Search results for: elliptic curve digital signature algorithm

Authors: Mohammed Amraouy, Mostafa Bellafkih, Abdellah Bennane, Aziza Benomar

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Distance education has become popular due to their ability to provide learning from almost anywhere and anytime. COVID-19 forced educational institutions to urgently introduce distance education to ensure pedagogical continuity, so all stakeholders were invited to adapt to this new paradigm. In order to identify strengths and weaknesses, the research focuses on the need to create an effective mechanism for evaluating distance education. The aims of this research were to explore and evaluate the use of digital media in general and official platforms in particular in distance education practices. To this end, we have developed and validated a questionnaire before administering it to a sample of 431 teachers in Morocco. Teachers reported lower knowledge and skills in the didactic use of ICT in the distance education process. In addition, although age and educative experience of the teachers continue to modulate the level of instrumental skills. Therefore, resources (digital resources and infrastructure) and the teachers’ ICT training present serious limitations, which require a training more focused on the distance educational paradigm and educational environments that allow teachers to create educational activities able to promote and facilitate the distance learning process.

Keywords: distance education, e-learning, teachers’ perceptions, assessment

Procedia PDF Downloads 137

Authors: Do-Jin Jang, Sung-Ah Kim

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A kinetic façade responds to user requirements and environmental conditions.  In designing a kinetic façade, kinetic patterns play a key role in determining its performance. This paper proposes a biomimetic method for the multi-objective optimization for kinetic façade design. The autonomous decentralized control system is combined with flocking algorithm. The flocking agents are autonomously reacting to sensor values and bring about kinetic patterns changing over time. A series of experiments were conducted to verify the potential and limitations of the flocking based decentralized control. As a result, it could show the highest performance balancing multiple objectives such as solar radiation and openness among the comparison group.

Keywords: biomimicry, flocking algorithm, autonomous decentralized control, multi-objective optimization

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Authors: Jing Zhao, Yoshiharu Ishikawa, Chuan Xiao, Kento Sugiura

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In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data.

Keywords: simulation data, data summarization, spatial histograms, exploration, visualization

Procedia PDF Downloads 177

Authors: Ghada Badr, Manar Hosny, Nuha Bintayyash, Eman Albilali, Souad Larabi Marie-Sainte

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The median problem is significantly applied to derive the most reasonable rearrangement phylogenetic tree for many species. More specifically, the problem is concerned with finding a permutation that minimizes the sum of distances between itself and a set of three signed permutations. Genomes with equal number of genes but different order can be represented as permutations. In this paper, an algorithm, namely BeamGA median, is proposed that combines a heuristic search approach (local beam) as an initialization step to generate a number of solutions, and then a Genetic Algorithm (GA) is applied in order to refine the solutions, aiming to achieve a better median with the smallest possible reversal distance from the three original permutations. In this approach, any genome rearrangement distance can be applied. In this paper, we use the reversal distance. To the best of our knowledge, the proposed approach was not applied before for solving the median problem. Our approach considers true biological evolution scenario by applying the concept of common intervals during the GA optimization process. This allows us to imitate a true biological behavior and enhance genetic approach time convergence. We were able to handle permutations with a large number of genes, within an acceptable time performance and with same or better accuracy as compared to existing algorithms.

Keywords: median problem, phylogenetic tree, permutation, genetic algorithm, beam search, genome rearrangement distance

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Authors: Michael Anthony Doherty

