Search results for: algorithms and data structure

Authors: Ousmane Youme, Jean Marie Dembele, Eugene Ezin, Christophe Cambier

Abstract:

In recent years, the convolutional neural networks (CNN) architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on a genetic algorithm to find the best individual in the optimal space. Our algorithms drastically reduce resource costs and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition.

Keywords: CNN architecture, genetic algorithm, evolution algorithm, length constraints

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Authors: Seema Vaidya

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Mining association rule is a key issue in data mining. In any case, the standard models ignore the distinction among the exchanges, and the weighted association rule mining does not transform on databases with just binary attributes. This paper proposes a novel continuous example and executes a tree (FP-tree) structure, which is an increased prefix-tree structure for securing compacted, discriminating data about examples, and makes a fit FP-tree-based mining system, FP enhanced capacity algorithm is used, for mining the complete game plan of examples by illustration incessant development. Here, this paper handles the motivation behind making remarkable and weighted item sets, i.e. rare weighted item set mining issue. The two novel brightness measures are proposed for figuring the infrequent weighted item set mining issue. Also, the algorithm are handled which perform IWI which is more insignificant IWI mining. Moreover we utilized the rare item set for choice based structure. The general issue of the start of reliable definite rules is troublesome for the grounds that hypothetically no inciting technique with no other person can promise the rightness of influenced theories. In this way, this framework expects the disorder with the uncommon signs. Usage study demonstrates that proposed algorithm upgrades the structure which is successful and versatile for mining both long and short diagnostics rules. Structure upgrades aftereffects of foreseeing rare diseases of patient.

Keywords: association rule, data mining, IWI mining, infrequent item set, frequent pattern growth

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Authors: Toral Khalpada, Kanhai Joshi

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Generally, constructions of structures built in India are indeterminate structures. The purpose of this study is to investigate the application of a structure that is proved to be non-economical. The testing practice involves the application of different types of loads on both, determinate and indeterminate structure by computing it on a software system named Staad and also inspecting them practically on the construction site, analyzing the most efficient structure and diagnosing the utilization of the structure which is not so beneficial as compared to other. Redundant structures (indeterminate structure) are found to be more reasonable. All types of loads were applied on the beams of both determinate and indeterminate structures parallelly on the software and the same was done on the site practically which proved that maximum stresses in statically indeterminate structures are generally lower than those in comparable determinate structures. These structures are found to have higher stiffness resulting in lesser deformations so indeterminate structures are economical and are better than determinate structures to use for construction. On the other hand, statically determinate structures have the benefit of not producing stresses because of temperature changes. Therefore, our study tells that indeterminate structure is more beneficial but determinate structure also has used as it can be used in many areas; it can be used for the construction of two hinged arch bridges where two supports are sufficient and where there is no need for expensive indeterminate structure. Further investigation is needed to contrive more implementation of the determinate structure.

Keywords: construction, determinate structure, indeterminate structure, stress

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Authors: Arka Roy, Chandra Prakash Dubey

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Inverse problem generally used for recovering hidden information from outside available data. Vertical component of gravity field we will be going to use for underneath density structure calculation. Ill-posing nature is main obstacle for any inverse problem. Linear regularization using Tikhonov formulation are used for appropriate choice of SVD and GSVD components. For real time data handle, signal to noise ratios should have to be less for reliable solution. In our study, 2D and 3D synthetic model with rectangular grid are used for gravity field calculation and its corresponding inversion for density reconstruction. Fine grid also we have considered to hold any irregular structure. Keeping in mind of algebraic ambiguity factor number of observation point should be more than that of number of data point. Picard plot is represented here for choosing appropriate or main controlling Eigenvalues for a regularized solution. Another important study is depth resolution plot (DRP). DRP are generally used for studying how the inversion is influenced by regularizing or discretizing. Our further study involves real time gravity data inversion of Vredeforte Dome South Africa. We apply our method to this data. The results include density structure is in good agreement with known formation in that region, which puts an additional support of our method.

