Search results for: mathematical algorithms of targeting and persecution

Authors: Wanqing You, Kai Qian, Ying Qian

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The concepts of Cloud Computing provide users with infrastructure, platform and software as service, which make those services more accessible for people via Internet. To better analysis the performance of Cloud Computing provisioning policies as well as resources allocation strategies, a toolkit named CloudSim proposed. With CloudSim, the Cloud Computing environment can be easily constructed by modelling and simulating cloud computing components, such as datacenter, host, and virtual machine. A good scheduling strategy is the key to achieve the load balancing among different machines as well as to improve the utilization of basic resources. Recently, the existing scheduling algorithms may work well in some presumptive cases in a single machine; however they are unable to make the best decision for the unforeseen future. In real world scenario, there would be numbers of tasks as well as several virtual machines working in parallel. Based on the concepts of multi-queue, this paper presents a new scheduling algorithm to schedule tasks with CloudSim by taking into account several parameters, the machines’ capacity, the priority of tasks and the history log.

Keywords: hierarchical queue, load balancing, CloudSim, information technology

Procedia PDF Downloads 405

Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar

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In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

Procedia PDF Downloads 610

Authors: Zhen Peng, Baifeng Wu

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

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

Procedia PDF Downloads 272

Authors: C. S. Bianca Sabrina, N. Egidio Raimundo, L. Alexandre Baratella, C. H. João Paulo

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This paper aims to present a study, with the theoretical concepts and applications of the Quadcopter, using the MATLAB simulator. In order to use this tool, the study of the stability of the drone through a Proportional - Integral - Derivative (PID) controller will be presented. After the stability study, some tests are done on the simulator and its results will be presented. From the mathematical model, it is possible to find the Newton-Euler angles, so that it is possible to stabilize the quadcopter in a certain position in the air, starting from the ground. In order to understand the impact of the controllers gain values on the stabilization of the Euler-Newton angles, three conditions will be tested with different controller gain values.

Keywords: controllers, drones, quadcopter, stability

Procedia PDF Downloads 182

Authors: Mohammad Arif Khan

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This paper analyzes and presents the development of Artificial Neural Network based controller of space vector modulation (ANN-SVPWM) for a seven-phase voltage source inverter. At first, the conventional method of producing sinusoidal output voltage by utilizing six active and one zero space vectors are used to synthesize the input reference, is elaborated and then new PWM scheme called Artificial Neural Network Based PWM is presented. The ANN based controller has the advantage of the very fast implementation and analyzing the algorithms and avoids the direct computation of trigonometric and non-linear functions. The ANN controller uses the individual training strategy with the fixed weight and supervised models. A computer simulation program has been developed using Matlab/Simulink together with the neural network toolbox for training the ANN-controller. A comparison of the proposed scheme with the conventional scheme is presented based on various performance indices. Extensive Simulation results are provided to validate the findings.

Keywords: space vector PWM, total harmonic distortion, seven-phase, voltage source inverter, multi-phase, artificial neural network

Procedia PDF Downloads 438

Authors: Fouzi Aboura

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The fractional order proportional-integral-derivative (FOPID) controller or fractional order (PIλDµ) is a proportional-integral-derivative (PID) controller where integral order (λ) and derivative order (µ) are fractional, one of the important application of classical PID is the Automatic Voltage Regulator (AVR).The FOPID controller needs five parameters optimization while the design of conventional PID controller needs only three parameters to be optimized. In our paper we have proposed a comparison between algorithms Differential Evolution (DE) and Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) ,we have studied theirs characteristics and performance analysis to find an optimum parameters of the FOPID controller, a new objective function is also proposed to take into account the relation between the performance criteria’s.

Keywords: FOPID controller, fractional order, AVR system, objective function, optimization, GA, PSO, HGAPSO

Procedia PDF Downloads 80

Authors: Wang Zhulin, Min Fuhong, Peng Guangya, Wang Yaoda, Cao Yi

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In this paper, a magnetron memristor model based on hyperbolic sine function is presented and the correctness proved by studying the trajectory of its voltage and current phase, and then a memristor chaotic system with the memristor model is presented. The phase trajectories and the bifurcation diagrams and Lyapunov exponent spectrum of the magnetron memristor system are plotted by numerical simulation, and the chaotic evolution with changing the parameters of the system is also given. The paper includes numerical simulations and mathematical model, which confirming that the system, has a wealth of dynamic behavior.