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Modern Methods of Construction (MMC) is a significant topic in the construction industry; while reviewing (MMC) over different fields that are significant in the modern construction world, the following areas were assessed where (MMC) is developing, supply chain management, automation, digital technology, and new construction technologies. Different methods were considered as an approach to research and exploring areas highlighted within the construction industry that are making advancements using Modern Methods of Construction Methods and Technologies (MCMTs). The research was conducted using the following methodologies, literature review of academic sources, primary and secondary data sources, questionaries, and interviews. The paper is composed of two parts, firstly a literature review and secondly a questionnaire used as the basis for interviews were utilised to achieve the following key objectives: to identify (MCMTs) being implemented in the construction industry, research and compile information with regards to these methods, determine their purpose and their application in the industry, establishing what (MCMTs) are being used in the industry while also determining the success of the methods. The research considers the evolution and development of these methods in projects and within the industry itself. Major findings were as follows; automation technologies such as robotics, offsite fabrication utilising automated production lines are increasingly part of project execution, digital technologies such as AR and VR are increasingly utilised in project co-ordination, (MMCTs) are proving to be a solution to the construction industry problems such as a lack of skilled workforce, hazardous work tasks, and situations, new construction technologies are available and finding their place in mainstream construction, (SCM) and (GSCM) are evolving to new levels using new systems and technologies such as block chain technology as well as Company Size and Project size influence the use of (MMCTs) and the adoption of (MMCTS). In summary the paper endeavours to identify and detail how areas of (MMCTs) are developing and are gaining traction within mainstream construction.

Keywords: automation, digital technology, new construction technologies, supply chain management

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Authors: Jean Berger, Mohamed Barkaoui

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Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly incorporating false alarm sensor readings, to solve a single agent military logistics search-and-delivery path planning problem with anticipated feedback is presented. The decision model consists in minimizing expected entropy considering anticipated possible observation outcomes over a given time horizon. The model captures uncertainty associated with observation events for all possible scenarios. Entropy represents a measure of uncertainty about the searched target location. Feedback information resulting from possible sensor observations outcomes along the projected path plan is exploited to update anticipated unit target occupancy beliefs. For the first time, a compact belief update formulation is generalized to explicitly include false positive observation events that may occur during plan execution. A novel genetic algorithm is then proposed to efficiently solve search path planning, providing near-optimal solutions for practical realistic problem instances. Given the run-time performance of the algorithm, natural extension to a closed-loop environment to progressively integrate real visit outcomes on a rolling time horizon can be easily envisioned. Computational results show the value of the approach in comparison to alternate heuristics.

Keywords: search path planning, false alarm, search-and-delivery, entropy, genetic algorithm

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Authors: Nidal F. Shilbayeh, Reem A. Al-Saidi, Ahmed H. Alsswey

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In this paper, we presented an evaluation and analysis of E-Voting Authentication Preparation Scheme (EV-APS). EV-APS applies some modified security aspects that enhance the security measures and adds a strong wall of protection, confidentiality, non-repudiation and authentication requirements. Some of these modified security aspects are Kerberos authentication protocol, PVID scheme, responder certificate validation, and the converted Ferguson e-cash protocol. Authentication and privacy requirements have been evaluated and proved. Authentication guaranteed only eligible and authorized voters were permitted to vote. Also, the privacy guaranteed that all votes will be kept secret. Evaluation and analysis of some of these security requirements have been given. These modified aspects will help in filtering the counter buffer from unauthorized votes by ensuring that only authorized voters are permitted to vote.

Keywords: e-voting preparation stage, blind signature protocol, Nonce based authentication scheme, Kerberos Authentication Protocol, pseudo voter identity scheme PVID

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Authors: Nabil Mezhoud

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The economic operation of electric energy generating systems is one of the predominant problems in energy systems. Due to the need for better reliability, high energy quality, lower losses, lower cost and a clean environment, the application of renewable and sustainable energy sources, such as wind energy, solar energy, etc., in recent years has become more widespread. In this work, one of a bio-inspired meta-heuristic algorithm inspired by the flashing behavior of fireflies at night called the Firefly Algorithm (FFA) is applied to solve the Optimal Energy Management (OEM) and the environmental index (EI) problems of a micro-grid (MG) operating by Renewable and Sustainable Generation Systems (RSGS). Our main goal is to minimize the nonlinear objective function of an electrical microgrid, taking into account equality and inequality constraints. The FFA approach was examined and tested on a standard MG system composed of different types of RSGS, such as wind turbines (WT), photovoltaic systems (PV), and non-renewable energy, such as fuel cells (FC), micro turbine (MT), diesel generator (DEG) and loads with energy storage systems (ESS). The results are promising and show the effectiveness and robustness of the proposed approach to solve the OEM and the EI problems. The results of the proposed method have been compared and validated with those known references published recently.

