Search results for: machine intelligence

Authors: Willy Mbenza, Sho Kenjiro

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Increasing efforts are directed towards a better understanding and foreknowledge of extreme precipitation likelihood, given the adverse effects associated with their occurrence. This knowledge plays a crucial role in long-term planning and the formulation of effective emergency response. However, predicting extreme events reliably presents a challenge to conventional empirical/statistics due to the involvement of numerous variables spanning different time and space scales. In the recent time, Machine Learning has emerged as a promising tool for predicting the dynamics of extreme precipitation. ML techniques enables the consideration of both local and regional physical variables that have a strong influence on the likelihood of extreme precipitation. These variables encompasses factors such as air temperature, soil moisture, specific humidity, aerosol concentration, among others. In this study, we develop an ML model that incorporates both local and regional variables while establishing a robust relationship between physical variables and precipitation during the downscaling process. Furthermore, the model provides valuable information on the frequency and duration of a given intensity of precipitation.

Keywords: machine learning (ML), predictions, rainfall events, regional variables

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Authors: Seyed Esmail Seyedi Bariran, Laysheng Ewe, Amy Ling

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Since supplier selection appears as a vital decision, selecting supplier based on the best and most accurate ways has a lot of importance for enterprises. In this study, a new Artificial Intelligence approach is exerted to remove weaknesses of supplier selection. The paper has three parts. First part is choosing the appropriate criteria for assessing the suppliers’ performance. Next one is collecting the data set based on experts. Afterwards, the data set is divided into two parts, the training data set and the testing data set. By the training data set the best structure of GEP and ANN are selected and to evaluate the power of the mentioned methods the testing data set is used. The result obtained shows that the accuracy of GEP is more than ANN. Moreover, unlike ANN, a mathematical equation is presented by GEP for the supplier selection.

Keywords: supplier selection, automotive supply chains, ANN, GEP

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Authors: Feng Yang

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Machine learning (ML), especially deep learning (DL), has been extensively applied to many applications in recently years and gained great success in solving different problems, including scientific problems. However, conventional ML/DL methodologies are purely data-driven which have the limitations, such as need of ample amount of labelled training data, lack of consistency to physical principles, and lack of generalizability to new problems/domains. Recently, there is a growing consensus that ML models need to further take advantage of prior knowledge to deal with these limitations. Physics-informed machine learning, aiming at integration of physics/domain knowledge into ML, has been recognized as an emerging area of research, especially in the recent 2 to 3 years. In this work, physics-informed ML, specifically physics-informed neural network (NN), is employed and implemented to estimate the displacements at x, y, z directions in a solid mechanics problem that is controlled by equilibrium equations with boundary conditions. By incorporating the physics (i.e. the equilibrium equations) into the learning process of NN, it is showed that the NN can be trained very efficiently with a small set of labelled training data. Experiments with different settings of the NN model and the amount of labelled training data were conducted, and the results show that very high accuracy can be achieved in fulfilling the equilibrium equations as well as in predicting the displacements, e.g. in setting the overall displacement of 0.1, a root mean square error (RMSE) of 2.09 × 10−4 was achieved.

Keywords: deep learning, neural network, physics-informed machine learning, solid mechanics

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Authors: Prakash Singh

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Being a collegial leader would require such a person to promote an organizational passion that identifies and acknowledges the contribution of every employee. Collegiality is about sharing responsibilities and being accountable for one’s actions. Leaders must therefore be equipped with the knowledge, skills, abilities, beliefs, and dispositions that will allow them to succeed in their organizations. These abilities should not only dwell on cognition alone, but also, equally, on the development of their emotional intelligence (EQ). It is therefore a myth that leaders are entrusted with absolute power to manage all the resources of their organizations. Workers feel confident with leaders who are adaptable, flexible and supportive when it comes to shared decision-making and the devolution of power within the organization. Research strongly supports the notion that a leader requires a high level of EQ in addition to IQ (cognitive intelligence) to achieve the goals of the organization. On the other hand, traditional managers require cognitive abilities and technical skills to get the work done by their employees. This does not imply that management is not important in organizations. However, the approach of managers becomes highly critical when the focus is purely task orientated. Enabling or empowering employees, therefore, is an important aspect in establishing emotionally intelligent collaboration, as the willing and satisfied participation of the employees can be the result of leaders’ commitment to establishing a collegial working environment as demonstrated by their behaviours. This paper therefore analyses why it matters for ideal leaders to be imbued with the traits of EQ and collegiality.