Keywords: depth resolution plot, gravity inversion, Picard plot, SVD, Tikhonov formulation

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Authors: D. López-Mancilla, J. M. Roblero-Villa

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Since 1990, the research on chaotic dynamics has received considerable attention, particularly in light of potential applications of this phenomenon in secure communications. Data encryption using chaotic systems was reported in the 90's as a new approach for signal encoding that differs from the conventional methods that use numerical algorithms as the encryption key. The algorithms for image encryption have received a lot of attention because of the need to find security on image transmission in real time over the internet and wireless networks. Known algorithms for image encryption, like the standard of data encryption (DES), have the drawback of low level of efficiency when the image is large. The encrypting based on chaos proposes a new and efficient way to get a fast and highly secure image encryption. In this work, a user interface for image encryption and a novel and easiest way to encrypt images using chaos are presented. The main idea is to reshape any image into a n-dimensional vector and combine it with vector extracted from a chaotic system, in such a way that the vector image can be hidden within the chaotic vector. Once this is done, an array is formed with the original dimensions of the image and turns again. An analysis of the security of encryption from the images using statistical analysis is made and is used a stage of optimization for image encryption security and, at the same time, the image can be accurately recovered. The user interface uses the algorithms designed for the encryption of images, allowing you to read an image from the hard drive or another external device. The user interface, encrypt the image allowing three modes of encryption. These modes are given by three different chaotic systems that the user can choose. Once encrypted image, is possible to observe the safety analysis and save it on the hard disk. The main results of this study show that this simple method of encryption, using the optimization stage, allows an encryption security, competitive with complicated encryption methods used in other works. In addition, the user interface allows encrypting image with chaos, and to submit it through any public communication channel, including internet.

Keywords: image encryption, chaos, secure communications, user interface

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Authors: Halima Adel Halim Shnishah, David Mulvaney

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Applying traditional symmetric cryptography algorithms while computing encryption and decryption provides immunity to secret keys against different attacks. One of the popular techniques generating automated secret keys is evolutionary computing by using Eureqa API tool, which got attention in 2013. In this paper, we are generating automated secret keys for image encryption and decryption using Eureqa API (tool which is used in evolutionary computing technique). Eureqa API models pseudo-random input data obtained from a suitable source to generate secret keys. The validation of generated secret keys is investigated by performing various statistical tests (histogram, chi-square, correlation of two adjacent pixels, correlation between original and encrypted images, entropy and key sensitivity). Experimental results obtained from methods including histogram analysis, correlation coefficient, entropy and key sensitivity, show that the proposed image encryption algorithms are secure and reliable, with the potential to be adapted for secure image communication applications.

Keywords: image encryption algorithms, Eureqa, statistical measurements, automated key generation

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Authors: M. Chumburidze, N. Salia, T. Namchevadze

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This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods.

Keywords: credit network, case portfolio, binary tree, priority queue, stack

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Authors: Walid Cherif

Abstract:

Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: F. Mohammadsadeghi

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Relevance of research: Axial compressors are used in both aircraft engine construction and ground-based gas turbine engines. The compressor is considered to be one of the main gas turbine engine units, which define absolute and relative indicators of engine in general. Failure of compressor often leads to drastic consequences. Therefore, safe (stable) operation must be maintained when using axial compressor. Currently, we can observe a tendency of increase of power unit, productivity, circumferential velocity and compression ratio of axial compressors in gas turbine engines of aircraft and ground-based application whereas metal consumption of their structure tends to fall. This causes the increase of dynamic loads as well as danger of damage of high load compressor or engine structure elements in general due to transient processes. In operating practices of aeronautical engineering and ground units with gas turbine drive the operational stability failure of gas turbine engines is one of relatively often failure causes what can lead to emergency situations. Surge occurrence is considered to be an absolute buckling failure. This is one of the most dangerous and often occurring types of instability. However detailed were the researches of this phenomenon the development of measures for surge before-the-fact prevention is still relevant. This is why the research of transient processes for axial compressors is necessary in order to provide efficient, stable and secure operation. The paper addresses the problem of automatic control system improvement by integrating the anti-surge algorithms for axial compressor of aircraft gas turbine engine. Paper considers dynamic exhaustion of gas dynamic stability of compressor stage, results of numerical simulation of airflow flowing through the airfoil at design and stalling modes, experimental researches to form the criteria that identify the compressor state at pre-surge mode detection. Authors formulated basic ways for developing surge preventing systems, i.e. forming the algorithms that allow detecting the surge origination and the systems that implement the proposed algorithms.

