Search results for: Search Algorithms

Authors: Madhuri Goswami

Abstract:

The matrix of power establishes the hegemonic dominance and supremacy of one group through exercising repression and relegation upon the other. However, the injustice done to any race, ethnicity or caste has instigated the protest and resistance through various modes- social campaigns, political movements, literary expression and so on. Consequently, the search for identity, the means of claiming it and strive for recognition have evolved as the persistent phenomena all through the world. In the discourse of protest and minority literature, these two discourses- African American and Indian Dalit- surprisingly, share wrath and anger, hope and aspiration, and quest for identity and struggle for self-assertion. African American and Indian Dalit are two geographically and culturally apart communities that stand together on a single platform. This paper has sought to comprehend the form and investigate the formation of identity in general and in the literary work of Toni Morrison and Indian Dalit writing, particularly i.e. Black identity and Dalit identity. The study has speculated two types of identity namely, individual or self and social or collective identity in the literary province of this marginalized literature. Morrison’s work outsources that self-identity is not merely a reflection of an inner essence; it is constructed through social circumstances and relations. Likewise, Dalit writings too have a fair record of the discovery of self-hood and formation of identity which connects to the realization of self-assertion and worthiness of their culture among Dalit writers. Bama, Pawar, Limbale, Pawde, and Kamble investigate their true self concealed amid societal alienation. The study has found that the struggle for recognition is, in fact, the striving to become the definer, instead of just being defined; and, this striving eventually, leads to the introspection among them. To conclude, Morrison as well as Indian marginalized authors, despite being set quite distant, communicate the relation between individual and community in the context of self-consciousness, self-identification, and (self) introspection. This research opens a scope for further research to find out similar phenomena and trace an analogy in other world literature.

Keywords: Identity, introspection, self-access, struggle for recognition

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 426

Authors: Srisuwan Kasemsawat

Abstract:

This is a survey research using quantitative and qualitative methodology. There were three objectives: 1) To study participatory level of community in water and waste environment management. 2) To study the affecting factors for community participation in water and waste environment management in Ampawa District, Samut Songkram Province. 3) To search for the participatory patterns in water and waste management. The population sample for the quantitative research was 1,364 people living in Ampawa District. The methodology was simple random sampling. Research instrument was a questionnaire and the qualitative research used purposive sampling in 6 Sub Districts which are Ta Ka, Suanluang, Bangkae, Muangmai, Kwae-om, and Bangnanglee Sub District Administration Organization. Total population is 63. For data analysis, the study used content analysis from quantitative research to synthesize and build question frame from the content for interview and conducting focus group interview. The study found that the community participatory in the issue of level in water and waste management are moderate of planning, operation, and evaluation. The issue of being beneficial is at low level. Therefore, the overall participatory level of community in water and waste environment management is at a medium level. The factors affecting the participatory of community in water and waste management are age, the period dwelling in the community and membership in which the mean difference is statistic significant at 0.05 in area of operation, being beneficial, and evaluation. For patterns of community participation, there is the correlation with water and waste management in 4 concerns which are 1) Participation in planning 2) Participation in operation 3) Participation in being beneficial both directly and indirectly benefited 4) Participation in evaluation and monitoring. The recommendation from this study is the need to create conscious awareness in order to increase participation level of people by organizing activities that promote participation with volunteer spirit. Government should open opportunities for people to participate in sharing ideas and create the culture of living together with equality which would build more concrete participation.

Keywords: Participation, Participatory Patterns, Water and Waste Management, Environmental Management.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2325

Authors: Aini Hussain, M A Hannan, Azah Mohamed, Hilmi Sanusi, Burhanuddin Yeop Majlis

Abstract:

Airbag deployment has been known to be responsible for huge death, incidental injuries and broken bones due to low crash severity and wrong deployment decisions. Therefore, the authorities and industries have been looking for more innovative and intelligent products to be realized for future enhancements in the vehicle safety systems (VSSs). Although the VSSs technologies have advanced considerably, they still face challenges such as how to avoid unnecessary and untimely airbag deployments that can be hazardous and fatal. Currently, most of the existing airbag systems deploy without regard to occupant size and position. As such, this paper will focus on the occupant and crash sensing performances due to frontal collisions for the new breed of so called smart airbag systems. It intends to provide a thorough discussion relating to the occupancy detection, occupant size classification, occupant off-position detection to determine safe distance zone for airbag deployment, crash-severity analysis and airbag decision algorithms via a computer modeling. The proposed system model consists of three main modules namely, occupant sensing, crash severity analysis and decision fusion. The occupant sensing system module utilizes the weight sensor to determine occupancy, classify the occupant size, and determine occupant off-position condition to compute safe distance for airbag deployment. The crash severity analysis module is used to generate relevant information pertinent to airbag deployment decision. Outputs from these two modules are fused to the decision module for correct and efficient airbag deployment action. Computer modeling work is carried out using Simulink, Stateflow, SimMechanics and Virtual Reality toolboxes.

