Search results for: evolution algorithm

Authors: Taisir Eldos, Aws Kanan, Waleed Nazih, Ahmad Khatatbih

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Chemical Reaction Optimization (CRO) is an optimization metaheuristic inspired by the nature of chemical reactions as a natural process of transforming the substances from unstable to stable states. Starting with some unstable molecules with excessive energy, a sequence of interactions takes the set to a state of minimum energy. Researchers reported successful application of the algorithm in solving some engineering problems, like the quadratic assignment problem, with superior performance when compared with other optimization algorithms. We adapted this optimization algorithm to the Printed Circuit Board Drilling Problem (PCBDP) towards reducing the drilling time and hence improving the PCB manufacturing throughput. Although the PCBDP can be viewed as instance of the popular Traveling Salesman Problem (TSP), it has some characteristics that would require special attention to the transactions that explore the solution landscape. Experimental test results using the standard CROToolBox are not promising for practically sized problems, while it could find optimal solutions for artificial problems and small benchmarks as a proof of concept.

Keywords: evolutionary algorithms, chemical reaction optimization, traveling salesman, board drilling

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Authors: Ali Babaei

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There is a significant relationship between Soul Faculties and human relationships. Man has at least three levels of relationship according to three levels of his Faculties: individual (with himself), dual (with another) and collective (with others). Since all human actions are organized by the type of use of their internal faculties, their "hierarchy of relations" is related to the "hierarchy of their Faculties." In the final explanation based on the ontology of Islamic wisdom, one can consider the hierarchy of human Faculties in three levels: 1. senses, 2. intellect and heart, and 3. Soul. The best relationship, in the individual one is that every human being, with healthy senses, achieves both the intellectual growth and the perfection of the heart, which we call "Clear-headed" and "Good-hearted.” The result of human evolution in this two aspects will lead to the development of a powerful personality which can be interpreted as "spiritual prosperity"; having a great soul is the result of such evolution. A smart brain without a "Good-heart"ince can lead to criminality; and mere "Good-heart"ince" without "Clear-head"ince leads to "naivety". “clear-head”ince is achieved through thoughtfulness and study, and "Good-heart"ince through love and worship. So the best way to achieve perfection in a personal relationship is to have a dependable appearance, a coherent thinking

Keywords: Ontology , good-heartince, wisdom, relationship, clear-head”ince, criminality, naivety

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Authors: Amin Sedighfar, M. R. Moniri

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Determination of state of charge (SOC) in today’s world becomes an increasingly important issue in all the applications that include a battery. In fact, estimation of the SOC is a fundamental need for the battery, which is the most important energy storage in Hybrid Electric Vehicles (HEVs), smart grid systems, drones, UPS and so on. Regarding those applications, the SOC estimation algorithm is expected to be precise and easy to implement. This paper presents an online method for the estimation of the SOC of Valve-Regulated Lead Acid (VRLA) batteries. The proposed method uses the well-known Kalman Filter (KF), and Neural Networks (NNs) and all of the simulations have been done with MATLAB software. The NN is trained offline using the data collected from the battery discharging process. A generic cell model is used, and the underlying dynamic behavior of the model has used two capacitors (bulk and surface) and three resistors (terminal, surface, and end), where the SOC determined from the voltage represents the bulk capacitor. The aim of this work is to compare the performance of conventional integration-based SOC estimation methods with a mixed algorithm. Moreover, by containing the effect of temperature, the final result becomes more accurate. 

Keywords: Kalman filter, neural networks, state-of-charge, VRLA battery

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Authors: Ibrahim Gomaa, Hoda M. O. Mokhtar

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Although most of the existing skyline queries algorithms focused basically on querying static points through static databases; with the expanding number of sensors, wireless communications and mobile applications, the demand for continuous skyline queries has increased. Unlike traditional skyline queries which only consider static attributes, continuous skyline queries include dynamic attributes, as well as the static ones. However, as skyline queries computation is based on checking the domination of skyline points over all dimensions, considering both the static and dynamic attributes without separation is required. In this paper, we present an efficient algorithm for computing continuous skyline queries without discriminating between static and dynamic attributes. Our algorithm in brief proceeds as follows: First, it excludes the points which will not be in the initial skyline result; this pruning phase reduces the required number of comparisons. Second, the association between the spatial positions of data points is examined; this phase gives an idea of where changes in the result might occur and consequently enables us to efficiently update the skyline result (continuous update) rather than computing the skyline from scratch. Finally, experimental evaluation is provided which demonstrates the accuracy, performance and efficiency of our algorithm over other existing approaches.