Keywords: axial compressor, rotation stall, Surg, unstable operation of gas turbine engine

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Authors: M. Bahari Mehrabani, Hua-Peng Chen

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Management and maintenance of coastal defence structures during the expected life cycle have become a real challenge for decision makers and engineers. Accurate evaluation of the current condition and future performance of flood defence structures is essential for effective practical maintenance strategies on the basis of available field inspection data. Moreover, as coastal defence structures age, it becomes more challenging to implement maintenance and management plans to avoid structural failure. Therefore, condition inspection data are essential for assessing damage and forecasting deterioration of ageing flood defence structures in order to keep the structures in an acceptable condition. The inspection data for flood defence structures are often collected using discrete visual condition rating schemes. In order to evaluate future condition of the structure, a probabilistic deterioration model needs to be utilised. However, existing deterioration models may not provide a reliable prediction of performance deterioration for a long period due to uncertainties. To tackle the limitation, a time-dependent condition-based model associated with a transition probability needs to be developed on the basis of condition grade scheme for flood defences. This paper presents a probabilistic method for predicting future performance deterioration of coastal flood defence structures based on condition grading inspection data and deterioration curves estimated by expert judgement. In condition-based deterioration modelling, the main task is to estimate transition probability matrices. The deterioration process of the structure related to the transition states is modelled according to Markov chain process, and a reliability-based approach is used to estimate the probability of structural failure. Visual inspection data according to the United Kingdom Condition Assessment Manual are used to obtain the initial condition grade curve of the coastal flood defences. The initial curves then modified in order to develop transition probabilities through non-linear regression based optimisation algorithms. The Monte Carlo simulations are then used to evaluate the future performance of the structure on the basis of the estimated transition probabilities. Finally, a case study is given to demonstrate the applicability of the proposed method under no-maintenance and medium-maintenance scenarios. Results show that the proposed method can provide an effective predictive model for various situations in terms of available condition grading data. The proposed model also provides useful information on time-dependent probability of failure in coastal flood defences.

Keywords: condition grading, flood defense, performance assessment, stochastic deterioration modelling

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Authors: Yassir AbdelRazig

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Evolutionary optimization methods such as genetic algorithms have been used extensively for the construction site layout problem. More recently, ant colony optimization algorithms, which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to benchmark combinatorial optimization problems. This paper proposes a formulation of the site layout problem in terms of a sequencing problem that is suitable for solution using an ant colony optimization algorithm. In the construction industry, site layout is a very important planning problem. The objective of site layout is to position temporary facilities both geographically and at the correct time such that the construction work can be performed satisfactorily with minimal costs and improved safety and working environment. During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the construction site layout problem. This paper proposes an ant colony optimization model for construction site layout. A simple case study for a highway project is utilized to illustrate the application of the model.

Keywords: ant colony, construction site layout, optimization, genetic algorithms

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Authors: Mazouz Halima, Belghachi Abdrahmane

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Effects of electron irradiation-induced deep level defects have been studied on both n/p and p/n indium phosphide solar cells with very thin emitters. The simulation results show that n/p structure offers a somewhat better short circuit current but the p/n structure offers improved circuit voltage, not only before electron irradiation, but also after 1MeV electron irradiation with 5.1015 fluence. The simulation also shows that n/p solar cell structure is more resistant than that of p/n structure.