Keywords: Crash severity analysis, occupant size classification, smart airbag, vehicle safety system.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4055

Authors: Thomas Schalow

Abstract:

This paper posits the need to take a cross-cultural approach to communication with non-human cultures and intelligences in order to meet the following three imminent contingencies: communicating with sentient biological intelligences, communicating with extraterrestrial intelligences, and communicating with artificial super-intelligences. The paper begins with a discussion of how intelligence emerges. It disputes some common assumptions we maintain about consciousness, intention, and language. The paper next explores cross-cultural communication among humans, including non-sapiens species. The next argument made is that we need to become much more serious about communicating with the non-human, intelligent life forms that already exist around us here on Earth. There is an urgent need to broaden our definition of communication and reach out to the other sentient life forms that inhabit our world. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it has proven useful, even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised and based on the cross-cultural approach to communication proposed in this paper if we are truly serious about finding and communicating with life beyond Earth. The final theme explored in this paper is communication with non-biological super-intelligences using a cross-cultural communication approach. This will present a serious challenge for humanity, as we have never been truly compelled to converse with other species, and our failure to seriously consider such intercourse has left us largely unprepared to deal with communication in a future that will be mediated and controlled by computer algorithms. Fortunately, our experience dealing with other human cultures can provide us with a framework for this communication. The basic assumptions behind intercultural communication can be applied to the many types of communication envisioned in this paper if we are willing to recognize that we are in fact dealing with other cultures when we interact with other species, alien life, and artificial super-intelligence. The ideas considered in this paper will require a new mindset for humanity, but a new disposition will prepare us to face the challenges posed by a future dominated by artificial intelligence.

Keywords: Artificial intelligence, CETI, communication, culture, language.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 940

Authors: F. Lazzeri, I. Reiter

Abstract:

Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: Time-series, features engineering methods for forecasting, energy demand forecasting, Azure machine learning.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1232

Authors: Ayman El Sabagh, Sobhy Sorour, Abd Elhamid Omar, Adel Ragab, Mohammad Sohidul Islam, Celaleddin Barutçular, Akihiro Ueda, Hirofumi Saneoka

Abstract:

Salinity is one of the major factors limiting crop production in an arid environment. Despite its global importance soybean production suffer the problems of salinity stress causing damages at plant development. So it is implacable to either search for salinity enhancement of soybean plants. Therefore, in the current study we try to clarify the mechanism that might be involved in the ameliorating effects of osmo-protectants such as proline and glycine betaine as well as, compost application on soybean plants grown under salinity stress. The experiment was conducted under greenhouse conditions at the Graduate School of Biosphere Science Laboratory of Hiroshima University, Japan in 2011. The experiment was designed as a spilt-split plot based on randomized complete block design with four replications. The treatments could be summarized as follows; (i) salinity concentrations (0 and 15 mM), (ii) compost treatments (0 and 24 t ha-1) and (iii) the exogenous, proline and glycine betaine concentrations (0 mM and 25 mM) for each. Results indicated that salinity stress induced reduction in growth and physiological aspects (dry weight per plant, chlorophyll content, N and K+ content) of soybean plant compared with those of the unstressed plants. On the other hand, salinity stress led to increases in the electrolyte leakage ratio, Na and proline contents. Special attention was paid to, the tolerance against salt stress was observed, the improvement of salt tolerance resulted from proline, glycine betaine and compost were accompanied with improved K+, and proline accumulation. While, significantly decreased electrolyte leakage ratio and Na+ content. These results clearly demonstrate that harmful effect of salinity could reduce on growth aspects of soybean. Consequently, exogenous osmoprotectants combine with compost will effectively solve seasonal salinity stress problem and are a good strategy to increase salinity resistance of soybean in the drylands.

Keywords: Compost, glycine betaine, growth, proline, salinity tolerance, soybean.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3175

Authors: Mohsen Nikaein

Abstract:

Because of importance of energy, optimization of power generation systems is necessary. Gas turbine cycles are suitable manner for fast power generation, but their efficiency is partly low. In order to achieving higher efficiencies, some propositions are preferred such as recovery of heat from exhaust gases in a regenerator, utilization of intercooler in a multistage compressor, steam injection to combustion chamber and etc. However thermodynamic optimization of gas turbine cycle, even with above components, is necessary. In this article multi-objective genetic algorithms are employed for Pareto approach optimization of Regenerative-Intercooling-Gas Turbine (RIGT) cycle. In the multiobjective optimization a number of conflicting objective functions are to be optimized simultaneously. The important objective functions that have been considered for optimization are entropy generation of RIGT cycle (Ns) derives using Exergy Analysis and Gouy-Stodola theorem, thermal efficiency and the net output power of RIGT Cycle. These objectives are usually conflicting with each other. The design variables consist of thermodynamic parameters such as compressor pressure ratio (Rp), excess air in combustion (EA), turbine inlet temperature (TIT) and inlet air temperature (T0). At the first stage single objective optimization has been investigated and the method of Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used for multi-objective optimization. Optimization procedures are performed for two and three objective functions and the results are compared for RIGT Cycle. In order to investigate the optimal thermodynamic behavior of two objectives, different set, each including two objectives of output parameters, are considered individually. For each set Pareto front are depicted. The sets of selected decision variables based on this Pareto front, will cause the best possible combination of corresponding objective functions. There is no superiority for the points on the Pareto front figure, but they are superior to any other point. In the case of three objective optimization the results are given in tables.

