Search results for: finite state machine

Authors: Ananya Sharma

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International Relations is a discipline of paradoxes. The State is the dominant political institution, with mainstream analysis theorizing the State, but theory remains at best a reactionary monolith. Critical Theorists have been pushing the envelope and to that extent, there has been a clear shift in the dominant discourse away from State-centrism to individuals and group-level behaviour. This paradigm shift has been accompanied with more nuanced conceptualizations of other variables at play–power, security, and trust, to name a few. Yet, the ambit of “what is discussed” remains primarily embedded in realist conceptualizations. With this background in mind, this paper will attempt to understand, juxtapose and evaluate how “order” has been conceptualized in International Relations theory. This paper is a tentative attempt to present a “state of the art” and in the process, set the stage for a deeper study to draw attention to what the author feels is a gaping lacuna in IR theory. The paper looks at how different branches of international relations theory envisage world order and the silences embedded therein. Further, by locating order and disorder inhabiting the same reality along a continuum, alternative readings of world orders are drawn from the critical theoretical traditions, in which various articulations of justice impart the key normative pillar to the world order.

Keywords: global justice, international relations theory, legitimacy, world order

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Authors: Jatinder Kaur, Pankaj Thakur

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The effect of pressure and temperature in non-homogeneous circular cylinder by taking non-homogeneity of material in terms of compressibility c=c₀r⁻ᵏ has been observed. From the results, it could be seen that for K<0, high pressure is required in the initial yielding state than for the case K >0. Under thermal conditions for value K<0, lesser amount of pressure is required for initial yielding, and further, the amount keeps on decreasing with an increase in temperature. Curves are drawn between pressure and radii ratio for initial and fully plastic state with and without temperature conditions. Further graphs between stresses (hoop and radial) and radii ratio for fully plastic state with and without temperature conditions are also drawn and concluded that hoop stresses become minimum with the increase in temperature as compared to radial stresses.

Keywords: cylinder, elastic, plastic, copper, steel, stresses, pressure, load

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Authors: George Kurian, Hongmei Chi

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Digital evidence plays a critical role in most legal investigations. In many cases, thumbnail databases show important information in that investigation. The probability of having digital evidence retrieved from a computer or smart device has increased, even though the previous user removed data and deleted apps on those devices. Due to the increase in digital forensics, the ability to store residual information from various thumbnail applications has improved. This paper will focus on investigating thumbnail information from Windows 10. Thumbnail images of interest in forensic investigations may be intact even when the original pictures have been deleted. It is our research goal to recover useful information from thumbnails. In this research project, we use various forensics tools to collect left thumbnail information from deleted videos or pictures. We examine and describe the various thumbnail sources in Windows and propose a methodology for thumbnail collection and analysis from laptops or desktops. A machine learning algorithm is adopted to help speed up content from thumbnail pictures.

Keywords: digital forensic, forensic tools, soundness, thumbnail, machine learning, OCR

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Authors: Arun Prasad Jaganathan

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In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper introduces an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization

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Authors: T. Yamaguchi, M. Watanabe, M. Sasajima, C. Yuan, S. Maruyama, T. B. Ibrahim, H. Tomita

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This paper deals with nonlinear vibration analysis using finite element method for frame structures consisting of elastic and viscoelastic damping layers supported by multiple nonlinear concentrated springs with hysteresis damping. The frame is supported by four nonlinear concentrated springs near the four corners. The restoring forces of the springs have cubic non-linearity and linear component of the nonlinear springs has complex quantity to represent linear hysteresis damping. The damping layer of the frame structures has complex modulus of elasticity. Further, the discretized equations in physical coordinate are transformed into the nonlinear ordinary coupled differential equations using normal coordinate corresponding to linear natural modes. Comparing shares of strain energy of the elastic frame, the damping layer and the springs, we evaluate the influences of the damping couplings on the linear and nonlinear impact responses. We also investigate influences of damping changed by stiffness of the elastic frame on the nonlinear coupling in the damped impact responses.