Keywords: continuous query processing, dynamic database, moving object, skyline queries

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Feixiang Zhao, Shuangcheng Jia, Qian Li

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High-precision measurement of the target’s position and size is one of the hotspots in the field of vision inspection. This paper proposes a three-dimensional object positioning and measurement method using a monocular camera and GPS, namely the Distance Area Number-New Minimize Projection Error (DAN-NMPE). Our algorithm contains two parts: DAN and NMPE; specifically, DAN is a picture sequence algorithm, NMPE is a relatively positive optimization algorithm, which greatly improves the measurement accuracy of the target’s position and size. Comprehensive experiments validate the effectiveness of our proposed method on a self-made traffic sign dataset. The results show that with the laser point cloud as the ground truth, the size and position errors of the traffic sign measured by this method are ± 5% and 0.48 ± 0.3m, respectively. In addition, we also compared it with the current mainstream method, which uses a monocular camera to locate and measure traffic signs. DAN-NMPE attains significant improvements compared to existing state-of-the-art methods, which improves the measurement accuracy of size and position by 50% and 15.8%, respectively.

Keywords: monocular camera, GPS, positioning, measurement

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Authors: Marek Długosz, Paweł Skruch

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One of purposes of home automation systems is to provide appropriate comfort to the users by suitable air temperature control and stabilization inside the rooms. The control of temperature level is not a simple task and the basic difficulty results from the fact that accurate parameters of the object of control, that is a building, remain unknown. Whereas the structure of the model is known, the identification of model parameters is a difficult task. In this paper, a control algorithm allowing the present temperature to be reached inside the building within the specified time without the need to know accurate parameters of the building itself is presented.

Keywords: control, home automation system, wireless networking, automation engineering

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Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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Authors: Mouslem Damkhi

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This paper focuses on possible states of a football tournament final table according to the number of participating teams. Each team holds a position in the table with which it is possible to determine the highest and lowest points for that team. This paper proposes an optimized search space based on the minimum and maximum number of points which can be gained by each team to produce and enumerate the possible states for a football tournament final table. The proposed search space minimizes producing the invalid states which cannot occur during a football tournament. The generated states are filtered by a validity checking algorithm which seeks to reach a tournament graph based on a generated state. Thus, the algorithm provides a way to determine which team’s wins, draws and loses values guarantee a particular table position. The paper also presents and discusses the experimental results of the approach on the tournaments with up to eight teams. Comparing with a blind search algorithm, our proposed approach reduces generating the invalid states up to 99.99%, which results in a considerable optimization in term of the execution time.

Keywords: combinatorics, enumeration, graph, tournament

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Authors: Kamal Agrawal, Ved Prakash Nayak, Akshay Patil