Keywords: renewable energy sources, energy management, distributed generator, micro-grids, firefly algorithm

Procedia PDF Downloads 76

Authors: G. C. Joshi, M. Paul, B. K. Kalita, V. Ranga, J. S. Rawat, P. S. Rawat

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Being a habitat of the global village, every place has established connection through the strength and power of social media piercing through the political boundaries. Social media is a digital platform, where people across the world can interact as it has advantages of being universal, anonymous, easily accessible, indirect interaction, gathering and sharing information. The power of social media lies in the intensity of sharing extreme opinions or feelings, in contrast to the personal interactions which can be easily mapped in the form of Sentiment Mapping. The easy access to social networking sites such as Facebook, Twitter and blogs made unprecedented opportunities for citizens to voice their opinions loaded with dynamics of emotions. These further influence human thoughts where social media plays a very active role. A recent incident of public importance was selected as a case study to map the sentiments of people through Twitter. Understanding those dynamics through the eye of an ordinary people can be challenging. With the help of R-programming language and by the aid of GIS techniques sentiment maps has been produced. The emotions flowing worldwide in the form of tweets were extracted and analyzed. The number of tweets had diminished by 91 % from 25/08/2017 to 31/08/2017. A boom of sentiments emerged near the origin of the case, i.e., Delhi, Haryana and Punjab and the capital showed maximum influence resulting in spillover effect near Delhi. The trend of sentiments was prevailing more as neutral (45.37%), negative (28.6%) and positive (21.6%) after calculating the sentiment scores of the tweets. The result can be used to know the spatial distribution of digital penetration in India, where highest concentration lies in Mumbai and lowest in North East India and Jammu and Kashmir.

Keywords: sentiment mapping, digital literacy, GIS, R statistical language, spatio-temporal

Procedia PDF Downloads 151

Authors: Han-Woong Ahn, In-Gun Kim, Hyun-Seok Hong, Dong-Woo Kang, Ju Lee

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It is important to accurately identify machine parameters for direct vector control. To obtain the parameter values, traditional methods can be used such as no-load and rotor locked tests. However, there are many differences between values obtained from the traditional tests and actual values. In addition, there are drawbacks that additional equipment and cost are required for the experiment. Therefore, it is hard to temporary operation to estimate induction motor parameters. Therefore, this paper deals with the estimation algorithm of induction motor parameters without a motor operation and the measurement from additional equipment such as sensors and dynamometer. The validity and usefulness of the estimation algorithm considering inverter nonlinearity is verified by comparing the conventional method with the proposed method.

Keywords: induction motor, parameter, off-line estimation, inverter nonlinearity

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Authors: Sonveer Singh, Sanjay Agrawal, D. V. Avasthi, Jayant Shekhar

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The aim of this paper is to improve the efficiency of photovoltaic thermal (PVT) system with the help of Genetic Algorithms with multi-objective function. There are some parameters that affect the efficiency of PVT system like depth and length of the channel, velocity of flowing fluid through the channel, thickness of the tedlar and glass, temperature of inlet fluid i.e. all above parameters are considered for optimization. An attempt has been made to the model and optimizes the parameters of glazed hybrid single channel PVT module when two objective functions have been considered separately. The two objective function for optimization of PVT module is overall electrical and thermal efficiency. All equations for PVT module have been derived. Using genetic algorithms (GAs), above two objective functions of the system has been optimized separately and analysis has been carried out for two cases. Two cases are: Case-I; Improvement in electrical and thermal efficiency when overall electrical efficiency is optimized, Case-II; Improvement in electrical and thermal efficiency when overall thermal efficiency is optimized. All the parameters that are used in genetic algorithms are the parameters that could be changed, and the non-changeable parameters, like solar radiation, ambient temperature cannot be used in the algorithm. It has been observed that electrical efficiency (14.08%) and thermal efficiency (19.48%) are obtained when overall thermal efficiency was an objective function for optimization. It is observed that GA is a very efficient technique to estimate the design parameters of hybrid single channel PVT module.