Keywords: dynamic response, nonlinear impact response, finite element analysis, numerical analysis

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Authors: Ismaila Oladunni Muhammed, Adegbenga Raphael Ashiru

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Construction industry has been attributed to be the engine growth of Nigerian economic and infrastructural development. It promotes infrastructural development and grows an average output of Nigerian Gross Domestic Profit. However, from this great prospect, yearly reports show that consistent accidents on construction sites in Zamfara State has affected a substantial number of workers as they become temporarily or permanently disabled, thereby making many construction sites a death trap. This posed a great threat to the industry’s sustainability, de- motivate workers from working in the industry, and further have negative impact on the economy as well. It is as a result of high construction site accident currently experiencing in Zamfara state that this research was carried out to appraise the sustainability of health and safety of construction workers on sites. The proper practice and compliance to Construction Health and safety laws are very vital to the output and growth of Zamfara State construction sector. However, a quantitative approach was adopted to justify the aforesaid statement which will provide a broader understanding of the study. Descriptive statistical analysis was obtained through Statistical Package for the Social Sciences (SPSS Version 20). Furthermore, the findings from the research highlighted that the performance and output of construction workers on construction sites depends on the proper practice of health and safety. The research findings also recommended ways to enhance employers and employee’s compliance with existing laws guiding health and safety on Zamfara State construction sites. Nevertheless, the purpose of the paper is to appraise the sustainability of Health and Safety on Zamfara State construction sites. This study further provided thorough information for resolving challenges of construction sites accidents to improve construction firm productivity and also ensured construction worker’s safety on site.

Keywords: construction industry, health and safety regulations, causes of accident, construction safety practices

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Authors: Rana Tajammal Rashid

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Federalism is a political system in which government power and responsibility are divided between a federal legislature and units of the state or provincial legislatures. This system provides the structure for the states having large territory and through that can manage the state affairs and administration easily. Many of the largest countries in the world are federations, like; The United States, Canada, India, Pakistan South Africa, Argentina, and Australia. Every large democratic nation has a federal system of government. This study will explore the feature and good governance of two developed countries Canada and Australia. This study will be helpful to the developing countries like Pakistan, India which have a federal form of structure to run the affairs of the state. In the federal system of Pakistan there are lot of issues and conflicts with the provinces with a comparative study of these two developed countries, i.e., Australia and Canada, our policy and decision maker political actors will understand in which way a state will successfully manage the issues related to federalism. This study will also provide the help to the students of comparative politics that how to analysis the different political system of the developed countries of the world.

Keywords: federalism, features of federalism, types of federalism, history of federalism, Australian federalism, Canadian federalism, federalism developments, executives, federal and provincial autonomy legislative, judicial

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Authors: Tosin Osasona

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The Nigeria Police Force bears the constitutional mandate as the primary policing agency for the protection of life and property within Nigeria; however, the police have an historical ill-reputation for corruption, ineptitude and impunity. Using the institutional theory of police as the framework of analysis, the paper argues that the performance of the police in Nigeria mirrors the dominant political, social and economic institutions and the structural environment of the Nigerian state. The article puts in perspective the deliberate political decision to underfund the police, leaving officers of the force the extra task of foraging for funds to undertake the duty that the Nigeria state primarily exists for; the article further explores the nexus between corruption in the police in Nigeria and the issue of funding. The article finds that the Nigerian state, by deliberately under-funding the police, while expecting the agency to perform its duties, has indirectly sanctioned the corruption of the force and approved the cooption of the institution of police and policing for private use in Nigeria.