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Raipur is known as a city of water bodies. The city had around 200 man-made and natural lakes of varying sizes. These structures were constructed to collect rainwater and control flooding in the city. Due to the transition from community participation to state government, as well as rapid urbanisation, Raipur now has only about 80 lakes left. Rapid and unplanned growth has resulted in pollution, encroachment, and eutrophication of the city's lakes. The state government keeps these lakes in good condition by cleaning them and proposing lakefront developments. However, maintaining individual lakes is insufficient because urban lakes are not distinct entities. It is a system comprised of the lake, shore, catchment, and other components. While Urban lake system (ULS) is a combination of multiple such lake systems interacting in a complex urban setting. Thus, the project aims to propose a co-evolution model for urban lake systems (ULS) and rapid urbanization in Raipur. The goals are to comprehend the ULS and to identify elements and dimensions of urbanization that influence the ULS. Evaluate the impact of rapid urbanization on the ULS & vice versa in the study area. Determine how to maximize the positive impact while minimizing the negative impact identified in the study area. Propose short-, medium-, and long-term planning interventions to support the ULS's co-evolution with rapid urbanization. A complexity approach is used to investigate the ULS. It is a technique for understanding large, complex systems. A complex system is one with many interconnected and interdependent elements and dimensions. Thus, elements of ULS and rapid urbanization are identified through a literature study to evaluate statements of their impacts (Beneficial/ Adverse) on one another. Rapid urbanization has been identified as having elements such as demography, urban legislation, informal settlement, urban infrastructure, and tourism. Similarly, the catchment area of the lake, the lake's water quality, the water spread area, and lakefront developments are all being impacted by rapid urbanisation. These nine elements serve as parameters for the subsequent analysis. Elements are limited to physical parameters only. The city has designated a study area based on the definition provided by the National Plan for the Conservation of Aquatic Ecosystems. Three lakes are discovered within a one-kilometer radius, establishing a tiny urban lake system. Because the condition of a lake is directly related to the condition of its catchment area, the catchment area of these three lakes is delineated as the study area. Data is collected to identify impact statements, and the interdependence diagram generated between the parameters yields results in terms of interlinking between each parameter and their impact on the system as a whole. The planning interventions proposed for the ULS and rapid urbanisation co-evolution model include spatial proposals as well as policy recommendations for the short, medium, and long term. This study's next step will be to determine how to implement the proposed interventions based on the availability of resources, funds, and governance patterns.

Keywords: urban lake system, co-evolution, rapid urbanization, complex system

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Authors: Carlo Cernicchiaro, Pedro D. Gaspar, Martim L. Aguiar

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The agricultural sector is becoming more critical than ever in view of the expected overpopulation of the Earth. The introduction of robotic solutions in this field is an increasingly researched topic to make the most of the Earth's resources, thus going to avoid the problems of wear and tear of the human body due to the harsh agricultural work, and open the possibility of a constant careful processing 24 hours a day. This project is realized for a terrestrial autonomous robot aimed to navigate in an orchard collecting fallen peaches below the trees. When it receives the signal indicating the low battery, it has to return to the docking station where it will replace its battery and then return to the last work point and resume its routine. Considering a preset path in orchards with tree rows with variable length by which the robot goes iteratively using the algorithm D*. In case of low battery, the D* algorithm is still used to determine the fastest return path to the docking station as well as to come back from the docking station to the last work point. MATLAB simulations were performed to analyze the flexibility and adaptability of the developed algorithm. The simulation results show an enormous potential for adaptability, particularly in view of the irregularity of orchard field, since it is not flat and undergoes modifications over time from fallen branch as well as from other obstacles and constraints. The D* algorithm determines the best route in spite of the irregularity of the terrain. Moreover, in this work, it will be shown a possible solution to improve the initial points tracking and reduce time between movements.

Keywords: path planning, fastest return path, agricultural autonomous terrestrial robot, docking station

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Authors: Nazlı Üstünes Demirhan

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International norms are subject to pressures of change through contestation during the course of their lifetimes. The nature of the contestation is one of the factors that are likely to have a determinative role in the direction of this change towards a stronger or weaker norm. This paper aims to understand the relation between the legality of contestation and the direction of change in norm strength. Based on a multidimensional norm strength conceptualization, it is hypothesized that use of legal logic and rhetoric of argumentation would have a positive influence for norm strength, whereas non-legal nature of contestation would lack this and weaken the norm. In order to show this, the evolution of the human protection norm between 1999 and 2018 will be examined with reference to two major contestation periods; Kosovo intervention of 1999, which led to the development of R2P doctrine, and Libya intervention of 2011, which is followed by the demise of the norm. The comparative analysis will be conducted through process tracing method with a document analysis on the Security Council meeting minutes, resolutions, and press releases. This study aims to contribute to the norm contestation literature with the introduction of legal process analysis. It also relates to further questions in IR/IL nexus, relating to the value added of norm legality as well as the politics of legalization.