Keywords: Exergy, Entropy Generation, Brayton Cycle, DesignParameters, Optimization, Genetic Algorithm, Multi-Objective.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2479

Authors: R. Arulmurugan, P. Sengottuvelan

Abstract:

Object detection using Wavelet Neural Network (WNN) plays a major contribution in the analysis of image processing. Existing cluster-based algorithm for co-saliency object detection performs the work on the multiple images. The co-saliency detection results are not desirable to handle the multi scale image objects in WNN. Existing Super Resolution (SR) scheme for landmark images identifies the corresponding regions in the images and reduces the mismatching rate. But the Structure-aware matching criterion is not paying attention to detect multiple regions in SR images and fail to enhance the result percentage of object detection. To detect the objects in the high-resolution remote sensing images, Tagged Grid Matching (TGM) technique is proposed in this paper. TGM technique consists of the three main components such as object determination, object searching and object verification in WNN. Initially, object determination in TGM technique specifies the position and size of objects in the current image. The specification of the position and size using the hierarchical grid easily determines the multiple objects. Second component, object searching in TGM technique is carried out using the cross-point searching. The cross out searching point of the objects is selected to faster the searching process and reduces the detection time. Final component performs the object verification process in TGM technique for identifying (i.e.,) detecting the dissimilarity of objects in the current frame. The verification process matches the search result grid points with the stored grid points to easily detect the objects using the Gabor wavelet Transform. The implementation of TGM technique offers a significant improvement on the multi-object detection rate, processing time, precision factor and detection accuracy level.

Keywords: Object Detection, Cross-point Searching, Wavelet Neural Network, Object Determination, Gabor Wavelet Transform, Tagged Grid Matching.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1922

Authors: Roberto Sabatini, Terry Moore, Chris Hill

Abstract:

This paper presents a novel Global Navigation Satellite System (GNSS) Avionics Based Integrity Augmentation (ABIA) system architecture suitable for civil and military air platforms, including Unmanned Aircraft Systems (UAS). Taking the move from previous research on high-accuracy Differential GNSS (DGNSS) systems design, integration and experimental flight test activities conducted at the Italian Air Force Flight Test Centre (CSV-RSV), our research focused on the development of a novel approach to the problem of GNSS ABIA for mission- and safety-critical air vehicle applications and for multi-sensor avionics architectures based on GNSS. Detailed mathematical models were developed to describe the main causes of GNSS signal outages and degradation in flight, namely: antenna obscuration, multipath, fading due to adverse geometry and Doppler shift. Adopting these models in association with suitable integrity thresholds and guidance algorithms, the ABIA system is able to generate integrity cautions (predictive flags) and warnings (reactive flags), as well as providing steering information to the pilot and electronic commands to the aircraft/UAS flight control systems. These features allow real-time avoidance of safety-critical flight conditions and fast recovery of the required navigation performance in case of GNSS data losses. In other words, this novel ABIA system addresses all three cornerstones of GNSS integrity augmentation in mission- and safety-critical applications: prediction (caution flags), reaction (warning flags) and correction (alternate flight path computation).

Keywords: Global Navigation Satellite Systems (GNSS), Integrity Augmentation, Unmanned Aircraft Systems, Aircraft Based Augmentation, Avionics Based Integrity Augmentation, Safety-Critical Applications.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3173

Authors: B. Frączek

Abstract:

