenza A (H3N2), genetic characterization, viral isolation, RT-PCR, Saudi Arabia

Procedia PDF Downloads 248