Keywords: humanitarian intervention, legality, norm contestation, norm dynamics, norm strength, responsibility to protect

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Authors: V. Monisha, D. Sindhuja, M. Sowmiya

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Over the decade, the applications about multimedia have been developed rapidly. The advancement in the communication field at the faster pace, it is necessary to protect the data during transmission. Thus, security of multimedia contents becomes a vital issue, and it is a need for protecting the digital content against malfunctions. Digital watermarking becomes the solution for the copyright protection and authentication of data in the network. In multimedia applications, embedded watermarks should be robust, and imperceptible. For improving robustness, the discrete wavelet transform is used. Both encoding and extraction algorithm can be done using MATLAB R2012a. In this Discrete wavelet transform (DWT) domain of digital image, watermarking algorithm is used, and hardware implementation can be done on Xilinx based FPGA.

Keywords: digital watermarking, DWT, robustness, FPGA

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Authors: Chung-Nan Lee, Sheng-Wei Chu, You-Chiun Wang

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LTE (Long Term Evolution) employs the eMBMS (evolved Multimedia Broadcast/Multicast Service) protocol to deliver video streams to a multicast group of users. However, it requires all multicast members to receive a video stream in the same transmission rate, which would degrade the overall service quality when some users encounter bad channel conditions. To overcome this problem, this paper provides two efficient resource allocation schemes in such LTE network: The symbol synchronization (S2) scheme assumes that the macro and pico eNodeBs use the same frequency channel to deliver the video stream to all users. It then adopts a multicast transmission index to guarantee the fairness among users. On the other hand, the resource reuse (R2) scheme allows eNodeBs to transmit data on different frequency channels. Then, by introducing the concept of frequency reuse, it can further improve the overall service quality. Extensive simulation results show that the S2 and R2 schemes can respectively improve around 50% of fairness and 14% of video quality as compared with the common maximum throughput method.

Keywords: LTE networks, multicast, resource allocation, layered video

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Authors: Jiaqi Huang, Lu Jin

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Pervious concrete features with high water permeability rate. However, due to the lack of fine aggregates, the compressive strength is usually lower than other conventional concrete products. Optimization of pervious concrete mix design has long been recognized as an effective mechanism to achieve high compressive strength while maintaining desired permeability rate. In this paper, a Gibbs Sampling based algorithm is proposed to approximate the optimal mix design to achieve a high compressive strength of pervious concrete. We prove that the proposed algorithm efficiently converges to the set of global optimal solutions. The convergence rate and accuracy depend on a control parameter employed in the proposed algorithm. The simulation results show that, by using the proposed approach, the system converges to the optimal solution quickly and the derived optimal mix design achieves the maximum compressive strength while maintaining the desired permeability rate.

Keywords: convergence, Gibbs Sampling, high compressive strength, optimal mix design, pervious concrete

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Authors: Jamal Ayour, Mohamed Benichou

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Fruit quality is one of the main concerns of consumers, producers, and distributors. The evolution of apricot fruits undergoes a strong acceleration during maturation, and the rapidity of post-harvest evolution of the ripe fruit is particularly selective in the apricot. The objective of this study is to identify new cultivars with an interesting quality as well as a better yield allowing a more prolonged production over time. The evaluation of the fruit quality of new apricot cultivars from the Marrakech region was carried out by analyzing their physical and biochemical attributes during ripening. The results obtained clearly show a great diversity of the physicochemical attributes of the selected clones. Also, some genotypes of apricots showed contents of sugars, organic acids (vitamin C) and β carotene significantly higher than those of the most famous varieties of Morocco: Canino, Delpatriarca, and Maoui. Principal component analysis (PCA), taking into account the maturity stage and the diversity of cultivars, made it possible to define three groups with similar physicochemical attributes. The results of this study are of great use, particularly for the selection of genotypes with a better quality of fruit, both for consumption or industrial processing and with important contents of physicochemical attributes.