Keywords: memristor, chaotic circuit, dynamical behavior, chaotic system

Procedia PDF Downloads 483

Authors: Sanjay Singh, Sushil Kumar, Rashmi Jain

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Quantum registering is another pattern in computational hypothesis and a quantum mechanical framework has a few helpful properties like Entanglement. We plan to store data concerning the structure and substance of a basic picture in a quantum framework. Consider a variety of n qubits which we propose to use as our memory stockpiling. In recent years classical processing is switched to quantum image processing. Quantum image processing is an elegant approach to overcome the problems of its classical counter parts. Image storage, retrieval and its processing on quantum machines is an emerging area. Although quantum machines do not exist in physical reality but theoretical algorithms developed based on quantum entangled states gives new insights to process the classical images in quantum domain. Here in the present work, we give the brief overview, such that how entangled states can be useful for quantum image storage and retrieval. We discuss the properties of tripartite Greenberger-Horne-Zeilinger and W states and their usefulness to store the shapes which may consist three vertices. We also propose the techniques to store shapes having more than three vertices.

Keywords: Greenberger-Horne-Zeilinger, image storage and retrieval, quantum entanglement, W states

Procedia PDF Downloads 290

Authors: Agboka Komi Mensah, John Odindi, Elfatih M. Abdel-Rahman, Onisimo Mutanga, Henri Ez Tonnang

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Ecosystems are networks of organisms and populations that form a community of various species interacting within their habitats. Such habitats are defined by abiotic and biotic conditions that establish the initial limits to a population's growth, development, and reproduction. The habitat’s conditions explain the context in which species interact to access resources such as food, water, space, shelter, and mates, allowing for feeding, dispersal, and reproduction. Dispersal is an essential life-history strategy that affects gene flow, resource competition, population dynamics, and species distributions. Despite the importance of dispersal in population dynamics and survival, understanding the mechanism underpinning the dispersal of organisms remains challenging. For instance, when an organism moves into an ecosystem for survival and resource competition, its progression is highly influenced by extrinsic factors such as its physiological state, climatic variables and ability to evade predation. Therefore, greater spatial detail is necessary to understand organism dispersal dynamics. Understanding organisms dispersal can be addressed using empirical and mechanistic modelling approaches, with the adopted approach depending on the study's purpose Cellular automata (CA) is an example of these approaches that have been successfully used in biological studies to analyze the dispersal of living organisms. Cellular automata can be briefly described as occupied cells by an individual that evolves based on proper decisions based on a set of neighbours' rules. However, in the ambit of modelling individual organisms dispersal at the landscape scale, we lack user friendly tools that do not require expertise in mathematical models and computing ability; such as a visual analytics framework for tracking and forecasting the dispersal behaviour of organisms. The term "visual analytics" (VA) describes a semiautomated approach to electronic data processing that is guided by users who can interact with data via an interface. Essentially, VA converts large amounts of quantitative or qualitative data into graphical formats that can be customized based on the operator's needs. Additionally, this approach can be used to enhance the ability of users from various backgrounds to understand data, communicate results, and disseminate information across a wide range of disciplines. To support effective analysis of the dispersal of organisms at the landscape scale, we therefore designed Pydisp which is a free visual data analytics tool for spatiotemporal dispersal modeling built in Python. Its user interface allows users to perform a quick and interactive spatiotemporal analysis of species dispersal using bioecological and climatic data. Pydisp enables reuse and upgrade through the use of simple principles such as Fuzzy cellular automata algorithms. The potential of dispersal modeling is demonstrated in a case study by predicting the dispersal of Fopius arisanus (Sonan), endoparasitoids to control Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) in Kenya. The results obtained from our example clearly illustrate the parasitoid's dispersal process at the landscape level and confirm that dynamic processes in an agroecosystem are better understood when designed using mechanistic modelling approaches. Furthermore, as demonstrated in the example, the built software is highly effective in portraying the dispersal of organisms despite the unavailability of detailed data on the species dispersal mechanisms.