The paper analyses the usage of the Internet by university students in Visegrad Countries (4V Countries) who study economic fields in their formal and informal financial education and captures the areas of untapped potential of Internet in educational processes. Higher education and training, technological readiness, and the financial market development are in the group of pillars, that are key for efficiency driven economies. These three pillars have become an inspiration to the research on using the Internet in the financial education among economic university students as the group of the best educated people in finance. The financial education is a process that allows for improving the level of financial literacy. In turn, the financial literacy it is the set of financial knowledge, skills, awareness and patterns influencing the financial decisions. The level of financial literacy influences the level of financial well-being of individuals, determines the scale of saving of households and at the same time gives the greater chance for sustainable and more predictable development of the financial market with the positive impact on economy. The financial literacy is necessary for each group of society but its appropriate level is desirable especially in respect of economics students as future participants of financial markets as well as the experts and advisors in financial decision making. The low level of financial literacy is the great problem of many target groups in both developing and developed countries and the financial education is seen as the best way of improving this situation. Also the financial inclusion plays the special role in enhancing the level of financial literacy in the aspect of education by practice as well as due to interrelation between level of financial literacy and degree of financial inclusion. Despite many initiatives under financial education, the level of financial literacy is still very low. Scientists still search for new ways of solving this problem. One of the proposal is more effective usage of the new technology in financial education, especially the Internet, because of the growing popularity of e-learning and the increasing number of Internet users, especially among young people who are called the Generation Net. Due to special role of the university students studying the economics fields for the future financial markets, students of four universities from Visegrad Countries (Czech Republic, Hungary, Poland and Slovakia) were invited to participate in the survey. The aim of the article is to present the level and ways of using the Internet technology in financial education and indicating the so far unused or underused opportunities.

Keywords: Financial education, financial inclusion, financial literacy, usage of Internet in education.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1485

Authors: Laura Çami (Vorpsi), Xhon Skënderi

Abstract:

In today's rapidly evolving technological landscape, the traditional principles of contract law are facing significant challenges. The emergence of new technologies, such as electronic signatures, smart contracts, and online dispute resolution mechanisms, is transforming the way contracts are formed, interpreted, and enforced. This paper examines the implications of these technological advancements on the constitutional principles of contract law. One of the fundamental principles of contract law is freedom of contract, which ensures that parties have the autonomy to negotiate and enter into contracts as they see fit. However, the use of technology in the contracting process has the potential to disrupt this principle. For example, online platforms and marketplaces often offer standard-form contracts, which may not reflect the specific needs or interests of individual parties. This raises questions about the equality of bargaining power between parties and the extent to which parties are truly free to negotiate the terms of their contracts. Another important principle of contract law is the requirement of consideration, which requires that each party receives something of value in exchange for their promise. The use of digital assets, such as cryptocurrencies, has created new challenges in determining what constitutes valuable consideration in a contract. Due to the ambiguity in this area, disagreements about the legality and enforceability of such contracts may arise. Furthermore, the use of technology in dispute resolution mechanisms, such as online arbitration and mediation, may raise concerns about due process and access to justice. The use of algorithms and artificial intelligence to determine the outcome of disputes may also raise questions about the impartiality and fairness of the process. Finally, it should be noted that there are many different and complex effects of technical improvements on the fundamental constitutional foundations of contract law. As technology continues to evolve, it will be important for policymakers and legal practitioners to consider the potential impacts on contract law and to ensure that the principles of fairness, equality, and access to justice are preserved in the contracting process.

Keywords: Technological advancements, constitutional principles, contract law, smart contracts, online dispute resolution, freedom of contract.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 112

Authors: Dhananjay C. Joshi, Jung-Hsin Lin

Abstract:

Protein-protein interactions (PPI) play a crucial role in many biological processes such as cell signalling, transcription, translation, replication, signal transduction, and drug targeting, etc. Structural information about protein-protein interaction is essential for understanding the molecular mechanisms of these processes. Structures of protein-protein complexes are still difficult to obtain by biophysical methods such as NMR and X-ray crystallography, and therefore protein-protein docking computation is considered an important approach for understanding protein-protein interactions. However, reliable prediction of the protein-protein complexes is still under way. In the past decades, several grid-based docking algorithms based on the Katchalski-Katzir scoring scheme were developed, e.g., FTDock, ZDOCK, HADDOCK, RosettaDock, HEX, etc. However, the success rate of protein-protein docking prediction is still far from ideal. In this work, we first propose a more practical measure for evaluating the success of protein-protein docking predictions,the rate of first success (RFS), which is similar to the concept of mean first passage time (MFPT). Accordingly, we have assessed the ZDOCK bound and unbound benchmarks 2.0 and 3.0. We also createda new benchmark set for protein-protein docking predictions, in which the complexes have experimentally determined binding affinity data. We performed free energy calculation based on the solution of non-linear Poisson-Boltzmann equation (nlPBE) to improve the binding mode prediction. We used the well-studied thebarnase-barstarsystem to validate the parameters for free energy calculations. Besides,thenlPBE-based free energy calculations were conducted for the badly predicted cases by ZDOCK and ZRANK. We found that direct molecular mechanics energetics cannot be used to discriminate the native binding pose from the decoys.Our results indicate that nlPBE-based calculations appeared to be one of the promising approaches for improving the success rate of binding pose predictions.