Keywords: apricot, acidity, carotenoids, color, sugar, quality, vitamin C

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Authors: Arfan Ali Nagra, Tariq Shahzad, Meshal Alharbi, Khalid Masood Khan, Muhammad Mugees Asif, Taher M. Ghazal, Khmaies Ouahada

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Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (DNA microarray) facilitates computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been to identify a number of genes in the cancer dataset. The classification algorithm contains ELM, K- centroid nearest neighbor (KCNN), and support vector machine (SVM) to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

Keywords: microarray cancer, improved PSO, ELM, SVM, evolutionary algorithms

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Authors: Adamu Abdullahi, On Francisca, Saidu Isah Rambo, G. N. Obunadike, D. T. Chinyio

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The paper discusses the potential threat of Denial of Service (DoS) attacks in the Internet of Things (IoT) networks on constrained application protocols (CoAP). As billions of IoT devices are expected to be connected to the internet in the coming years, the security of these devices is vulnerable to attacks, disrupting their functioning. This research aims to tackle this issue by applying mixed methods of qualitative and quantitative for feature selection, extraction, and cluster algorithms to detect DoS attacks in the Constrained Application Protocol (CoAP) using the Machine Learning Algorithm (MLA). The main objective of the research is to enhance the security scheme for CoAP in the IoT environment by analyzing the nature of DoS attacks and identifying a new set of features for detecting them in the IoT network environment. The aim is to demonstrate the effectiveness of the MLA in detecting DoS attacks and compare it with conventional intrusion detection systems for securing the CoAP in the IoT environment. Findings: The research identifies the appropriate node to detect DoS attacks in the IoT network environment and demonstrates how to detect the attacks through the MLA. The accuracy detection in both classification and network simulation environments shows that the k-means algorithm scored the highest percentage in the training and testing of the evaluation. The network simulation platform also achieved the highest percentage of 99.93% in overall accuracy. This work reviews conventional intrusion detection systems for securing the CoAP in the IoT environment. The DoS security issues associated with the CoAP are discussed.

Keywords: algorithm, CoAP, DoS, IoT, machine learning

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Authors: Ahmed Kamil Hasan Al-Ali, Bouchra Senadji, Ganesh Naik

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We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics of the speech signal. Channel effects are reduced using an intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) approach for classification. The proposed algorithm is evaluated by using an Australian forensic voice comparison database, combined with car, street and home noises from QUT-NOISE at a signal to noise ratio (SNR) ranging from -10 dB to 10 dB. Experimental results indicate that the MFCC feature warping-ICA achieves a reduction in equal error rate about (48.22%, 44.66%, and 50.07%) over using MFCC feature warping when the test speech signals are corrupted with random sessions of street, car, and home noises at -10 dB SNR.

Keywords: noisy forensic speaker verification, ICA algorithm, MFCC, MFCC feature warping

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Abdullah Beyazkurk, Onur Ozdemir

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The evolution of warfare has been affected from technological developments to a large extent. Another important factor that affected the evolution of warfare is the space. Technological developments became cornerstones for the organization of the forces on the field, while space of the battlefield gained importance with the introduction of urban areas as 'battlefields'. The use of urban areas as battlefields increased the casualty, while technological developments began to play a remedial role. Thus, the unmanned systems drew attention as the remedy. Today's widely used unmanned aerial vehicles have great effects on the operations. On the other hand, with the increasing urbanization, and the wide use of urban areas as battlefields make it a necessity to benefit from unmanned systems on the ground as well. This study focuses on the use of unmanned aerial systems as well as unmanned ground systems in urban warfare, with regards to their performance and cost affectivity. The study defends that the use of unmanned vehicles will be remedial for increasing casualty rates, while their precision and superhuman capacity will manifest the performance advantage. The findings of this study will help modern armies focus on unmanned systems, especially for the urban, anti-terror, or counter insurgency operations.