Keywords: cellular automata, fuzzy logic, landscape, spatiotemporal

Procedia PDF Downloads 64

Authors: Krystian Chachula, Robert Nowak

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Manufacturing home-made explosives and synthetic drugs is an increasing problem in Europe. To combat that, a data fusion system is proposed for the detection and localization of production sites in urban environments. The data consists of measurements of properties of wastewater performed by various sensors installed in a sewage network. A four-stage fusion strategy allows detecting sources of waste products from known chemical reactions. First, suspicious measurements are used to compute the amount and position of discharged compounds. Then, this information is propagated through the sewage network to account for missing sensors. The next step is clustering and the formation of tracks. Eventually, tracks are used to reconstruct discharge events. Sensor measurements are simulated by a subsystem based on real-world data. In this paper, different discharge scenarios are considered to show how the parameters of used algorithms affect the effectiveness of the proposed system. This research is a part of the SYSTEM project (SYnergy of integrated Sensors and Technologies for urban sEcured environMent).

Keywords: continuous monitoring, information fusion and sensors, internet of things, multisensor fusion

Procedia PDF Downloads 100

Authors: Aishik Deb, Rituparna Sinha

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We have developed an algorithm to detect the abnormal segment/"structural variation in the genome across a number of samples. We have worked on simulated as well as real data from the BAM Files and have designed a segmentation algorithm where abnormal segments are detected. This algorithm aims to improve the accuracy and performance of the existing CNV-CH algorithm. The next-generation sequencing (NGS) approach is very fast and can generate large sequences in a reasonable time. So the huge volume of sequence information gives rise to the need for Big Data and parallel approaches of segmentation. Therefore, we have designed a map-reduce approach for the existing CNV-CH algorithm where a large amount of sequence data can be segmented and structural variations in the human genome can be detected. We have compared the efficiency of the traditional and map-reduce algorithms with respect to precision, sensitivity, and F-Score. The advantages of using our algorithm are that it is fast and has better accuracy. This algorithm can be applied to detect structural variations within a genome, which in turn can be used to detect various genetic disorders such as cancer, etc. The defects may be caused by new mutations or changes to the DNA and generally result in abnormally high or low base coverage and quantification values.

Keywords: cancer detection, convex hull segmentation, map reduce, next generation sequencing

Procedia PDF Downloads 117

Authors: Hans Marquez Ramos, Cesar Hernandez, Ingrid Páez

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This paper develops a multiple channel assignment model, which allows to take advantage in most efficient way, spectrum opportunities in cognitive radio networks. Developed scheme allows make several available and frequency adjacent channel assignments, which require a bigger wide band, under an equality environment. The hybrid assignment model it is made by to algorithms, one who makes the ranking and select available frequency channels and the other one in charge of establishing an equality criteria, in order to not restrict spectrum opportunities for all other secondary users who wish to make transmissions. Measurements made were done for average bandwidth, average delay, as well fairness computation for several channel assignment. Reached results were evaluated with experimental spectrum occupational data from GSM frequency band captured. Developed model, shows evidence of improvement in spectrum opportunity use and a wider average transmit bandwidth for each secondary user, maintaining equality criteria in channel assignment.

Keywords: bandwidth, fairness, multichannel, secondary users

Procedia PDF Downloads 481

Authors: Lele Zhang, Hui Leng Choo, Alexander Konyukhov, Shuguang Li

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Finite Element (FE) model of simplified e-bike structure was generated by main frame with two tiers, which consisted of pipe, mass, beam, and shell elements (pipe 289, beam188, shell 181, shell 281, combin14, link11, mass21). These elements would be introduced and demonstrated using mathematical formulas. Based on coupling theory, constrain equations was proposed. Exporting all the parameters obtained from theory part, the connection stiffness matrix of the whole e-bike structure between each of these elements was detected.

Keywords: coupling theory, stiffness matrix, e-bike, finite element model

Procedia PDF Downloads 363

Authors: Nilki Upathissa, Torin Wirasinghe

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This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users.

Keywords: recommendation systems, hybrid recommendation systems, machine learning, data science, long short-term memory, recurrent neural network

Procedia PDF Downloads 71

Authors: Shreya Sara Ittycheria, K. C. Sivakumar, Shijulal Nelson Sathi, Priya Srinivas