Keywords: genetic algorithm, energy, exergy, PVT module, optimization

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Authors: Francesca Spagnoli, Shenja van der Graaf, Pieter Ballon

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Humans do not behave rationally. We are emotional, easily influenced by others, as well as by our context. The study of human behaviour became a supreme endeavour within many academic disciplines, including economics, sociology, and clinical and social psychology. Understanding what motivates humans and triggers them to perform certain activities, and what it takes to change their behaviour, is central both for researchers and companies, as well as policy makers to implement efficient public policies. While numerous theoretical approaches for diverse domains such as health, retail, environment have been developed, the methodological models guiding the evaluation of such research have reached for a long time their limits. Within this context, digitisation, the Information and communication technologies (ICT) and wearable, the Internet of Things (IoT) connecting networks of devices, and new possibilities to collect and analyse massive amounts of data made it possible to study behaviour from a realistic perspective, as never before. Digital technologies make it possible to (1) capture data in real-life settings, (2) regain control over data by capturing the context of behaviour, and (3) analyse huge set of information through continuous measurement. Within this complex context, this paper describes a new framework for initiating behavioural change, capitalising on the digital developments in applied research projects and applicable both to academia, enterprises and policy makers. By applying this model, behavioural research can be conducted to address the issues of different domains, such as mobility, environment, health or media. The Modular Behavioural Analysis Approach (MBAA) is here described and firstly validated through a concrete use case within the domain of health. The results gathered have proven that disclosing information about health in connection with the use of digital apps for health, can be a leverage for changing behaviour, but it is only a first component requiring further follow-up actions. To this end, a clear definition of different 'behavioural profiles', towards which addressing several typologies of interventions, it is essential to effectively enable behavioural change. In the refined version of the MBAA a strong focus will rely on defining a methodology for shaping 'behavioural profiles' and related interventions, as well as the evaluation of side-effects on the creation of new business models and sustainability plans.

Keywords: behavioural change, framework, health, nudging, sustainability

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Authors: Eki Ruskartina, Vincent F. Yu, Budi Santosa, A. A. N. Perwira Redi

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This paper introduces symbiotic organism search (SOS) for solving capacitated vehicle routing problem (CVRP). SOS is a new approach in metaheuristics fields and never been used to solve discrete problems. A sophisticated decoding method to deal with a discrete problem setting in CVRP is applied using the basic symbiotic organism search (SOS) framework. The performance of the algorithm was evaluated on a set of benchmark instances and compared results with best known solution. The computational results show that the proposed algorithm can produce good solution as a preliminary testing. These results indicated that the proposed SOS can be applied as an alternative to solve the capacitated vehicle routing problem.

Keywords: symbiotic organism search, capacitated vehicle routing problem, metaheuristic

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Authors: Yashar Ibragimov, Nino Berishvili

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Technological, economic and other innovation-related advances of the 21st century have influenced the old, traditional business models. Presently, organizational change has become an integral part of corporate strategy for the majority of businesses. Such shifts have resulted in both new challenges and opportunities. The expansion of the use of information and communication technologies has driven fundamental shifts towards digital change. Organizations are being forced to revise processes, goals and overall mission in order to stay competitive in the marketplace. However, the implementation of digital transformation brings uncertainty, causes stress and raises concerns about future jobs. The study employs systematic literature review to fill the gap in understanding relationship between employee motivation and commitment during the transformation. A conceptual model proposes the antecedents (OCB and Leader Member Exchange) of employee motivation and investigates its impact on employee commitment to change. The utilized model elucidates how to maintain employee motivation and commitment in the context of organizational transformation and sets the ground for future research.