Keywords: technology, warfare, urban warfare, unmanned systems, unmanned ground vehicles, unmanned aerial vehicles

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Authors: Jonathan Heng, Yoong Cheah Huei

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A strategic model that does not trigger any false alarms to detect anomalies in Secure Water Treatment (SWaT) test bed is presented. This model uses machine learning invariants formulated from streamlining the general form of Auto-Regressive models with eXogenous input. A creative generalized CUSUM algorithm to integrate the invariants and the detection strategy technique is successfully developed and tested in the SWaT Programmable Logic Controllers (PLCs). Three steps to fine-tune parameters, b and τ in the generalized algorithm are stated and an example used to demonstrate the tuning process is discussed. This approach can swiftly and effectively detect various scopes of cyber-attacks such as multiple points single stage and multiple points multiple stages in SWaT. This technique can be applied in water treatment plants and other cyber physical systems like power and gas plants too.

Keywords: machine learning invariants, generalized CUSUM algorithm with invariants and detection strategy, scope of cyber attacks, strategic model, tuning parameters

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Authors: Shreeya Umashankar

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The Supreme Court of India has emerged as the most powerful organ of State and amongst the foremost constitutional courts in the world through the instrument of Public Interest Litigation (PIL), the exercise of writ jurisdiction and the expansive interpretation of fundamental rights guaranteed by the Constitution of India. Judicial activism impinging on every facet of governance has become the norm in recent times. This paper traces the evolution of judicial activism since Independence through pronouncements of the Supreme Court. It brings out distinct phases in this evolution– the initial phase of judicial restraint, the first phase of an activist judiciary where the Supreme Court primarily was concerned with protection of fundamental rights and humane treatment of citizens; the second phase where the Supreme Court took keen interest in preservation and protection of the environment; the third phase where the Supreme Court extended its reach into the socio-economic arena and the fourth phase when issues of transparency and probity in governance led to interventions by the Supreme Court. The paper illustrates through judgements of the Supreme Court that the instrument of the PIL and the exercise of writ jurisdiction by the Supreme Court go beyond the traditional postulates of judicial processes and political theory on separation of powers between the organs of State.

Keywords: fundamental rights, judicial activism, public interest litigation, Supreme Court of India

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Authors: Hatem Hajri, Mohamed-Cherif Rahal

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Both Lidars and Radars are sensors for obstacle detection. While Lidars are very accurate on obstacles positions and less accurate on their velocities, Radars are more precise on obstacles velocities and less precise on their positions. Sensor fusion between Lidar and Radar aims at improving obstacle detection using advantages of the two sensors. The present paper proposes a real-time Lidar/Radar data fusion algorithm for obstacle detection and tracking based on the global nearest neighbour standard filter (GNN). This algorithm is implemented and embedded in an automative vehicle as a component generated by a real-time multisensor software. The benefits of data fusion comparing with the use of a single sensor are illustrated through several tracking scenarios (on a highway and on a bend) and using real-time kinematic sensors mounted on the ego and tracked vehicles as a ground truth.

Keywords: ground truth, Hungarian algorithm, lidar Radar data fusion, global nearest neighbor filter

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Authors: Dmitry Yu. Korulkin, Raissa A. Muzychkina

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In review the generalized data about biosynthetic routs formation anthraquinone molecules in natural cells. The basic possibilities of various ways of biosynthesis of different quinoid substances are shown.

Keywords: anthraquinones, biochemical evolution, biosynthesis, metabolism

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Authors: Terence Soule, Tami Al Ghamdi

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To study how the partial knowledge transfer may affect the Genetic Algorithm (GA) performance, we model the Transfer Learning (TL) process using GA as the model solver. The objective of the TL is to transfer the knowledge from one problem to another related problem. This process imitates how humans think in their daily life. In this paper, we proposed to study a case where the knowledge transferred from the S problem has less information than what the T problem needs. We sampled the transferred population using different strategies of TL. The results showed transfer part of the knowledge is helpful and speeds the GA process of finding a solution to the problem.