Keywords: InP solar cell, p/n and n/p structure, electron irradiation, output parameters

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Authors: Elsheikh Asser

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Time and cost are the main goals of the construction project management. The first schedule developed may not be a suitable schedule for beginning or completing the project to achieve the target completion time at a minimum total cost. In general, there are trade-offs between time and cost (TCT) to complete the activities of a project. This research presents genetic algorithms (GAs) multi-objective model for project scheduling considering different scenarios such as least cost, least time, and target time.

Keywords: genetic algorithms, time-cost trade-off, multi-objective model, project scheduling

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: K. Khanafer

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The aim of this investigation was to validate the fluid-structure interaction (FSI) model of type B aortic dissection with our experimental results from a bench-top-model. Another objective was to study the relationship between the size of a septectomy that increases the outflow of the false lumen and its effect on the values of the differential of pressure between true lumen and false lumen. FSI analysis based on Galerkin’s formulation was used in this investigation to study flow pattern and hemodynamics within a flexible type B aortic dissection model using boundary conditions from our experimental data. The numerical results of our model were verified against the experimental data for various tear size and location. Thus, CFD tools have a potential role in evaluating different scenarios and aortic dissection configurations.

Keywords: aortic dissection, fluid-structure interaction, in vitro model, numerical

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Authors: Chih Hsing Lin, Wen-Ching Chen, Ssu-Ying Chen, Chih-Chyau Yang, Chien-Ming Wu, Chun-Ming Huang

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Critical structures such as buildings, bridges and dams require periodic inspections to ensure safe operation. The reliable inspection of structures can be achieved by combing temperature sensor and accelerometers. In this work, we propose a building structure health monitoring device (BSHMD) with using three 1-axis accelerometers, gateway, analog to digital converter (ADC), and data logger to monitoring the building structure. The proposed BSHMD achieves the features of low cost by using three 1-axis accelerometers with the data synchronization problem being solved, and easily installation and removal. Furthermore, we develop a packet acquisition program to receive the sensed data and then classify it based on time and date. Compared with 3-axis accelerometer, our proposed 1-axis accelerometers based device achieves 64.3% cost saving. Compared with previous structural monitoring device, the BSHMD achieves 89% area saving. Therefore, with using the proposed device, the realtime diagnosis system for building damage monitoring can be conducted effectively.

Keywords: building structure health monitoring, cost effective, 1-axis accelerometers, real-time diagnosis

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Muhammad Rehan Usman, Muhammad Arslan Usman, Soo Young Shin

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The new era of digital communication has brought up many challenges that network operators need to overcome. The high demand of mobile data rates require improved networks, which is a challenge for the operators in terms of maintaining the quality of experience (QoE) for their consumers. In live video transmission, there is a sheer need for live surveillance of the videos in order to maintain the quality of the network. For this purpose objective algorithms are employed to monitor the quality of the videos that are transmitted over a network. In order to test these objective algorithms, subjective quality assessment of the streamed videos is required, as the human eye is the best source of perceptual assessment. In this paper we have conducted subjective evaluation of videos with varying spatial and temporal impairments. These videos were impaired with frame freezing distortions so that the impact of frame freezing on the quality of experience could be studied. We present subjective Mean Opinion Score (MOS) for these videos that can be used for fine tuning the objective algorithms for video quality assessment.