Keywords: Police Corruption, Funding , Informal Taxation, POlice Checkpoint

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Authors: Safi Haider

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This work seeks to understand the fundamental mourning ceremonies termed azadari in the tristate Shi’i community. Azadari, a name derived from the Arabic aza (mourning) and the Persian dari (commemoration) as it exists in the Tri-State Shi'i community has a unique relation to the hearts and minds of its adherents. It shows the development of the community from the various perspective offered by the participants of this work. This work seeks to analyze Azadari in the light of the Tri-State Shi'i community, which is a deeply diverse community, consisting of immigrants from various other countries, including Pakistan, India, Iran, and the Arab communities. At its heart, this work is an ethnography, it seeks to know the experience of those who are a part of the Muharram commemorations and it seek to see what the underlying psychological and the social foundations of Azadari are. Five people from each of the four communities were interviewed, and the aim was to have at least two men, two women, two youth, two elders, and one person from either of these categories. What was found was that the Shi’i community is scarcely a monolith in its mourning practices, and there is a great difference not only when comparing one cultural community to another, but also within the communities as well. This work seeks to analyze azadari from the various perspective of the Shi’i community in the tri-state area. This work seeks to analyze interviews from twenty people in total: two men, two women, two youth, and two adults from each of the communities of the Shi’i of the tristate area, for a total of twenty people. Two Priests were also interviewed for the sake of the paper as well.

Keywords: Ashura, Imam Husayn, Islam, Muharram, Shi'i

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Authors: M. D. D. Chathurangi, M. G. K. Nayanathara, K. M. H. M. M. Gunapala, G. M. R. G. Dayananda, Kavinga Yapa Abeywardena, Deemantha Siriwardana

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Due to the growing popularity of social media platforms at present, there are various concerns, mostly cyberbullying, spam, bot accounts, and the spread of incorrect information. To develop a risk score calculation system as a thorough method for deciphering and exposing unethical social media profiles, this research explores the most suitable algorithms to our best knowledge in detecting the mentioned concerns. Various multiple models, such as Naïve Bayes, CNN, KNN, Stochastic Gradient Descent, Gradient Boosting Classifier, etc., were examined, and the best results were taken into the development of the risk score system. For cyberbullying, the Logistic Regression algorithm achieved an accuracy of 84.9%, while the spam-detecting MLP model gained 98.02% accuracy. The bot accounts identifying the Random Forest algorithm obtained 91.06% accuracy, and 84% accuracy was acquired for fake news detection using SVM.

Keywords: cyberbullying, spam behavior, bot accounts, fake news, machine learning

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Authors: Bharatendra Rai

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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

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Authors: Evangelos G. Ilias, Panagiotis G. Ilias, Vasileios T. Popotas

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The United Kingdom has one of the oldest railway networks in the world dating back to 1825 when the world’s first passenger railway was opened. The network has some 40,000 bridges of various construction types using a wide range of materials including masonry, steel, cast iron, wrought iron, concrete and timber. It is commonly accepted that the successful operation of the network is vital for the economy of the United Kingdom, consequently the cost effective maintenance of the existing infrastructure is a high priority to maintain the operability of the network, prevent deterioration and to extend the life of the assets. Every bridge on the railway network is required to be assessed every eighteen years and a structured approach to assessments is adopted with three main types of progressively more detailed assessments used. These assessment types include Level 0 (standardized spreadsheet assessment tools), Level 1 (analytical hand calculations) and Level 2 (generally finite element analyses). There is a degree of conservatism in the first two types of assessment dictated to some extent by the relevant standards which can lead to some structures not achieving the required load rating. In these situations, a Level 2 Assessment is often carried out using finite element analysis to uncover ‘latent strength’ and improve the load rating. If successful, the more sophisticated analysis can save on costly strengthening or replacement works and avoid disruption to the operational railway. This paper presents the ‘strengthening by assessment’ achieved by Level 2 analyses. The use of more accurate analysis assumptions and the implementation of non-linear modelling and functions (material, geometric and support) to better understand buckling modes and the structural behaviour of historic construction details that are not specifically covered by assessment codes are outlined. Metallic bridges which are susceptible to loss of section size through corrosion have largest scope for improvement by the Level 2 Assessment methodology. Three case studies are presented, demonstrating the effectiveness of the sophisticated Level 2 Assessment methodology using finite element analysis against the conservative approaches employed for Level 0 and Level 1 Assessments. One rail overbridge and two rail underbridges that did not achieve the required load rating by means of a Level 1 Assessment due to the inadequate restraint provided by U-Frame action are examined and the increase in assessed capacity given by the Level 2 Assessment is outlined.