Keywords: protein-protein docking, protein-protein interaction, molecular mechanics energetics, Poisson-Boltzmann calculations

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1751

Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

Abstract:

In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: Image processing, Illumination equalization, Shadow filtering, Object detection, Colour models, Image segmentation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 971

Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

Abstract:

One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets, especially in the motorist sector, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of Python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. 60 vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes that the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: Convolutional Neural Network, CNN, location identification, tracking, GPS, GSM.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 322

Authors: Abdellali Kelil, Shengrui Wang, Ryszard Brzezinski

Abstract:

The literature reports a large number of approaches for measuring the similarity between protein sequences. Most of these approaches estimate this similarity using alignment-based techniques that do not necessarily yield biologically plausible results, for two reasons. First, for the case of non-alignable (i.e., not yet definitively aligned and biologically approved) sequences such as multi-domain, circular permutation and tandem repeat protein sequences, alignment-based approaches do not succeed in producing biologically plausible results. This is due to the nature of the alignment, which is based on the matching of subsequences in equivalent positions, while non-alignable proteins often have similar and conserved domains in non-equivalent positions. Second, the alignment-based approaches lead to similarity measures that depend heavily on the parameters set by the user for the alignment (e.g., gap penalties and substitution matrices). For easily alignable protein sequences, it's possible to supply a suitable combination of input parameters that allows such an approach to yield biologically plausible results. However, for difficult-to-align protein sequences, supplying different combinations of input parameters yields different results. Such variable results create ambiguities and complicate the similarity measurement task. To overcome these drawbacks, this paper describes a novel and effective approach for measuring the similarity between protein sequences, called SAF for Substitution and Alignment Free. Without resorting either to the alignment of protein sequences or to substitution relations between amino acids, SAF is able to efficiently detect the significant subsequences that best represent the intrinsic properties of protein sequences, those underlying the chronological dependencies of structural features and biochemical activities of protein sequences. Moreover, by using a new efficient subsequence matching scheme, SAF more efficiently handles protein sequences that contain similar structural features with significant meaning in chronologically non-equivalent positions. To show the effectiveness of SAF, extensive experiments were performed on protein datasets from different databases, and the results were compared with those obtained by several mainstream algorithms.

Keywords: Protein, Similarity, Substitution, Alignment.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1360

Authors: Lubna Eljabu, Mohammad Etemad, Stan Matwin

Abstract:

The automatic extraction of shipping routes is advantageous for intelligent traffic management systems to identify events and support decision-making in maritime surveillance. At present, there is a high demand for the extraction of maritime traffic networks that resemble the real traffic of vessels accurately, which is valuable for further analytical processing tasks for vessels trajectories (e.g., naval routing and voyage planning, anomaly detection, destination prediction, time of arrival estimation). With the help of big data and processing huge amounts of vessels’ trajectory data, it is possible to learn these shipping routes from the navigation history of past behaviour of other, similar ships that were travelling in a given area. In this paper, we propose a spatial clustering model of vessels’ trajectories (SPTCLUST) to extract spatial representations of sailing routes from historical Automatic Identification System (AIS) data. The whole model consists of three main parts: data preprocessing, path finding, and route extraction, which consists of clustering and representative trajectory extraction. The proposed clustering method provides techniques to overcome the problems of: (i) optimal input parameters selection; (ii) the high complexity of processing a huge volume of multidimensional data; (iii) and the spatial representation of complete representative trajectory detection in the context of trajectory clustering algorithms. The experimental evaluation showed the effectiveness of the proposed model by using a real-world AIS dataset from the Port of Halifax. The results contribute to further understanding of shipping route patterns. This could aid surveillance authorities in stable and sustainable vessel traffic management.

Keywords: Vessel trajectory clustering, trajectory mining, Spatial Clustering, marine intelligent navigation, maritime traffic network extraction, sdailing routes extraction.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 353

Authors: A. El Harraj, N. Raissouni

Abstract:

The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes invariance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: Video surveillance, background subtraction, Contrast Limited Histogram Equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2038

Authors: Mbainaibeye Jérôme, Noureddine Ellouze

Abstract:

Wavelet transforms is a very powerful tools for image compression. One of its advantage is the provision of both spatial and frequency localization of image energy. However, wavelet transform coefficients are defined by both a magnitude and sign. While algorithms exist for efficiently coding the magnitude of the transform coefficients, they are not efficient for the coding of their sign. It is generally assumed that there is no compression gain to be obtained from the coding of the sign. Only recently have some authors begun to investigate the sign of wavelet coefficients in image coding. Some authors have assumed that the sign information bit of wavelet coefficients may be encoded with the estimated probability of 0.5; the same assumption concerns the refinement information bit. In this paper, we propose a new method for Separate Sign Coding (SSC) of wavelet image coefficients. The sign and the magnitude of wavelet image coefficients are examined to obtain their online probabilities. We use the scalar quantization in which the information of the wavelet coefficient to belong to the lower or to the upper sub-interval in the uncertainly interval is also examined. We show that the sign information and the refinement information may be encoded by the probability of approximately 0.5 only after about five bit planes. Two maps are separately entropy encoded: the sign map and the magnitude map. The refinement information of the wavelet coefficient to belong to the lower or to the upper sub-interval in the uncertainly interval is also entropy encoded. An algorithm is developed and simulations are performed on three standard images in grey scale: Lena, Barbara and Cameraman. Five scales are performed using the biorthogonal wavelet transform 9/7 filter bank. The obtained results are compared to JPEG2000 standard in terms of peak signal to noise ration (PSNR) for the three images and in terms of subjective quality (visual quality). It is shown that the proposed method outperforms the JPEG2000. The proposed method is also compared to other codec in the literature. It is shown that the proposed method is very successful and shows its performance in term of PSNR.