Keywords: collegiality, emotional intelligence, empowering employees, traditional managers

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Authors: Akshat Kumar, Vidushi

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This paper identifies 27 KOIs, 26 of which are currently classified as candidates and one as false positives that have a high probability of being confirmed. For this purpose, 11 machine learning algorithms were implemented on the cumulative kepler dataset sourced from the NASA exoplanet archive; it was observed that the best-performing model was HistGradientBoosting and XGBoost with a test accuracy of 93.5%, and the lowest-performing model was Gaussian NB with a test accuracy of 54%, to test model performance F1, cross-validation score and RUC curve was calculated. Based on the learned models, the significant characteristics for confirm exoplanets were identified, putting emphasis on the object’s transit and stellar properties; these characteristics were namely koi_count, koi_prad, koi_period, koi_dor, koi_ror, and koi_smass, which were later considered to filter out the potential KOIs. The paper also calculates the Earth similarity index based on the planetary radius and equilibrium temperature for each KOI identified to aid in their classification.

Keywords: Kepler objects of interest, exoplanets, space exploration, machine learning, earth similarity index, transit photometry

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Authors: Jarbas Lopes Cardoso Jr., Frederic Andres, Alexandre Guitton, Asanee Kawtrakul, Silvio E. Barbin

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The important objective of the CyberBrain Mass Agriculture Alarm Acquisition and Analysis (CBMa4) project is to minimize the impacts of diseases and disasters on rice cultivation. For example, early detection of insects will reduce the volume of insecticides that is applied to the rice fields through the use of CBMa4 platform. In order to reach this goal, two major factors need to be considered: (1) the social network of smart farmers; and (2) the warning data alarm acquisition and analysis component. This paper outlines the process for collecting the warning and improving the decision-making result to the warning. It involves two sub-processes: the warning collection and the understanding enrichment. Human sensors combine basic suitable data processing techniques in order to extract warning related semantic according to collective intelligence. We identify each warning by a semantic content called 'warncons' with multimedia metaphors and metadata related to these metaphors. It is important to describe the metric to measuring the relation among warncons. With this knowledge, a collective intelligence-based decision-making approach determines the action(s) to be launched regarding one or a set of warncons.

Keywords: agricultural engineering, warning systems, social network services, context awareness

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Authors: Aulia Fitri

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This aims to seek which part of the linguistics with the common mistakes occurred between Instagram translation and human translation. Instagram is a social media account that is widely used by people in the world. Everyone with the Instagram account can consume the captions and pictures that are shared by their friends, celebrity, and public figures across countries. Instagram provides the machine translation under its caption space that will assist users to understand the language of their non-native. The researcher takes samples from an Indonesian public figure whereas the account is followed by many followers. The public figure tries to help her followers from other countries understand her posts by putting up the English version after the Indonesian version. However, the research on Instagram account has not been done yet even though the account is widely used by the worldwide society. There are 20 samples that will be analysed on the perspective of translation quality and linguistics tools. As the MT, Instagram tends to give a literal translation without regarding the topic meant. On the other hand, the human translation tends to exaggerate the translation which leads a different meaning in English. This is an interesting study to discuss when the human nature and robotic-system influence the translation result.