Keywords: assessment, bridges, buckling, finite element analysis, non-linear modelling, strengthening

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: Yuanjin Liu

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Retirement withdrawal strategies are very important to minimize the probability of ruin in retirement. The ruin probability is modeled as a function of initial withdrawal age, gender, asset allocation, inflation rate, and initial withdrawal rate. The ruin probability is obtained based on the 2019 period life table for the Social Security, IRS Required Minimum Distribution (RMD) Worksheets, US historical bond and equity returns, and inflation rates using simulation. Several popular machine learning algorithms of the generalized additive model, random forest, support vector machine, extreme gradient boosting, and artificial neural network are built. The model validation and selection are based on the test errors using hyperparameter tuning and train-test split. The optimal model is recommended for retirees to monitor the ruin probability. The optimal withdrawal strategy can be obtained based on the optimal predictive model.

Keywords: ruin probability, retirement withdrawal strategies, predictive models, optimal model

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

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Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: Nosakhare Enoma, Alphose Zingoni

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The requirements for various toroidal shell forms are increasing due to new applications, available storage space and the consideration of appearance. Because of the complexity of some of these structural forms, the finite element method is nowadays mainly used for their analysis, even for simple static studies. This paper presents an easy-to-use analytical algorithm for pressurized multi-segmented toroidal shells of revolution. The membrane solution, which acts as a particular solution of the bending-theory equations, is developed based on membrane theory of shells, and a general approach is formulated for quantifying discontinuity effects at the shell junctions using the well-known Geckeler’s approximation. On superimposing these effects, and applying the ensuing solution to the problem of the pressurized toroid with four segments, closed-form stress results are obtained for the entire toroid. A numerical example is carried out using the developed method. The analytical results obtained show excellent agreement with those from the finite element method, indicating that the proposed method can be also used for complementing and verifying FEM results, and providing insights on other related problems.

Keywords: bending theory of shells, membrane hypothesis, pressurized toroid, segmented toroidal vessel, shell analysis

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Authors: Lule Basha, Eralda Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

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Authors: M. Iruleswari, A. Jeyapaul Murugan

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Most fundamental part used in many Digital Signal Processing (DSP) application is a Finite Impulse Response (FIR) filter because of its linear phase, stability and regular structure. Designing a high-speed and hardware efficient FIR filter is a very challenging task as the complexity increases with the filter order. In most applications the higher order filters are required but the memory usage of the filter increases exponentially with the order of the filter. Using multipliers occupy a large chip area and need high computation time. Multiplier-less memory-based techniques have gained popularity over past two decades due to their high throughput processing capability and reduced dynamic power consumption. This paper describes the design and implementation of highly efficient Look-Up Table (LUT) based circuit for the implementation of FIR filter using Distributed arithmetic algorithm. It is a multiplier less FIR filter. The LUT can be subdivided into a number of LUT to reduce the memory usage of the LUT for higher order filter. Analysis on the performance of various filter orders with different address length is done using Xilinx 14.5 synthesis tool. The proposed design provides less latency, less memory usage and high throughput.