Keywords: Image compression, wavelet transform, sign coding, magnitude coding.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1626

Authors: Mohamed K. Shahin, Ahmed M. Badawi, Mohamed E. M. Rasmy

Abstract:

Prior research evidenced that unimodal biometric systems have several tradeoffs like noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. In order for the biometric system to be more secure and to provide high performance accuracy, more than one form of biometrics are required. Hence, the need arise for multimodal biometrics using combinations of different biometric modalities. This paper introduces a multimodal biometric system (MMBS) based on fusion of whole dorsal hand geometry and fingerprints that acquires right and left (Rt/Lt) near-infra-red (NIR) dorsal hand geometry (HG) shape and (Rt/Lt) index and ring fingerprints (FP). Database of 100 volunteers were acquired using the designed prototype. The acquired images were found to have good quality for all features and patterns extraction to all modalities. HG features based on the hand shape anatomical landmarks were extracted. Robust and fast algorithms for FP minutia points feature extraction and matching were used. Feature vectors that belong to similar biometric traits were fused using feature fusion methodologies. Scores obtained from different biometric trait matchers were fused using the Min-Max transformation-based score fusion technique. Final normalized scores were merged using the sum of scores method to obtain a single decision about the personal identity based on multiple independent sources. High individuality of the fused traits and user acceptability of the designed system along with its experimental high performance biometric measures showed that this MMBS can be considered for med-high security levels biometric identification purposes.

Keywords: Unimodal, Multi-Modal, Biometric System, NIR Imaging, Dorsal Hand Geometry, Fingerprint, Whole Hands, Feature Extraction, Feature Fusion, Score Fusion

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2172

Authors: Sokkhey Phauk, Takeo Okazaki

Abstract:

The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: Academic performance prediction system, prediction model, educational data mining, dominant factors, feature selection methods, student performance.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 907

Authors: Gianpaolo Ghiani, Emanuela Guerriero, Emanuele Manni, Alessandro Romano

Abstract:

In this article, we deal with a variant of the classical course timetabling problem that has a practical application in many areas of education. In particular, in this paper we are interested in high schools remedial courses. The purpose of such courses is to provide under-prepared students with the skills necessary to succeed in their studies. In particular, a student might be under prepared in an entire course, or only in a part of it. The limited availability of funds, as well as the limited amount of time and teachers at disposal, often requires schools to choose which courses and/or which teaching units to activate. Thus, schools need to model the training offer and the related timetabling, with the goal of ensuring the highest possible teaching quality, by meeting the above-mentioned financial, time and resources constraints. Moreover, there are some prerequisites between the teaching units that must be satisfied. We first present a Mixed-Integer Programming (MIP) model to solve this problem to optimality. However, the presence of many peculiar constraints contributes inevitably in increasing the complexity of the mathematical model. Thus, solving it through a general-purpose solver may be performed for small instances only, while solving real-life-sized instances of such model requires specific techniques or heuristic approaches. For this purpose, we also propose a heuristic approach, in which we make use of a fast constructive procedure to obtain a feasible solution. To assess our exact and heuristic approaches we perform extensive computational results on both real-life instances (obtained from a high school in Lecce, Italy) and randomly generated instances. Our tests show that the MIP model is never solved to optimality, with an average optimality gap of 57%. On the other hand, the heuristic algorithm is much faster (in about the 50% of the considered instances it converges in approximately half of the time limit) and in many cases allows achieving an improvement on the objective function value obtained by the MIP model. Such an improvement ranges between 18% and 66%.