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hCG, a heterodimer composed of α and β subunits, is a peptide hormone having numerous biological functions. Although hCG is expressed by placenta during pregnancy, ectopic β-hCG secretion is observed in many non-trophoblastic tumors including that of breast. In-vitro and in-vivo studies done in the lab, have proved that BRCA1 defective cancers express β-hCG and when β-hCG is expressed or supplemented, it promotes tumor progression and exhibits resistance to carboplatin and ABT888, in such cancers but not in BRCA1 wild type cancers. In cancer cells, instead of binding to its regular receptor, LH-CGR, β-hCG binds with Transforming Growth Factor Receptor 2 (TGFβRII) and phosphorylates it resulting in faster tumor progression through the Smad signaling pathway. Targeting β-hCG could be a potential therapeutic strategy for managing BRCA1 defective cancers. Here, molecular docking and dynamic simulation studies were done to identify potential small molecule inhibitors against β-hCG as there are currently no such inhibitors reported. The binding sites of TGFβRII on β-hCG were identified from the top 10 predicted complexes from Z Dock. Virtual screening of selected commercially available small molecules from various libraries such as ZINC, NCI and Life Chemicals amounting to a total of 50,025 molecules were done. Four potential small molecule inhibitors were identified, RgcbPs-1, RgcbPs-2, RgcbPs-3 and RgcbPs-4 with binding affinities -60.778 kcal/mol, -45.447 kcal/mol, -65.2268 kcal/mol and -82.040 kcal/mol respectively. Further, 100ns Molecular Dynamics (MD) simulation showed that these molecules form stable complexes with β-hCG. RgcbPs-1 maintains hydrogen bonds with Q54, L52, Q46, C100, G36, C57, C38 residues, RgcbPs-2 maintains hydrogen bonds with A83 residue, RgcbPs-3 maintains hydrogen bonds with C57, Y58, R94, G101 residues and RgcbPs-4 maintains hydrogen bonds with G36, C38, T40, C57, D99, C100, G101 and L104 residues of β-hCG all of which coincide with the TGFβRII binding site on β-hCG. These results show that these two inhibitors could be used either singly or in combination for inhibiting β-hCG from binding to TGFβRII and thereby directly inhibiting the tumorigenesis pathway.

Keywords: β-hCG, breast cancer, dynamic simulations, molecular docking, small molecule inhibitors, virtual screening.

Procedia PDF Downloads 87

Authors: Qiang Zhang, Xiaojian Hu1

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Intersection is the place where vehicles and pedestrians must pass through, turn and evacuate. Obtaining the motion data of vehicles near the intersection is of great significance for transportation research. Since there are usually many targets and there are more conflicts between targets, this makes it difficult to obtain vehicle motion parameters in traffic videos of intersections. According to the characteristics of traffic videos, this paper applies video technology to realize the automated track, count and trajectory extraction of vehicles to collect traffic data by roadside surveillance cameras installed near the intersections. Based on the video recognition method, the vehicles in each lane near the intersection are tracked with extracting trajectory and counted respectively in various degrees of occlusion and visibility. The performances are compared with current recognized CPU-based algorithms of real-time tracking-by-detection. The speed of the presented system is higher than the others and the system has a better real-time performance. The accuracy of direction has reached about 94.99% on average, and the accuracy of classification and statistics has reached about 75.12% on average.

Keywords: tracking and statistics, vehicle, signalized intersection, motion parameter, trajectory

Procedia PDF Downloads 203

Authors: Hacene Benkhoula, Mohamed Badreddine Benabdella, Hamid Bouzeboudja, Abderrahmane Asraoui

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The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given.

Keywords: wind power, Gaussien process, modelling, forecasting

Procedia PDF Downloads 387

Authors: Qazi Salman Khalid, Muhammad Khalid, Shahid Maqsood

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This paper presents a solution for optimizing the cycle time in an assembly line with human-robot collaboration and diverse operators. A genetic algorithm with tailored parameters is used to address the assembly line balancing problem in the automobile sector. A mathematical model is developed, depicting the problem. Currently, the firm runs on the largest candidate rule; however, it causes a lag in orders, which ultimately gets penalized. The results of the study show that the proposed GA is effective in providing efficient solutions and that the cycle time has significantly impacted productivity.

Keywords: line balancing, cycle time, genetic algorithm, productivity

Procedia PDF Downloads 122

Authors: Amulya P. Rao, Prathima S., Sreedevi N.