Keywords: human translation, machine translation (MT), translation quality, linguistic tool

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Authors: Sungin Jeong

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This paper presents a linear oscillating generator of cylindrical type for hybrid electric vehicle application. The focus of the study is the suggestion of the optimal model and the design rule of the cylindrical linear oscillating generator with permanent magnet in the back-iron translator. The cylindrical topology is achieved using equivalent magnetic circuit considering leakage elements as initial modeling. This topology with permanent magnet in the back-iron translator is described by number of phases and displacement of stroke. For more accurate analysis of an oscillating machine, it will be compared by moving just one-pole pitch forward and backward the thrust of single-phase system and three-phase system. Through the analysis and comparison, a single-phase system of cylindrical topology as the optimal topology is selected. Finally, the detailed design of the optimal topology takes the magnetic saturation effects into account by finite element analysis. Besides, the losses are examined to obtain more accurate results; copper loss in the conductors of machine windings, eddy-current loss of permanent magnet, and iron-loss of specific material of electrical steel. The considerations of thermal performances and mechanical robustness are essential, because they have an effect on the entire efficiency and the insulations of the machine due to the losses of the high temperature generated in each region of the generator. Besides electric machine with linear oscillating movement requires a support system that can resist dynamic forces and mechanical masses. As a result, the fatigue analysis of shaft is achieved by the kinetic equations. Also, the thermal characteristics are analyzed by the operating frequency in each region. The results of this study will give a very important design rule in the design of linear oscillating machines. It enables us to more accurate machine design and more accurate prediction of machine performances.

Keywords: equivalent magnetic circuit, finite element analysis, hybrid electric vehicle, linear oscillating generator

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Authors: Seyed Sobhan Alvani, Mohammad Gohari

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By developing cities and increasing the population, traffic congestion has become a vital problem. Due to this crisis, urban designers try to present solutions to decrease this difficulty. On the other hand, predicting the model with perfect accuracy is essential for solution-providing. The current study presents a model based on artificial intelligence which can predict traffic index based on city population, growth rate, and area. The accuracy of the model was evaluated, which is acceptable and it is around 90%. Thus, urban designers and planners can employ it for predicting traffic index in the future to provide strategies.

Keywords: traffic index, population growth rate, cities wideness, artificial neural network

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Authors: Hamed Rahmani, Wim Groot

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The term "youth not in employment, education or training (NEET)" refers to a combination of youth unemployment and school dropout. This study investigates the variables that increase the risk of becoming NEET in Iran. A selection bias-adjusted Probit model was employed using machine learning to identify these risk factors. We used cross-sectional data obtained from the Statistical Centre of Iran and the Ministry of Cooperatives Labour and Social Welfare that was taken from the labour force survey conducted in the spring of 2021. We look at years of education, work experience, housework, the number of children under the age of six in the home, family education, birthplace, and the amount of land owned by households. Results show that hours spent performing domestic chores enhance the likelihood of youth becoming NEET, and years of education and years of potential work experience decrease the chance of being NEET. The findings also show that female youth born in cities were less likely than those born in rural regions to become NEET.

Keywords: NEET youth, probit, CART, machine learning, unemployment

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: C. E. Nugraheni, L. Abednego

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This paper is concerned with minimization of mean tardiness and flow time in a real single machine production scheduling problem. Two variants of genetic algorithm as meta-heuristic are combined with hyper-heuristic approach are proposed to solve this problem. These methods are used to solve instances generated with real world data from a company. Encouraging results are reported.

Keywords: hyper-heuristics, evolutionary algorithms, production scheduling, meta-heuristic

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Authors: Thiago Fabrizio Gavilano Farje, Marcelo Patricio Herrera Malpartida

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This study examines the influence of artificial intelligence (AI) on the work efficiency of administrative employees in the financial sector of Metropolitan Lima, Peru, during the year 2024. Focusing on the relationship between AI implementation and work efficiency, it addresses specific variables such as decision-making, motivation, and employee productivity. To accomplish the analysis between AI and work efficiency within the financial sector of Metropolitan Lima, it is necessary to evaluate how AI optimizes time in administrative tasks, examine how AI impacts the agility of the process of making decisions, and investigate the influence of AI on the satisfaction and motivation of employees. The research adopts a correlational and explanatory approach, designed to establish and understand the connections between AI and work efficiency. A survey design adapted from an OECD study is used, applying questionnaires to a representative sample of administrative workers in the financial sector who incorporate AI into their functions. The target population includes administrative workers in the financial sector of Metropolitan Lima, estimated at 73,097 employees based on data from the Censo Nacional de Empresas y Establecimientos and studies by the BCRP. The sample, selected through simple random sampling, comprises 246 workers.