Keywords: transfer learning, partial transfer, evolutionary computation, genetic algorithm

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Authors: Caio Marcelo Nunes, Anderson da Silva Soares, Gustavo Teodoro Laureano, Clarimar Jose Coelho

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This paper presents a methodology to pixel recognition in aerial images using fuzzy $c$-means algorithm. This algorithm is a alternative to recognize areas considering uncertainties and inaccuracies. Traditional clustering technics are used in recognizing of multispectral images of earth's surface. This technics recognize well-defined borders that can be easily discretized. However, in the real world there are many areas with uncertainties and inaccuracies which can be mapped by clustering algorithms that use fuzzy sets. The methodology presents in this work is applied to multispectral images obtained from Landsat-5/TM satellite. The pixels are joined using the $c$-means algorithm. After, a classification process identify the types of surface according the patterns obtained from spectral response of image surface. The classes considered are, exposed soil, moist soil, vegetation, turbid water and clean water. The results obtained shows that the fuzzy clustering identify the real type of the earth's surface.

Keywords: aerial images, fuzzy clustering, image processing, pattern recognition

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

Procedia PDF Downloads 468

Authors: Hsiao-Yung Wang

Abstract:

This essay aims to examine how the transgender imagery has been constructed in the Taiwanese news media and its evolution from 1953 to 2016. It also explores the discourse patterns and rhetorical strategies in the transgender-related issues which contributed to levels of evaluation in forming ‘social deviance.’ Samples for analysis were selected from mainstream newspapers, including China Times, United Daily and Apple Daily. The time frame for sample selection is from August 1953 (when the first transgender case was reported in Taiwan) to June 2016. To enhance understanding of media representation as nominalistic-based, the author refers to the representative of critical rhetoric Raymie McKerrow for his study on remembrance and forgetfulness in public discourse (especially in his model of ‘critique of domination’); thereby categorizing the 64 years of transgender discourse into five periods: (1) transgender as ‘intersex’ of surgical-reparative medical treatment; (2) transgender as ‘freak gender-bender’ with criminal behaviors; (3) transgender as ‘ladyboy’ (‘katoey in a Thai term) of bar girls or sex workers; (4) transgender as ‘cross dresser’ of transvestite performance; and (5) transgender as ‘life-style or human right’ of spontaneous gender identification. Based on the research findings, this essay argues that the characterization of transgender reporting as a site for the production of compulsory sexism and gender stereotype by the specific forms of name-calling. Besides, the evolution of word-image addressing to transgender issues also pinpoints media as a reflection of fashion of the day. While the transgender imagery might be crystallized as ‘still social problems’ or ‘gender transgression’ in insidious rhetoric; and while the so-called ‘phobia’ persistently embodies in media discourse to exercise name-calling in an ambiguous (rather than in a bullying) way or under the cover of humanist-liberalist rationales, these emergent rhetorical dilemma should be resolved without any delay.

Keywords: critical rhetoric, media representation, McKerrow, nominalistic, social deviance, transgender

Procedia PDF Downloads 298

Authors: F. Ismael Roman Moreno, Francisca Alba Sanchez

Abstract:

Over time, the plant landscape has changed as a result of the numerous events on a global and local scale that have happened. This is the case of the Mediterranean ecosystems, one of the most complex and rich in endemisms on the planet, subjected to anthropic pressures from the beginning of civilizations. The intervention in these systems together with climate changes has led to changes in diversity, tree cover, shrub, and ultimately in the structure and functioning of these ecosystems. Paleopalinology is used as a tool for analysis of pollen and non-pollen microfossils preserved in the flooded grasslands of the Middle Atlas (Morocco). This allows reconstructing the evolution of vegetation and climate, as well as providing data and reasoning to different ecological, cultural and historical processes. Although climatic and anthropic events are well documented in Europe, they are not so well documented in North Africa, which gives added value to the study area. The results obtained serve to predict the behavior and evolution of Mediterranean mountain ecosystems during the Holocene, their response to future changes, resilience, and recovery from climatic and anthropic disturbances. In the stratigraphic series analyzed, nine major events were detected, eight of which appeared to be of climatic and anthropic origin, and one unexpected, related to volcanic activity.

Keywords: anthropic, Holocene, Morocco, paleopalynology, resilience

Procedia PDF Downloads 148