Keywords: employee motivation, change commitment, change management, leader member exchange, organizational citizenship behavior

Procedia PDF Downloads 79

Authors: Caio E. G. Reis, Jose G. Dórea, Teresa H. M. da Costa

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Objective: Epidemiological data shows an inverse association of coffee consumption with risk of type 2 diabetes mellitus. However, the clinical effects of coffee consumption on the glucose metabolism biomarkers remain controversial. Thus, this paper reviews clinical trials that evaluated the effects of coffee consumption on glucose metabolism. Research Design and Methods: We identified studies published until December 2014 by searching electronic databases and reference lists. We included randomized clinical trials which the intervention group received caffeinated and/or decaffeinated coffee and the control group received water or placebo treatments and measured biomarkers of glucose metabolism. The Jadad Score was applied to evaluate the quality of the studies whereas studies that scored ≥ 3 points were considered for the analyses. Results: Seven clinical trials (total of 237 subjects) were analyzed involving adult healthy, overweight and diabetic subjects. The studies were divided in short-term (1 to 3h) and long-term (2 to 16 weeks) duration. The results for short-term studies showed that caffeinated coffee consumption may increase the area under the curve for glucose response, while for long-term studies caffeinated coffee may improve the glycemic metabolism by reducing the glucose curve and increasing insulin response. These results seem to show that the benefits of coffee consumption occur in the long-term as has been shown in the reduction of type 2 diabetes mellitus risk in epidemiological studies. Nevertheless, until the relationship between long-term coffee consumption and type 2 diabetes mellitus is better understood and any mechanism involved identified, it is premature to make claims about coffee preventing type 2 diabetes mellitus. Conclusion: The findings suggest that caffeinated coffee may impairs glucose metabolism in short-term but in the long-term the studies indicate reduction of type 2 diabetes mellitus risk. More clinical trials with comparable methodology are needed to unravel this paradox.

Keywords: coffee, diabetes mellitus type 2, glucose, insulin

Procedia PDF Downloads 466

Authors: Simon Arunga, Teddy Kwaga, Rita Kageni, Michael Gichangi, Nyawira Mwangi, Fred Kagwa, Rogers Mwavu, Amos Baryashaba, Luis F. Nakayama, Katharine Morley, Michael Morley, Leo A. Celi, Jessica Haberer, Celestino Obua

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Purpose: The main aim for creating the Multimodal Database of Retinal Images for Africa (MoDRIA) was to provide a publicly available repository of retinal images for responsible researchers to conduct algorithm development in a bid to curb the challenges of ophthalmic artificial intelligence (AI) in Africa. Methods: Data and retina images were ethically sourced from sites in Uganda and Kenya. Data on medical history, visual acuity, ocular examination, blood pressure, and blood sugar were collected. Retina images were captured using fundus cameras (Foru3-nethra and Canon CR-Mark-1). Images were stored on a secure online database. Results: The database consists of 7,859 retinal images in portable network graphics format from 1,988 participants. Images from patients with human immunodeficiency virus were 18.9%, 18.2% of images were from hypertensive patients, 12.8% from diabetic patients, and the rest from normal’ participants. Conclusion: Publicly available data repositories are a valuable asset in the development of AI technology. Therefore, is a need for the expansion of MoDRIA so as to provide larger datasets that are more representative of Sub-Saharan data.

Keywords: retina images, MoDRIA, image repository, African database

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Authors: Alvaro Javier Ortega

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A large number of companies face the challenge of matching highly-skilled professionals to high-end positions by human resource deployment professionals. However, when the professional list and tasks to be matched are larger than a few dozens, this process result is far from optimal and takes a long time to be made. Therefore, an automated assignment algorithm for this workforce management problem is needed. The majority of companies are divided into several sectors or departments, where trained employees with different experience levels deal with a large number of tasks daily. Also, the execution order of all tasks is of mater consequence, due to some of these tasks just can be run it if the result of another task is provided. Thus, a wrong execution order leads to large waiting times between consecutive tasks. The desired goal is, therefore, creating accurate matches and a near-optimal execution order that maximizes the number of tasks performed and minimizes the idle time of the expensive skilled employees. The problem described before can be model as a mixed-integer non-linear programming (MINLP) as it will be shown in detail through this paper. A large number of MINLP algorithms have been proposed in the literature. Here, genetic algorithm solutions are considered and a comparison between two different mutation approaches is presented. The simulated results considering different complexity levels of assignment decisions show the appropriateness of the proposed model.