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Speech sound disorders (SSD) encompass the major concern in younger population of India with highest prevalence rate among the speech disorders. Children with SSD if not identified and rehabilitated at the earliest, are at risk for academic difficulties. This necessitates early identification using screening tools assessing the frequently misarticulated speech sounds. The literature on frequently misarticulated speech sounds is ample in English and other western languages targeting individuals with various communication disorders. Articulation is language specific, and there are limited studies reporting the same in Kannada, a Dravidian Language. Hence, the present study aimed to identify the frequently misarticulated consonants in Kannada and also to examine the error type. A retrospective, descriptive study was carried out using secondary data analysis of 41 participants (34-phonetic type and 7-phonemic type) with SSD in the age range 3-to 12-years. All the consonants of Kannada were analyzed by considering three words for each speech sound from the Kannada Diagnostic Photo Articulation test (KDPAT). Picture naming task was carried out, and responses were audio recorded. The recorded data were transcribed using IPA 2018 broad transcription. A criterion of 2/3 or 3/3 error productions was set to consider the speech sound to be an error. Number of error productions was calculated for each consonant in each participant. Then, the percentage of participants meeting the criteria were documented for each consonant to identify the frequently misarticulated speech sound. Overall results indicated that velar /k/ (48.78%) and /g/ (43.90%) were frequently misarticulated followed by voiced retroflex /ɖ/ (36.58%) and trill /r/ (36.58%). The lateral retroflex /ɭ/ was misarticulated by 31.70% of the children with SSD. Dentals (/t/, /n/), bilabials (/p/, /b/, /m/) and labiodental /v/ were produced correctly by all the participants. The highly misarticulated velars /k/ and /g/ were frequently substituted by dentals /t/ and /d/ respectively or omitted. Participants with SSD-phonemic type had multiple substitutions for one speech sound whereas, SSD-phonetic type had consistent single sound substitutions. Intra- and inter-judge reliability for 10% of the data using Cronbach’s Alpha revealed good reliability (0.8 ≤ α < 0.9). Analyzing a larger sample by replicating such studies will validate the present study results.

Keywords: consonant, frequently misarticulated, Kannada, SSD

Procedia PDF Downloads 111

Authors: Po-Jen Chen, Jian-Jiun Ding, Hung-Wei Hsu, Chien-Yao Wang, Jia-Ching Wang

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A more accurate scene classification algorithm using label relations and the hierarchy neural network was developed in this work. In many classification algorithms, it is assumed that the labels are mutually exclusive. This assumption is true in some specific problems, however, for scene classification, the assumption is not reasonable. Because there are a variety of objects with a photo image, it is more practical to assign multiple labels for an image. In this paper, two label relations, which are exclusive relation and hierarchical relation, were adopted in the classification process to achieve more accurate multiple label classification results. Moreover, the hierarchy neural network (hierarchy NN) is applied to classify the image and the directed acyclic graph structure is used for predicting a more reasonable result which obey exclusive and hierarchical relations. Simulations show that, with these techniques, a much more accurate scene classification result can be achieved.

Keywords: convolutional neural network, label relation, hierarchy neural network, scene classification

Procedia PDF Downloads 440

Authors: Pogula Rakesh, T. Kishore Kumar

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Speech enhancement is a long standing problem with numerous applications like teleconferencing, VoIP, hearing aids, and speech recognition. The motivation behind this research work is to obtain a clean speech signal of higher quality by applying the optimal noise cancellation technique. Real-time adaptive filtering algorithms seem to be the best candidate among all categories of the speech enhancement methods. In this paper, we propose a speech enhancement method based on Recursive Least Squares (RLS) adaptive filter of speech signals. Experiments were performed on noisy data which was prepared by adding AWGN, Babble and Pink noise to clean speech samples at -5dB, 0dB, 5dB, and 10dB SNR levels. We then compare the noise cancellation performance of proposed RLS algorithm with existing NLMS algorithm in terms of Mean Squared Error (MSE), Signal to Noise ratio (SNR), and SNR loss. Based on the performance evaluation, the proposed RLS algorithm was found to be a better optimal noise cancellation technique for speech signals.

Keywords: adaptive filter, adaptive noise canceller, mean squared error, noise reduction, NLMS, RLS, SNR, SNR loss

Procedia PDF Downloads 464

Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

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Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: fake news detection, natural language processing, machine learning, classification techniques.