Keywords: business management, artificial intelligence, decision making, labor efficiency, financial sector

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Authors: A. Ikhlef

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Image noise in computed tomography, is mainly caused by the statistical noise, system noise reconstruction algorithm filters. Since last few years, low dose x-ray imaging became more and more desired and looked as a technical differentiating technology among CT manufacturers. In order to achieve this goal, several technologies and techniques are being investigated, including both hardware (integrated electronics and photon counting) and software (artificial intelligence and machine learning) based solutions. From a hardware point of view, electronic noise could indeed be a potential driver for low and ultra-low dose imaging. We demonstrated that the reduction or elimination of this term could lead to a reduction of dose without affecting image quality. Also, in this study, we will show that we can achieve this goal using conventional electronics (low cost and affordable technology), designed carefully and optimized for maximum detective quantum efficiency. We have conducted the tests using large imaging objects such as 30 cm water and 43 cm polyethylene phantoms. We compared the image quality with conventional imaging protocols with radiation as low as 10 mAs (<< 1 mGy). Clinical validation of such results has been performed as well.

Keywords: computed tomography, electronic noise, scintillation detector, x-ray detector

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

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Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

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Authors: Titanilla Komenda

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Human-machine-collaboration defines a direct interaction between humans and machines to fulfil specific tasks. Those so-called collaborative machines are used without fencing and interact with humans in predefined workspaces. Even though, human-machine-collaboration enables a flexible adaption to variable degrees of freedom, industrial applications are rarely found. The reasons for this are not technical progress but rather limitations in planning processes ensuring safety for operators. Until now, humans and machines were mainly considered separately in the planning process, focusing on ergonomics and system performance respectively. Within human-machine-collaboration, those aspects must not be seen in isolation from each other but rather need to be analysed in interaction. Furthermore, a simulation model is needed that can validate the system performance and ensure the safety for the operator at any given time. Following on from this, a holistic simulation model is presented, enabling a simulative representation of collaborative tasks – including both, humans and machines. The presented model does not only include a geometry and a motion model of interacting humans and machines but also a numerical behaviour model of humans as well as a Boole’s probabilistic sensor model. With this, error scenarios can be simulated by validating system behaviour in unplanned situations. As these models can be defined on the basis of Failure Mode and Effects Analysis as well as probabilities of errors, the implementation in a collaborative model is discussed and evaluated regarding limitations and simulation times. The functionality of the model is shown on industrial applications by comparing simulation results with video data. The analysis shows the impact of considering human factors in the planning process in contrast to only meeting system performance. In this sense, an optimisation function is presented that meets the trade-off between human and machine factors and aids in a successful and safe realisation of collaborative scenarios.

Keywords: human-machine-system, human-robot-collaboration, safety, simulation

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Authors: Khiari Nor El Houda, Hiouani Amira Sarra

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Many EFL students with high capacities are hidden because they suffer from speaking anxiety (SA). Most of them find public speaking much demanding. They feel unable to communicate, they fear to make mistakes and they fear negative evaluation or being called on. With the growing number of the learners who suffer from foreign language speaking anxiety (FLSA), it is becoming increasingly difficult to ignore its harmful outcomes on their performance and success, especially during their first contact with the pupils, as they will be teaching in the near future. Different researchers suggested different ways to minimize the negative effects of FLSA. The present study sheds light on emotional intelligence skills training as an effective strategy not only to influence public speaking success but also to help pre-service EFL teachers lessen their speaking anxiety and eventually to prepare them for their professional career. A quasi-experiment was used in order to examine the research hypothesis. We worked with two groups of third-year EFL students at Oum El Bouaghi University. The Foreign Language Classroom Anxiety Scale (FLCAS) and the Emotional Quotient Inventory (EQ-i) were used to collect data about the participants’ FLSA and EI levels. The analysis of the data has yielded that the assumption that there is a negative correlation between EI and FLSA was statistically validated by the Pearson Correlation Test, concluding that, the more emotionally intelligent the individual is the less anxious s/he will be. In addition, the lack of amelioration in the results of the control group and the noteworthy improvement in the experimental group results led us to conclude that EI skills training was an effective strategy in minimizing the FLSA level and therefore, we confirmed our research hypothesis.