Keywords: employees, genetic algorithm, industry management, workforce

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Authors: Z. Mortezaie, H. Hassanpour, S. Asadi Amiri

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Captured images may suffer from Gaussian blur due to poor lens focus or camera motion. Unsharp masking is a simple and effective technique to boost the image contrast and to improve digital images suffering from Gaussian blur. The technique is based on sharpening object edges by appending the scaled high-frequency components of the image to the original. The quality of the enhanced image is highly dependent on the characteristics of both the high-frequency components and the scaling/gain factor. Since the quality of an image may not be the same throughout, we propose an adaptive unsharp masking method in this paper. In this method, the gain factor is computed, considering the gradient variations, for individual pixels of the image. Subjective and objective image quality assessments are used to compare the performance of the proposed method both with the classic and the recently developed unsharp masking methods. The experimental results show that the proposed method has a better performance in comparison to the other existing methods.

Keywords: unsharp masking, blur image, sub-region gradient, image enhancement

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Authors: Mohamad Farhat, Bal Virdee

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This paper present a compact Microstrip Bandpass filter exhibiting a very wide stop band and high selectivity. The filter comprises of asymmetric resonator structures, which are interconnected by an inter-digital capacitor to enable the realization of a wide bandwidth with high rejection level. High selectivity is obtained by optimizing the parameters of the interdigital capacitor. The filter has high out-of-band rejection (> 30 dB), less than 0.6 dB of insertion-loss, up to 5.5 GHz spurii free, and about 18 dB of return-loss. Full-wave electromagnetic simulator ADSTM (Mom) is used to analyze and optimize the prototype bandpass filter. The proposed technique was verified practically to validate the design methodology. The experimental results of the prototype circuit are presented and a good agreement was obtained comparing with the simulation results. The dimensions of the proposed filter are 32 x 24 mm2.The filter’s characteristics and compact size make it suitable for wireless communication systems.

Keywords: asymmetric resonator, bandpass filter, microstrip, spurious suppression, ultra-wide stop band

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Authors: Mohamed El Hachimi, Abdelhakim Ballouk, Ilyas Khelafa, Abdelaziz Mouhou

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Introduction: Type 1 diabetes occurs when beta cells are destroyed by the body's own immune system. Treatment of type 1 diabetes mellitus could be greatly improved by applying a closed-loop control strategy to insulin delivery, also known as an Artificial Pancreas (AP). Method: In this paper, we present a new formulation of the cost function for a Model Predictive Control (MPC) utilizing a technic which accelerates the speed of control of the AP and tackles the nonlinearity of the control problem via asymmetric objective functions. Finding: The finding of this work consists in a new Model Predictive Control algorithm that leads to good performances like decreasing the time of hyperglycaemia and avoiding hypoglycaemia. Conclusion: These performances are validated under in silico trials.

Keywords: artificial pancreas, control algorithm, biomedical control, MPC, objective function, nonlinearity

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Authors: Nur Aziza Luxfiati

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Clustering is partitioning data sets into sub-sets or groups in such a way that elements certain properties have shared property settings with a high level of similarity within one group and a low level of similarity between groups. . The K-Means algorithm is one of thealgorithmsclustering as a grouping tool that is most widely used in scientific and industrial applications because the basic idea of the kalgorithm is-means very simple. In this research, applying the technique of clustering using the k-means algorithm as a method of solving the problem of national development imbalances between regions in Central Java Province based on macroeconomic indicators. The data sample used is secondary data obtained from the Central Java Provincial Statistics Agency regarding macroeconomic indicator data which is part of the publication of the 2019 National Socio-Economic Survey (Susenas) data. score and determine the number of clusters (k) using the elbow method. After the clustering process is carried out, the validation is tested using themethodsBetween-Class Variation (BCV) and Within-Class Variation (WCV). The results showed that detection outlier using z-score normalization showed no outliers. In addition, the results of the clustering test obtained a ratio value that was not high, namely 0.011%. There are two district/city clusters in Central Java Province which have economic similarities based on the variables used, namely the first cluster with a high economic level consisting of 13 districts/cities and theclustersecondwith a low economic level consisting of 22 districts/cities. And in the cluster second, namely, between low economies, the authors grouped districts/cities based on similarities to macroeconomic indicators such as 20 districts of Gross Regional Domestic Product, with a Poverty Depth Index of 19 districts, with 5 districts in Human Development, and as many as Open Unemployment Rate. 10 districts.