Keywords: Heuristic, MIP model, Remedial course, School, Timetabling.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1592

Authors: Shih-Wen Hsiao, Yi-Cheng Tsao

Abstract:

In the field of reverse engineering and creative industries, applying 3D scanning process to obtain geometric forms of the objects is a mature and common technique. For instance, organic objects such as faces and nonorganic objects such as products could be scanned to acquire the geometric information for further application. However, although the data resolution of 3D scanning device is increasing and there are more and more abundant complementary applications, the penetration rate of 3D scanning for the public is still limited by the relative high price of the devices. On the other hand, Kinect, released by Microsoft, is known for its powerful functions, considerably low price, and complete technology and database support. Therefore, related studies can be done with the applying of Kinect under acceptable cost and data precision. Due to the fact that Kinect utilizes optical mechanism to extracting depth information, limitations are found due to the reason of the straight path of the light. Thus, various angles are required sequentially to obtain the complete 3D information of the object when applying a single Kinect for 3D scanning. The integration process which combines the 3D data from different angles by certain algorithms is also required. This sequential scanning process costs much time and the complex integration process often encounter some technical problems. Therefore, this paper aimed to apply multiple Kinects simultaneously on the field of developing a rapid 3D mannequin scan platform and proposed suggestions on the number and angles of Kinects. In the content, a method of establishing the coordination based on the relation between mannequin and the specifications of Kinect is proposed, and a suggestion of angles and number of Kinects is also described. An experiment of applying multiple Kinect on the scanning of 3D mannequin is constructed by Microsoft API, and the results show that the time required for scanning and technical threshold can be reduced in the industries of fashion and garment design.

Keywords: 3D scan, depth sensor, fashion and garment design, mannequin, multiple kinect sensor.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2228

Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

Abstract:

Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, unmanned aerial vehicle, UAV, random, Kriging.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 738

Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

Abstract:

Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: Canny pruning, hand recognition, machine learning, skin tracking.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1233

Authors: A. Fathianpour, M. B. Jelodar, S. Wilkinson

Abstract:

This paper outlines the approaches taken to assess multi-hazard assessments. There is currently confusion in assessing multi-hazard impacts, and so this study aims to determine which of the available options are the most useful. The paper uses an international literature search, and analysis of current multi-hazard assessments and a case study to illustrate the effectiveness of the chosen method. Findings from this study will help those wanting to assess multi-hazards to undertake a straightforward approach. The paper is significant as it helps to interpret the various approaches and concludes with the preferred method. Many people in the world live in hazardous environments and are susceptible to disasters. Unfortunately, when a disaster strikes it is often compounded by additional cascading hazards, thus people would confront more than one hazard simultaneously. Hazards include natural hazards (earthquakes, floods, etc.) or cascading human-made hazards (for example, Natural Hazard Triggering Technological disasters (Natech) such as fire, explosion, toxic release). Multi-hazards have a more destructive impact on urban areas than one hazard alone. In addition, climate change is creating links between different disasters such as causing landslide dams and debris flows leading to more destructive incidents. Much of the prevailing literature deals with only one hazard at a time. However, recently sophisticated multi-hazard assessments have started to appear. Given that multi-hazards occur, it is essential to take multi-hazard risk assessment under consideration. This paper aims to review the multi-hazard assessment methods through articles published to date and categorize the strengths and disadvantages of using these methods in risk assessment. Napier City is selected as a case study to demonstrate the necessity of using multi-hazard risk assessments. In order to assess multi-hazard risk assessments, first, the current multi-hazard risk assessment methods were described. Next, the drawbacks of these multi-hazard risk assessments were outlined. Finally, the improvements to current multi-hazard risk assessments to date were summarised. Generally, the main problem of multi-hazard risk assessment is to make a valid assumption of risk from the interactions of different hazards. Currently, risk assessment studies have started to assess multi-hazard situations, but drawbacks such as uncertainty and lack of data show the necessity for more precise risk assessment. It should be noted that ignoring or partial considering multi-hazards in risk assessment will lead to an overestimate or overlook in resilient and recovery action managements.

Keywords: Cascading hazards, multi-hazard, risk assessment, risk reduction.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1032

Authors: Sana Hamdi, Emna Bouazizi, Sami Faiz

Abstract:

In recent years, real-time spatial applications, like location-aware services and traffic monitoring, have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of our computing infrastructure. For instance, in real-time spatial Big Data, users expect to receive the results of each query within a short time period without holding in account the load of the system. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly especially in overload situations. To solve this problem, we propose the use of data partitioning as an optimization technique. Traditional horizontal and vertical partitioning can increase the performance of the system and simplify data management. But they remain insufficient for real-time spatial Big data; they can’t deal with real-time and stream queries efficiently. Thus, in this paper, we propose a novel data partitioning approach for real-time spatial Big data named VPA-RTSBD (Vertical Partitioning Approach for Real-Time Spatial Big data). This contribution is an implementation of the Matching algorithm for traditional vertical partitioning. We find, firstly, the optimal attribute sequence by the use of Matching algorithm. Then, we propose a new cost model used for database partitioning, for keeping the data amount of each partition more balanced limit and for providing a parallel execution guarantees for the most frequent queries. VPA-RTSBD aims to obtain a real-time partitioning scheme and deals with stream data. It improves the performance of query execution by maximizing the degree of parallel execution. This affects QoS (Quality Of Service) improvement in real-time spatial Big Data especially with a huge volume of stream data. The performance of our contribution is evaluated via simulation experiments. The results show that the proposed algorithm is both efficient and scalable, and that it outperforms comparable algorithms.