Keywords: emotional intelligence, emotional intelligence skills training, EQ-I, FLCAS, foreign language speaking anxiety, pre-service EFL teachers

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Authors: James V. Luisi

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Understanding consciousness requires a synthesis of ideas from multiple disciplines, including obvious ones like psychology, biology, evolution, neurology, and neuroscience, as well as less obvious ones like protozoology, botany, entomology, carcinology, herpetology, mammalogy, and computer sciences. Furthermore, to incorporate the necessary backdrop, it is best presented in a theme of Eastern philosophy, specifically leveraging the teachings of Buddhism for its relevance to early thought on consciousness. These ideas are presented as a multi-level framework that illustrates the various aspects of consciousness within a tapestry of foundational and dependent building blocks as to how living organisms evolved to understand elements of their reality sufficiently to survive, and in the case of Homo sapiens, eventually move beyond meeting the basic needs of survival, but to also achieve survival of the species beyond the eventual fate of our planet. This is not a complete system of thought, but just a framework of consciousness gathering some of the key elements regarding the evolution of consciousness and the advent of free will, and presenting them in a unique way that encourages readers to continue the dialog and thought process as an experience to enjoy long after reading the last page. Readers are encouraged to think for themselves about the issues raised herein and to question every facet presented, as much further exploration is needed. Needless to say, this subject will remain a rapidly evolving one for quite some time to come, and it is probably in the interests of everyone to at least consider attaining both an ability and willingness to participate in the dialog.

Keywords: consciousness, sentience, intelligence, artificial intelligence, Buddhism

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Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

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Authors: M. Pushparani, A. Sagaya

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Embedded systems have emerged as important elements in various domains with extensive applications in automotive, commercial, consumer, healthcare and transportation markets, as there is emphasis on intelligent devices. On the other hand, Business Intelligence (BI) has also been extensively used in a range of applications, especially in the agriculture domain which is the area of this research. The aim of this research is to create a framework for Embedded Weight Comparison Algorithm with Business Intelligence (EWCA-BI). The weight comparison algorithm will be embedded within the plantation management system and the weighbridge system. This algorithm will be used to estimate the weight at the site and will be compared with the actual weight at the plantation. The algorithm will be used to build the necessary alerts when there is a discrepancy in the weight, thus enabling better decision making. In the current practice, data are collected from various locations in various forms. It is a challenge to consolidate data to obtain timely and accurate information for effective decision making. Adding to this, the unstable network connection leads to difficulty in getting timely accurate information. To overcome the challenges embedding is done on a portable device that will have the embedded weight comparison algorithm to also assist in data capture and synchronize data at various locations overcoming the network short comings at collection points. The EWCA-BI will provide real-time information at any given point of time, thus enabling non-latent BI reports that will provide crucial information to enable efficient operational decision making. This research has a high potential in bringing embedded system into the agriculture industry. EWCA-BI will provide BI reports with accurate information with uncompromised data using an embedded system and provide alerts, therefore, enabling effective operation management decision-making at the site.

Keywords: embedded business intelligence, weight comparison algorithm, oil palm plantation, embedded systems

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Authors: Summer Jackson

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September 11, 2001 terrorist attacks exposed weaknesses in federal law enforcement’s ability to proactively counter threats to American homeland security. Following the attacks, legislative reforms and policy changes cleared both bureaucratic and legal obstacles to anti-terrorism efforts. The Federal Bureau of Investigation (FBI) transformed into a domestic intelligence agency responsible for preventing future terrorist attacks. Likewise, the passage of the 2001 USA Patriot Act gave federal agents new discretionary powers to more easily collect intelligence on those suspected of supporting terrorism. Despite these changes, there has been only limited scholarly attention paid to terrorism responses by the federal criminal justice system. This study sought to examine the investigative and prosecutorial changes made in the Post-9/11 era. The methodology employed bivariate and multivariate statistics using data from the American Terrorism Study (ATS). This analysis examined how policy changes are reflected in the nature of terrorism investigations, the handling of terrorist defendants by federal prosecutors, and the outcomes of terrorism cases since 2001. The findings indicate significant investigative and prosecutorial changes in the Post-9/11 era. Specifically, this study found terrorism cases involved younger defendants, fewer indictees per case, less use of human intelligence, less complicated attacks, less serious charges, and more plea bargains. Overall, this study highlights the important shifts in responses to terrorism following the 9/11 attacks.