Keywords: finite impulse response, distributed arithmetic, field programmable gate array, look-up table

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Authors: Ahmed Mudi, Bala Zakar

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The paper assesses the impact of safe motherhood initiative on maternal health of women in Gumel Local Government Area of Jigawa State. The work will specifically concentrate on the background on safe motherhood scheme and maternal health of women. The objective of this paper is to assess the level of safe motherhood scheme in Gumel local government area, to find out the level of maternal health in Gumel local government as well as to determine the impact of safe motherhood scheme on maternal health on women in Gumel Local Government Area Jigawa State. Various literature on the topic are reviewed, the paper adopts survey design and use questionnaire to collect data from the respondent. The study comprises 350 women selected from six rural communities in Gumel using random sampling techniques, and the data was analysed by simple frequency and percentage. The research concluded that safe motherhood initiative has a significant impact on the maternal health of women in Gumel Local Government Area of Jigawa State. Finally, suitable recommendations were given on how to improve the scheme to ensure better maternal health in the region.

Keywords: action, assessment, maternal health, safe motherhood, surgery

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Authors: José A. Menezes, Giordano Cabral, Bruno T. Gomes

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Choosing good features is an essential part of machine learning. Recent techniques aim to automate this process. For instance, feature learning intends to learn the transformation of raw data into a useful representation to machine learning tasks. In automatic audio classification tasks, this is interesting since the audio, usually complex information, needs to be transformed into a computationally convenient input to process. Another technique tries to generate features by searching a feature space. Genetic algorithms, for instance, have being used to generate audio features by combining or modifying them. We find this approach particularly interesting and, despite the undeniable advances of feature learning approaches, we wanted to take a step forward in the use of genetic algorithms to find audio features, combining them with more conventional methods, like PCA, and inserting search control mechanisms, such as constraints over a confusion matrix. This work presents the results obtained on particular audio classification problems.

Keywords: feature generation, feature learning, genetic algorithm, music information retrieval

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Authors: Tran Gia Khanh, Dao Phuong Nam, Do Trong Tan, Nguyen Van Huong, Mai Xuan Sinh

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This work presents the problem of tube-based robust model predictive controller for a class of continuous-time systems in the presence of input disturbances. The main objective is to point out the state trajectory of closed system being maintained inside a sequence of tubes. An estimation of attraction region of the closed system is pointed out based on input state stability (ISS) theory and linearized model in each time interval. The theoretical analysis and simulation results demonstrate the performance of the proposed algorithm for a wheeled inverted pendulum system.

Keywords: input state stability (ISS), tube-based robust MPC, continuous-time nonlinear systems, wheeled inverted pendulum

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Authors: Folasade Adegbaju, Olusola Arowolo, Olufisayo Onawumi

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Owolowo ile – ege garri processing industry which is a small scale cassava processing industry, located in Ilesha, Osun State was purposively selected as a case study because it is a cooperative business. This industry was established in 1991 by eight men (8) who were mostly retirees. A researcher made questionnaire was used to collect information from thirty (30) respondents: the manager, four official staffs and 25 randomly selected processors in the industry. The study found that within twelve years of the utilization of their self raised initial capital of N240, 000 naira (Two hundred and forty thousand naira) this cassava – based industry had impacted on and attracted the involvement of many more people because within the period of the study (i.e. 2007-2011) the processors had quadrupled in number (e.g. 8 to 30), the facilities (equipment) in use had increased from one machine and a frying pot to many, this translated into being able to produce large quantities of fried garri, fufu and also starch for marketing to the people in Ilesha and neighbouring cities like Ibadan, Lagos, etc. This is indicative of economic growth. The industry also became a source of employment for community members in the sense that, as at the time of study four staffs were employed to work and coordinate the industry. It was observed that despite all odds of small-scale industry and the problem of people migrating from rural to urban area, this agro-based industry still existed successfully in the community, and many of such industry can be replicated by such agricultural cooperative groups nationwide so as to further boost the productivity as well as the economy of the area and nation at large. However, government and individual still have major roles to play in ensuring the growth and development of the nation in this respect.The local agricultural cooperative groups should form regional cooperative consortium with more networking for the farmers, in order to create more jobs for the young ones and to increase agricultural productivity in the country thus resulting in a better and more sustainable economy.