Procedia PDF Downloads 146

Authors: Sanja Peter

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Transformation of urban environments can be compared to evolutionary processes. Systematic digital mapping of historical data can enable capturing some of these processes and their outcomes. For example, it may help reveal the structure of a city’s historical DNA. Gathering historical data for automatic processing may be giving a basis for cultural algorithms. Gothenburg City museum is trying to make city’s heritage information accessible through GIS-platforms and is now partnering with academic institutions to find appropriate methods to make accessible the knowledge on the city’s historical fabric. Hopefully, this will be carried out through a project called Digital Twin Cities. One part of this large project, concerning matters of Cultural Heritage, will be in collaboration with Chalmers University of Technology. The aim is to create a layered map showing historical developments of the city and extracting quantitative data about its built heritage, above and below the earth. It will allow interpreting the information from historic maps through, for example, names of the streets/places, geography, structural changes in urban fabric and information gathered by archaeologists’ excavations. Through the study of these geographical, historical and local metamorphoses, urban environment will reveal its metaphorical DNA or its MEM (Dawkins).

Keywords: Gothenburg, mapping, cultural heritage, city history

Procedia PDF Downloads 125

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

Procedia PDF Downloads 28

Authors: V. Sathya Narayanan, P. Sidharth, V. R. Sanal Kumar

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The success of any retail business is predisposed by its swift response and its knack in understanding the constraints and the requirements of customers. In this paper a conceptual design model of an automated customer-friendly supermarket has been proposed. In this model a 10-sided, space benefited, regular polygon shaped gravity shelves have been designed for goods storage and effective customer-specific algorithms have been built-in for quick automatic delivery of the randomly listed goods. The algorithm is developed with two main objectives, viz., delivery time and priority. For meeting these objectives the randomly listed items are reorganized according to the critical-path of the robotic arm specific to the identified shop and its layout and the items are categorized according to the demand, shape, size, similarity and nature of the product for an efficient pick-up, packing and delivery process. We conjectured that the proposed automated supermarket model reduces business operating costs with much customer satisfaction warranting a win-win situation.

Keywords: automated supermarket, electronic shopping, polygon-shaped rack, shortest path algorithm for shopping

Procedia PDF Downloads 387

Authors: H. Minor-Pérez, A. M. Mota-Silva, S. Ortiz-Barrios

Abstract:

Substances of vegetable origin with antioxidant capacity have a high potential for application on the conservation of some foods, can prevent or reduce for example oxidation of lipids. However a food is a complex system whose wide variety of components wich can reduce or eliminate this antioxidant capacity. The antioxidant activity can be determined with the ABTS technique. The radical ABTS+ is generated from the acid 2, 2´ - Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS). This radical is a composite color bluish-green, stable and with a spectrum of absorption into the UV-visible. The addition of antioxidants causes discoloration, value that can be reported as a percentage of inhibition of the cation radical ABTS+. The objective of this study was evaluated the effect of the combination of the pH and the essential oil of cinnamon (EOC) on inhibition of the radical ABTS+, using statistical design of central point (Design Expert) to obtain mathematical models that describe this phenomenon. Were evaluated 17 treatments with combinations of pH 5, 6 and 7 (citrate-phosphate buffer) and the concentration of essential oil of cinnamon (C): 0 µg/mL, 100 µg/mL and 200 µg/mL. The samples were analyzed using the ABTS technique. The reagent was dissolved in methanol 80% to standardized the absorbance to 0.7 +/- 0.1 at 754 nm. Then samples were mixed with reagent standardized ABTS and after 1 min and 7 min absorbance was read for each treatment at 754 nm. Was used a curve pattern with vitamin C and reported the values as inhibition (%) of radical ABTS+. The statistical analysis shows the experimental results were adjusted to a quadratic model, to the times of 1 min and 7 min. This model describes the influence of the factors investigated independently: pH and cinnamon essential oil (µg/mL) and the effect of the interaction between pH*C, as well as the square of the pH2 and C2. The model obtained was Y = 10.33684 - 3.98118*pH + 1.17031*C + 0.62745*pH2 - 3.26675*10-3*C2 - 0.013112*pH*C, where Y is the response variable. The coefficient of determination was 0.9949 for 1 min. The equation was obtained at 7 min and = - 10.89710 + 1.52341*pH + 1.32892*C + 0.47953*pH2 - 3.56605*10- *C2 - 0.034687*pH*C. The coefficient of determination was 0.9970. This means that only 1% of the total variation is not explained by the developed models. At 100 µg/mL of EOC was obtained an inhibition percentage of 80%, 84% and 97% for the pH values of 5,6 and 7 respectively, while a value of 200 µg/mL the inhibition (%) was very similar for the treatments. In these values of pH was obtained an inhibition close 97%. In conclusion the pH does not have a significant effect on the antioxidant capacity, while the concentration of EOC was decisive for the antioxidant capacity. The authors acknowledge the funding provided by the CONACYT for the project 131998.