Keywords: frame freezing, mean opinion score, objective assessment, subjective evaluation

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Authors: Yakubu Bala Mohammed, Nadire Chavus, Mohammed Bulama

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Generative Pre-trained Transformer (ChatGPT) represents an artificial intelligence (AI) tool capable of swiftly generating comprehensive responses to prompts and follow-up inquiries. This emerging AI tool was introduced in November 2022 by OpenAI firm, an American AI research laboratory, utilizing substantial language models. This present study aims to delve into the potential future consequences of ChatGPT usage in education using AI-based algorithms. The paper will bring forth the likely potential risks of ChatGBT utilization, such as academic integrity concerns, unfair learning assessments, excessive reliance on AI, and dissemination of inaccurate information using four machine learning algorithms: eXtreme-Gradient Boosting (XGBoost), Support vector machine (SVM), Emotional artificial neural network (EANN), and Random forest (RF) would be used to analyze the study collected data due to their robustness. Finally, the findings of the study will assist education stakeholders in understanding the future implications of ChatGPT usage in education and propose solutions and directions for upcoming studies.

Keywords: machine learning, ChatGPT, education, learning, implications

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Authors: Fatih Sulak, Betül A. Özdemir, Beyza Bozdemir

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In order to improve symmetric key research, several competitions had been arranged by organizations like National Institute of Standards and Technology (NIST) and International Association for Cryptologic Research (IACR). In recent years, the importance of authenticated encryption has rapidly increased because of the necessity of simultaneously enabling integrity, confidentiality and authenticity. Therefore, at January 2013, IACR announced the Competition for Authenticated Encryption: Security, Applicability, and Robustness (CAESAR Competition) which will select secure and efficient algorithms for authenticated encryption. Cryptographic algorithms are anticipated to behave like random mappings; hence, it is important to apply statistical randomness tests to the outputs of the algorithms. In this work, the statistical randomness tests in the NIST Test Suite and the other recently designed randomness tests are applied to six second round algorithms of the CAESAR Competition. It is observed that AEGIS achieves randomness after 3 rounds, Ascon permutation function achieves randomness after 1 round, Joltik encryption function achieves randomness after 9 rounds, Morus state update function achieves randomness after 3 rounds, Pi-cipher achieves randomness after 1 round, and Tiaoxin achieves randomness after 1 round.

Keywords: authenticated encryption, CAESAR competition, NIST test suite, statistical randomness tests

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Authors: Joanna Dauner, Jan Friedrich, Linda Elsner, Kora Kimpel

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Active materials such as Electroactive Polymers (EAPs) are promising for the development of novel shape-changing interfaces. This paper explores the potential of EAPs in a multilayer unimorph structure from a design perspective to investigate the visual qualities of the material for dynamic data visualization and data physicalization. We discuss various concepts of how the material can be used for this purpose. Multilayer unimorph EAPs are of particular interest to designers because they can be easily prototyped using everyday materials and tools. By changing the structure and geometry of the EAPs, their movement and behavior can be modified. We present the results of our preliminary user testing, where we evaluated different movement patterns. As a result, we introduce a prototype display built with EAPs for dynamic data physicalization. Finally, we discuss the potentials and drawbacks and identify further open research questions for the design discipline.

Keywords: electroactive polymer, shape-changing interfaces, smart material interfaces, data physicalization

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Authors: Zahid Ahmad, Samia Yousaf

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The main purpose of this study is to evaluate and assess the financial position, operating performance, and recent outlook of the companies. This study investigates the impact of capital structure, dividend policy and sustainability on the value of firms of textile spinning sector of Pakistan which is listed on Pakistan stock exchange. The panel data technique has been applied to this group of textile sector which is textile spinning. This study covers the last ten years of time period. All the data related to the variables have been collected from the annual reports and financial statements of the textile sector firms. There are differently related determinants to measure the capital structure which are fixed assets turnover ratio, debt ratio, equity ratio, debt to equity ratio, assets tangibility, and shareholder’s equity. Dividend policy is being measured by two determinants which are earning per share (EPS) and dividend payout ratio. Sustainability is being measured by three suitable factors which are sales growth, gross profit margin ratio and firm size. These are three independent variables and their determinants of this study. Value of firm is measured through the return on asset (ROA). Capital structure is at the top of the list among all the three variables. According to the results of this research work, somewhere all the three variables generates positive and significant effect on the firm’s performance and its growth.