Keywords: clustering, K-Means algorithm, macroeconomic indicators, inequality, national development

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Authors: Hassen Hamouda, Mohamed Ouwais Kabaou, Med Salim Bouhlel

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The growth of wireless technology made opportunistic scheduling a widespread theme in recent research. Providing high system throughput without reducing fairness allocation is becoming a very challenging task. A suitable policy for resource allocation among users is of crucial importance. This study focuses on scheduling multiple streaming flows on the downlink of a WiMAX system based on orthogonal frequency division multiple access (OFDMA). In this paper, we take the first step in formulating and analyzing this problem scrupulously. As a result, we proposed a new scheduling scheme based on Round Robin (RR) Algorithm. Because of its non-opportunistic process, RR does not take in account radio conditions and consequently it affect both system throughput and multi-users diversity. Our contribution called MORRA (Modified Round Robin Opportunistic Algorithm) consists to propose a solution to this issue. MORRA not only exploits the concept of opportunistic scheduler but also takes into account other parameters in the allocation process. The first parameter is called courtesy coefficient (CC) and the second is called Buffer Occupancy (BO). Performance evaluation shows that this well-balanced scheme outperforms both RR and MaxSNR schedulers and demonstrate that choosing between system throughput and fairness is not required.

Keywords: OFDMA, opportunistic scheduling, fairness hierarchy, courtesy coefficient, buffer occupancy

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Authors: Prikhil Agrawal, N. Priyanka

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With the increase of millions of users on Internet day by day, it is very essential to maintain highly reliable and secured data communication between various corporations. Although there are various traditional security imparting techniques such as antivirus software, password protection, data encryption, biometrics and firewall etc. But still network security has become the main issue in various leading companies. So IDSs have become an essential component in terms of security, as it can detect various network attacks and respond quickly to such occurrences. IDSs are used to detect unauthorized access to a computer system. This paper describes various intrusion detection techniques using GA approach. The intrusion detection problem has become a challenging task due to the conception of miscellaneous computer networks under various vulnerabilities. Thus the damage caused to various organizations by malicious intrusions can be mitigated and even be deterred by using this powerful tool.

Keywords: genetic algorithm (GA), intrusion detection system (IDS), dataset, network security

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Authors: Somsak Klaysung

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This study purpose to develop video to promote cultural tourism in Taling Chan District. For qualitative research, the sample size was 40 people from 5 group of the tourism entrepreneur in Taling Chan district, conducted the key informants’ content analysis by using focus group and structures in-depth interview from all stakeholders. Quota sampling was used for this kind of research. The findings indicated that get media video marketing and tourism contribute a set length 11.35 9 minutes there is plenty of social capital in Taling Chan District including detail like local wisdom, knowledge, and way of thinking related to nature, history, historic document, occupation, administration and attribute of local people. Additional research found the new path of travel through the water route according to Khlong Bang Ramat called Route 9 temples that travelers can travel by boat are available in the market in four areas Taling Chan also as well.

Keywords: digital video, integrated marketing communication, online media development, Taling Chan district

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Authors: Halimi Mohamed Taher, Kordoghli Bassem, Ben Hassen Mohamed, Sakli Faouzi

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Textile industry has been an important part of many developing countries economies such as Tunisia. This industry is confronted with a challenging and increasing competitive environment. Good quality management in production process is the key factor for retaining existence especially in raw material exploitation. The present work aims to develop an intelligent system for fabric inspection. In the first step, we have studied the method used for fabric control which takes into account the default length and localization in woven. In the second step, we have used a method based on the fuzzy logic to minimize the Demerit point indicator with appropriate total rollers length, so that the quality problem becomes multi-objective. In order to optimize the total fabric quality, we have applied the genetic algorithm (GA).