Keywords: Real-Time Spatial Big Data, Quality Of Service, Vertical partitioning, Horizontal partitioning, Matching algorithm, Hamming distance, Stream query.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1004

Authors: Valeria Tatano, Francesca Guidolin, Francesca Peltrera

Abstract:

The preservation of historical Italian heritage, at the urban and architectural scale, has to consider restrictions and requirements connected with conservation issues and usability needs, which are often at odds with historical heritage preservation. Recent decades have been marked by the search for increased accessibility not only of public and private buildings, but to the whole historical city, also for people with disability. Moreover, in the last years the concepts of Smart City and Healthy City seek to improve accessibility both in terms of mobility (independent or assisted) and fruition of goods and services, also for historical cities. The principles of Inclusive Design have introduced new criteria for the improvement of public urban space, between current regulations and best practices. Moreover, they have contributed to transforming “special needs” into an opportunity of social innovation. These considerations find a field of research and analysis in the historical city of Venice, which is at the same time a site of UNESCO world heritage, a mass tourism destination bringing in visitors from all over the world and a city inhabited by an aging population. Due to its conformation, Venetian urban fabric is only partially accessible: about four thousand bridges divide thousands of islands, making it almost impossible to move independently. These urban characteristics and difficulties were the base, in the last 20 years, for several researches, experimentations and solutions with the aim of eliminating architectural barriers, in particular for the usability of bridges. The Venetian Municipality with the EBA Office and some external consultants realized several devices (e.g. the “stepped ramp” and the new accessible ramps for the Venice Marathon) that should determine an innovation for the city, passing from the use of mechanical replicable devices to specific architectural projects in order to guarantee autonomy in use. This paper intends to present the state-of-the-art in bridges accessibility, through an analysis based on Inclusive Design principles and on the current national and regional regulation. The purpose is to evaluate some possible strategies that could improve performances, between limits and possibilities of interventions. The aim of the research is to lay the foundations for the development of a strategic program for the City of Venice that could successfully bring together both conservation and improvement requirements.

Keywords: Accessibility and inclusive design, historical heritage preservation, technological and social innovation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1340

Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

Abstract:

Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity and aflatoxinogenic capacity of the strains, topography, soil and climate parameters of the fig orchards are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high-performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques i.e., dimensionality reduction on the original dataset (Principal Component Analysis), metric learning (Mahalanobis Metric for Clustering) and K-nearest Neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson Correlation Coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 571

Authors: Sagir M. Yusuf, Chris Baber

Abstract:

In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Lèvy flight, situation awareness, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 464

Authors: Paola Lecca, Lorenzo Dematte

Abstract:

Reactiondiffusion systems are mathematical models that describe how the concentration of one or more substances distributed in space changes under the influence of local chemical reactions in which the substances are converted into each other, and diffusion which causes the substances to spread out in space. The classical representation of a reaction-diffusion system is given by semi-linear parabolic partial differential equations, whose general form is ÔêétX(x, t) = DΔX(x, t), where X(x, t) is the state vector, D is the matrix of the diffusion coefficients and Δ is the Laplace operator. If the solute move in an homogeneous system in thermal equilibrium, the diffusion coefficients are constants that do not depend on the local concentration of solvent and of solutes and on local temperature of the medium. In this paper a new stochastic reaction-diffusion model in which the diffusion coefficients are function of the local concentration, viscosity and frictional forces of solvent and solute is presented. Such a model provides a more realistic description of the molecular kinetics in non-homogenoeus and highly structured media as the intra- and inter-cellular spaces. The movement of a molecule A from a region i to a region j of the space is described as a first order reaction Ai k- → Aj , where the rate constant k depends on the diffusion coefficient. Representing the diffusional motion as a chemical reaction allows to assimilate a reaction-diffusion system to a pure reaction system and to simulate it with Gillespie-inspired stochastic simulation algorithms. The stochastic time evolution of the system is given by the occurrence of diffusion events and chemical reaction events. At each time step an event (reaction or diffusion) is selected from a probability distribution of waiting times determined by the specific speed of reaction and diffusion events. Redi is the software tool, developed to implement the model of reaction-diffusion kinetics and dynamics. It is a free software, that can be downloaded from http://www.cosbi.eu. To demonstrate the validity of the new reaction-diffusion model, the simulation results of the chaperone-assisted protein folding in cytoplasm obtained with Redi are reported. This case study is redrawing the attention of the scientific community due to current interests on protein aggregation as a potential cause for neurodegenerative diseases.

Keywords: Reaction-diffusion systems, Fick's law, stochastic simulation algorithm.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1696