Keywords: capital structure, dividend policy, panel data, sustainability

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Authors: Gee-Cheol Kim, Joo-Won Kang

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Structural characteristics of spatial structure are different from that of rahmen structures and it has many factors that are unpredictable experientially. Both horizontal and vertical earthquake should be considered because of seismic behaviour characteristics of spatial structures. This experimental study is conducted about seismic response characteristics of roof structure according to the effect of columns or walls, through scale model of arch structure that has the basic dynamic characteristics of spatial structure. Though remarkable response is not occurred for horizontal direction in the region of higher frequency than the region of frequency that seismic energy is concentrated, relatively large response is occurred in vertical direction. It is proved that seismic response of arch structure with column is varied according to property of column.

Keywords: arch structure, seismic response, shaking table, spatial structure

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, production process optimization, evolutionary algorithms, policy optimization, actor critic approach

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Authors: Kostas Metaxiotis, Kostas Liagkouras

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This study presents an Expert System specially designed to be used with Multiobjective Evolutionary Algorithms (MOEAs) for the solution of the portfolio selection problem. The validation of the proposed hybrid System is done by using data sets from Hang Seng 31 in Hong Kong, DAX 100 in Germany and FTSE 100 in UK. The performance of the proposed system is assessed in comparison with the Non-dominated Sorting Genetic Algorithm II (NSGAII). The evaluation of the performance is based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it. The results show that the proposed hybrid system is efficient for the solution of this kind of problems.

Keywords: expert systems, multi-objective optimization, evolutionary algorithms, portfolio selection

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: M. R. Bekli, N. Mebarki, I. Chadou

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In this study, we focus on the structure functions and violation of scale invariance in the context of non-commutative standard model (NCSM). We find that this violation appears in the first order of perturbation theory and a non-commutative version of the DGLAP evolution equation is deduced. Numerical analysis and comparison with experimental data imposes a new bound on the non-commutative parameter.

Keywords: NCSM, structure function, DGLAP equation, standard model

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

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Authors: Nathasa Mazna Ramli, Nurul Husna Mohd Salleh, Nurul Aini Muhamed

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This study focuses on the governance of an Islamic philanthropic institution in Malaysia. Specifically, the internal governance structure of corporate Islamic endowment, or waqf, is being analysed. The purposes of waqf are to provide continuous charity that could generate perpetual income flow for the needy. This study is based on the principle of MCCG 2012, Shariah Governance Framework and charity governance. This study utilises publicly available data to examine the internal governance structure of a corporate waqf. This study finds that the Islamic philanthropic Institution practices, to some extent, have a sound governance structure to discharge their transparency and accountability. Furthermore, findings also showed that though governance structure is in place, most of the structures are not disclosed in the annual reports of the company. Findings from the study could extend the knowledge in these areas and stimulate further research on the governance of Islamic philanthropic institutions, particularly for corporate waqf.

Keywords: accountability, governance, Islamic philanthropic, corporate waqf

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Authors: Abdelghani Alidra, Mohamed Tahar Kimour

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Genetic algorithm must adapt themselves at design time to cope with the search problem specific requirements and at runtime to balance exploration and convergence objectives. In a previous article, we have shown that modeling and implementing Genetic Algorithms (GA) using the software product line (SPL) paradigm is very appreciable because they constitute a product family sharing a common base of code. In the present article we propose to extend the use of the feature model of the genetic algorithms family to model the potential states of the GA in what is called a Dynamic Software Product Line. The objective of this paper is the systematic generation of a reconfigurable architecture that supports the dynamic of the GA and which is easily deduced from the feature model. The resultant GA is able to perform dynamic reconfiguration autonomously to fasten the convergence process while producing better solutions. Another important advantage of our approach is the exploitation of recent advances in the domain of dynamic SPLs to enhance the performance of the GAs.

Keywords: self-adaptive genetic algorithms, software engineering, dynamic software product lines, reconfigurable architecture

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