Keywords: antioxidant activity, ABTS technique, essential oil of cinnamon, mathematical models

Procedia PDF Downloads 389

Authors: Vahid Bairami Rad

Abstract:

Wireless communications offer organizations and users many benefits such as portability and flexibility, increased productivity, and lower installation costs. Wireless networks serve as the transport mechanism between devices and among devices and the traditional wired networks (enterprise networks and the internet). Wireless networks are many and diverse but are frequently categorized into three groups based on their coverage range: WWAN, WLAN, and WPAN. WWAN, representing wireless wide area networks, includes wide coverage area technologies such as 2G cellular, Cellular Digital Packet Data (CDPD), Global System for Mobile Communications (GSM), and Mobitex. WLAN, representing wireless local area networks, includes 802.11, Hyper lan, and several others. WPAN, represents wireless personal area network technologies such as Bluetooth and Infrared. The security services are provided largely by the WEP (Wired Equivalent Privacy) protocol to protect link-level data during wireless transmission between clients and access points. That is, WEP does not provide end-to-end security but only for the wireless portion of the connection.

Keywords: wireless lan, wired equivalent privacy, wireless network security, wlan security

Procedia PDF Downloads 552

Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

Abstract:

In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

Procedia PDF Downloads 454

Authors: Jerzy Slowik, Elzbieta Grochowska-Niedworok

Abstract:

The analysis of the market needs and trends in both treatment and prophylaxis shows the growing need to implement comprehensive solutions that would enable safe contact of the beneficiaries with the therapeutic and diagnostic support group. Based on the evaluation of the medical and sports industry services market, projects co-financed by the EFRR in the form of comprehensive care systems using IT tools for patients under treatment in the field of obesity and metabolism using the system were implemented under the Regional Operational Program of the Silesian Voivodeship for 2014-2020. SFAO 1.0 (Support for the Fight Against Obesity) number of the WND-RPSL project. 01.02.00-24-06EA / 16) as well as for competitors in qualified sports SK system (qualified sports) project number WND-RPSL. 01.02.00-24-0630 / 17-002. The service provided in accordance with SFAO 1.0 has shown a wide range of therapy possibilities - from monitoring the body's reactions during sports activities of healthy people to remote care for sick patients. As a result of the introduction of an innovative service, it was possible to increase the effectiveness of the therapy, which was manifested in the reduction of the starting doses of drugs by 10%, improvement of the efficiency of the respiratory and blood circulation system, and a 10% increase in bone density. Innovation in the provision of medical services in the field of qualified sports SK was a response to the needs of the athletes and their parents, coaches, physiotherapists, dieticians, and doctors who take care of people actively practicing qualified sports. The creation of the platform made it possible to constantly monitor the trainers necessary for both the proper training process and the control over the health of patients. Monitoring the patient's health by a specialized team in the field of various specialties allows for the proper targeting of the treatment and training process due to the increase in the availability of medical counseling. Specialists taking care of the patient can provide additional advice and modify the medical treatment of the patient on an ongoing basis, which is why we are dealing with a holistic approach.

Keywords: innovation of medical services, sport, obesity, innovation

Procedia PDF Downloads 113

Authors: Ehsan Saghehei, Mahdi Eghbali

Abstract:

The maximal covering location problem (MCLP) was originally developed to determine a set of facility locations which would maximize the total customers' demand serviced by the facilities within a predetermined critical service criterion. However, on some problems that differences between the demand nodes are covered or the number of nodes each node is large, the method of solving MCLP may ignore these differences. In this paper, Heuristic solution based on the ranking of demands in each node and the number of nodes covered by each node according to a predetermined critical value is proposed. The output of this method is to maximize total demands of nodes and number of covered nodes, simultaneously. Furthermore, by providing an example, the solution algorithm is described and its results are compared with Greedy and Lagrange algorithms. Also, the results of the algorithm to solve the larger problem sizes that compared with other methods are provided. A summary and future works conclude the paper.

Keywords: heuristic solution, maximal covering location problem, ranking, set covering

Procedia PDF Downloads 554