Keywords: agricultural cooperative, cassava processing industry, model, small scale enterprise

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Authors: Y. Bablu Singh, B. S. Purkayashtha, Chungkham Yashawanta Singh

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Morphological analysis forms the basic foundation in NLP applications including syntax parsing Machine Translation (MT), Information Retrieval (IR) and automatic indexing in all languages. It is the field of the linguistics; it can provide valuable information for computer based linguistics task such as lemmatization and studies of internal structure of the words. Computational Morphology is the application of morphological rules in the field of computational linguistics, and it is the emerging area in AI, which studies the structure of words, which are formed by combining smaller units of linguistics information, called morphemes: the building blocks of words. Morphological analysis provides about semantic and syntactic role in a sentence. It analyzes the Manipuri word forms and produces several grammatical information associated with the words. The Morphological Analyzer for Manipuri has been tested on 3500 Manipuri words in Shakti Standard format (SSF) using Meitei Mayek as source; thereby an accuracy of 80% has been obtained on a manual check.

Keywords: morphological analysis, machine translation, computational morphology, information retrieval, SSF

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Authors: Sina Fadaie, Seyed Abolhassan Naeini

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Coastal embankments play an important role in coastal structures by reducing the effect of the wave forces and controlling the movement of sediments. Many coastal areas are underlain by weak and compressible soils. Estimation of during construction settlement of coastal embankments is highly important in design and safety control of embankments and appurtenant structures. Accordingly, selecting and establishing of an appropriate model with a reasonable level of complication is one of the challenges for engineers. Although there are advanced models in the literature regarding design of embankments, there is not enough information on the prediction of their associated settlement, particularly in coastal areas having considerable soft soils. Marine engineering study in Iran is important due to the existence of two important coastal areas located in the northern and southern parts of the country. In the present study, the validity of Terzaghi’s consolidation theory has been investigated. In addition, the settlement of these coastal embankments during construction is predicted by using special methods in PLAXIS software by the help of appropriate boundary conditions and soil layers. The results indicate that, for the existing soil condition at the site, some parameters are important to be considered in analysis. Consequently, a model is introduced to estimate the settlement of the embankments in such geotechnical conditions.

Keywords: consolidation, settlement, coastal embankments, numerical methods, finite elements method

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Authors: Surita Maini, Sanjay Dhanka

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Machine learning (ML) involves developing algorithms and statistical models that enable computers to learn and make predictions or decisions based on data without being explicitly programmed. Because of its unlimited abilities ML is gaining popularity in medical sectors; Medical Imaging, Electronic Health Records, Genomic Data Analysis, Wearable Devices, Disease Outbreak Prediction, Disease Diagnosis, etc. In the last few decades, many researchers have tried to diagnose Breast Cancer (BC) using ML, because early detection of any disease can save millions of lives. Working in this direction, the authors have proposed a hybrid ML technique RBF SVM, to predict the BC in earlier the stage. The proposed method is implemented on the Breast Cancer UCI ML dataset with 569 instances and 32 attributes. The authors recorded performance metrics of the proposed model i.e., Accuracy 98.24%, Sensitivity 98.67%, Specificity 97.43%, F1 Score 98.67%, Precision 98.67%, and run time 0.044769 seconds. The proposed method is validated by K-Fold cross-validation.

Keywords: breast cancer, support vector classifier, machine learning, hyper parameter tunning

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Authors: C. Patrascioiu, Cao Minh Ahn

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The paper presents the research evolution in the propylene – propane distillation process, especially for the distillation columns equipped with heat pump. The paper is structured in three parts: separation of the propylene-propane mixture, steady state process modeling, and quality control systems. The first part is dedicated to state of art of the two distillation processes. The second part continues the author’s researches of the steady state process modeling. There has been elaborated a software simulation instrument that may be used to dynamic simulation of the process and to design the quality control systems. The last part presents the research of the control systems, especially for quality control systems.