Keywords: fabric control, Fuzzy logic, genetic algorithm, quality management

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Authors: Jihen Bennaceur, Wissem Zouaghi, Ali Mabrouk

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The major aim of cyber threat intelligence (CTI) is to provide sophisticated knowledge about cybersecurity threats to ensure internal and external safeguards against modern cyberattacks. Inaccurate, incomplete, outdated, and invaluable threat intelligence is the main problem. Therefore, data analysis based on AI algorithms is one of the emergent solutions to overcome the threat of information-sharing issues. In this paper, we propose a supervised machine learning-based algorithm to improve threat information sharing by providing a sophisticated classification of cyber threats and data. Extensive simulations investigate the accuracy, precision, recall, f1-score, and support overall to validate the designed algorithm and to compare it with several supervised machine learning algorithms.

Keywords: threat information sharing, supervised learning, data classification, performance evaluation

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Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

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E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

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Authors: Belaynesh Chekol, Numan Çelebi

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The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

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Authors: Alexis Polanco Jr, Tsai Lu Liu

Abstract:

Research on digital assessment for deaf and hard of hearing (DHH) students is negligible. Part of this stems from the DHH assessment design existing at the intersection of the emergent disciplines of usability, accessibility, and child-computer interaction (CCI). While these disciplines have some prevailing guidelines —e.g. in user experience design (UXD), there is Jacob Nielsen’s 10 Usability Heuristics (Nielsen-10); for accessibility, there are the Web Content Accessibility Guidelines (WCAG) & the Principles of Universal Design (PUD)— this research was unable to uncover a unified set of guidelines. Given that digital assessments have lasting implications for the funding and shaping of U.S. school districts, it is vital that cross-disciplinary guidelines emerge. As a result, this research seeks to provide a framework by which these disciplines can share knowledge. The framework entails a process of asking subject-matter experts (SMEs) and design & development professionals to self-describe their fields of expertise, how their work might serve DHH students, and to expose any incongruence between their ideal process and what is permissible at their workplace. This research used two rounds of mixed methods. The first round consisted of structured interviews with SMEs in usability, accessibility, CCI, and DHH education. These practitioners were not designers by trade but were revealed to use designerly work processes. In addition to asking these SMEs about their field of expertise, work process, etc., these SMEs were asked to comment about whether they believed Nielsen-10 and/or PUD were sufficient for designing products for middle-school DHH students. This first round of interviews revealed that Nielsen-10 and PUD were, at best, a starting point for creating middle-school DHH design guidelines or, at worst insufficient. The second round of interviews followed a semi-structured interview methodology. The SMEs who were interviewed in the first round were asked open-ended follow-up questions about their semantic understanding of guidelines— going from the most general sense down to the level of design guidelines for DHH middle school students. Designers and developers who were never interviewed previously were asked the same questions that the SMEs had been asked across both rounds of interviews. In terms of the research goals: it was confirmed that the design of digital assessments for DHH students is inherently cross-disciplinary. Unexpectedly, 1) guidelines did not emerge from the interviews conducted in this study, and 2) the principles of Nielsen-10 and PUD were deemed to be less relevant than expected. Given the prevalence of Nielsen-10 in UXD curricula across academia and certificate programs, this poses a risk to the efficacy of DHH assessments designed by UX designers. Furthermore, the following findings emerged: A) deep collaboration between the disciplines of usability, accessibility, and CCI is low to non-existent; B) there are no universally agreed-upon guidelines for designing digital assessments for DHH middle school students; C) these disciplines are structured academically and professionally in such a way that practitioners may not know to reach out to other disciplines. For example, accessibility teams at large organizations do not have designers and accessibility specialists on the same team.

Keywords: deaf, hard of hearing, design, guidelines, education, assessment

Procedia PDF Downloads 67

Authors: Kisan Sarda, Bhavika Shingote

Abstract:

This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage.

Keywords: semi parametric regression, photovoltaic (PV) system, regression modelling, irradiation

Procedia PDF Downloads 382