Keywords: absorption, distillation, heat pump, Unisim design

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Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

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Authors: Yuanhao Gao, Pin Lin, Kees Weijer

Abstract:

In this paper, we will determine the external body force distribution with analysis of stokes fluid motion using mathematical modelling and numerical approaching. The body force distribution is regarded as the unknown variable and could be determined by the idea of optimal control theory. The Stokes flow motion and its velocity are generated by given forces in a unit square domain. A regularized objective functional is built to match the numerical result of flow velocity with the generated velocity data. So that the force distribution could be determined by minimizing the value of objective functional, which is also the difference between the numerical and experimental velocity. Then after utilizing the Lagrange multiplier method, some partial differential equations are formulated consisting the optimal control system to solve. Finite element method and conjugate gradient method are used to discretize equations and deduce the iterative expression of target body force to compute the velocity numerically and body force distribution. Programming environment FreeFEM++ supports the implementation of this model.

Keywords: optimal control model, Stokes equation, finite element method, conjugate gradient method

Procedia PDF Downloads 397

Authors: Indunil Jayatilake, Warna Karunasena

Abstract:

Fiber Reinforced Polymer (FRP) composites enjoy an array of applications ranging from aerospace, marine and military to automobile, recreational and civil industry due to their outstanding properties. A structural glass fiber reinforced polymer (GFRP) composite sandwich panel made from E-glass fiber skin and a modified phenolic core has been manufactured in Australia for civil engineering applications. One of the major mechanisms of damage in FRP composites is skin-core debonding. The presence of debonding is of great concern not only because it severely affects the strength but also it modifies the dynamic characteristics of the structure, including natural frequency and vibration modes. This paper deals with the investigation of the dynamic characteristics of a GFRP beam with single and multiple debonding by finite element based numerical simulations and analyses using the STRAND7 finite element (FE) software package. Three-dimensional computer models have been developed and numerical simulations were done to assess the dynamic behavior. The FE model developed has been validated with published experimental, analytical and numerical results for fully bonded as well as debonded beams. A comparative analysis is carried out based on a comprehensive parametric investigation. It is observed that the reduction in natural frequency is more affected by single debonding than the equally sized multiple debonding regions located symmetrically to the single debonding position. Thus it is revealed that a large single debonding area leads to more damage in terms of natural frequency reduction than isolated small debonding zones of equivalent area, appearing in the GFRP beam. Furthermore, the extents of natural frequency shifts seem mode-dependent and do not seem to have a monotonous trend of increasing with the mode numbers.

Keywords: debonding, dynamic response, finite element modelling, novel FRP beams

Procedia PDF Downloads 113

Authors: Yasser El-Henawy, M. Abd El-Kader, Gamal H. Moustafa

Abstract:

A theoretical study of a humidification dehumidification solar desalination unit has been carried out to increase understanding the effect of weather conditions on the unit productivity. A humidification-dehumidification (HD) solar desalination unit has been designed to provide fresh water for population in remote arid areas. It consists of solar water collector and air collector; to provide the hot water and air to the desalination chamber. The desalination chamber is divided into humidification and dehumidification towers. The circulation of air between the two towers is maintained by the forced convection. A mathematical model has been formulated, in which the thermodynamic relations were used to study the flow, heat and mass transfer inside the humidifier and dehumidifier. The present technique is performed in order to increase the unit performance. Heat and mass balance has been done and a set of governing equations has been solved using the finite difference technique. The unit productivity has been calculated along the working day during the summer and winter sessions and has compared with the available experimental results. The average accumulative productivity of the system in winter has been ranged between 2.5 to 4 kg/m2.day, while the average summer productivity has been found between 8 to 12 kg/m2 day.

Keywords: solar desalination, solar collector, humidification and dehumidification, simulation, finite difference, water productivity

Procedia PDF Downloads 408