Keywords: terrorism, law enforcement, post-9/11, federal policy

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Authors: Jean Pierre Ribeiro Daquila

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This paper aims to analyze the use of the Theory of Multiple Intelligences as a tool to facilitate students’ learning. This theory, proposed by the American psychologist and educator Howard Gardner, was first established in 1983 and advocates that human beings possess eight intelligence and not only one, as defended by psychologists prior to his theory. These intelligence are bodily-kinesthetic intelligence, musical, linguistic, logical-mathematical, spatial, interpersonal, intrapersonal, and naturalist. This paper will focus on bodily-kinesthetic intelligence. Spatial and bodily-kinesthetic intelligences are sensed by athletes, dancers, and others who use their bodies in ways that exceed normal abilities. These are intelligences that are closely related. A quarterback or a ballet dancer needs to have both an awareness of body motions and abilities as well as a sense of the space involved in the action. Nevertheless, there are many reasons which make classical ballet dance more integrated with other intelligences. Ballet dancers make it look effortless as they move across the stage, from the lifts to the toe points; therefore, there is acting both in the performance of the repertoire and in hiding the pain or physical stress. The ballet dancer has to have great mathematical intelligence to perform a fast allegro; for instance, each movement has to be executed in a specific millisecond. Flamenco dancers need to rely as well on their mathematic abilities, as the footwork requires the ability to make half, two, three, four or even six movements in just one beat. However, the precision of the arm movements is freer than in ballet dance; for this reason, ballet dancers need to be more holistically aware of their movements; therefore, our experiment will test whether this greater attention required by ballet dancers makes them acquire better results in the training sessions when compared to flamenco dancers. An experiment will be carried out in this study by training ballet dancers through dance (four years of experience dancing minimum – experimental group 1); a group of flamenco dancers (four years of experience dancing minimum – experimental group 2). Both experimental groups will be trained in two different domains – phonetics and chemistry – to examine whether there is a significant improvement in these areas compared to the control group (a group of regular students who will receive the same training through a traditional method). However, this paper will focus on phonetic training. Experimental group 1 will be trained with the aid of classical music plus bodily work. Experimental group 2 will be trained with flamenco rhythm and kinesthetic work. We would like to highlight that this study takes dance as an example of a possible area of strength; nonetheless, other types of arts can and should be used to support students, such as drama, creative writing, music and others. The main aim of this work is to suggest that other intelligences, in the case of this study, bodily-kinesthetic, can be used to help improve pronunciation.

Keywords: multiple intelligences, pronunciation, effective pronunciation trainings, short drills, musical intelligence, bodily-kinesthetic intelligence

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Authors: Kwaku Damoah

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This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

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Authors: Seongjun Kim, Jinwooung Kim, Sung-Ah Kim

Abstract:

The development of information and communication technology also affects human cognition and thinking, especially in the field of design, new techniques are being tried. In architecture, new design methodologies such as machine learning or data-driven design are being applied. In particular, these methodologies are used in analyzing the factors related to the value of real estate or analyzing the feasibility in the early planning stage of the apartment housing. However, since the value of apartment buildings is often determined by external factors such as location and traffic conditions, rather than the interior elements of buildings, data is rarely used in the design process. Therefore, although the technical conditions are provided, the internal elements of the apartment are difficult to apply the data-driven design in the design process of the apartment. As a result, the designers of apartment housing were forced to rely on designer experience or modular design alternatives rather than data-driven design at the design stage, resulting in a uniform arrangement of space in the apartment house. The purpose of this study is to propose a methodology to support the designers to design the apartment unit plan with high consumer preference by deriving the correlation and importance of the floor plan elements of the apartment preferred by the consumers through the machine learning and reflecting this information from the early design process. The data on the pre-sale competition rate and the elements of the floor plan are collected as data, and the correlation between pre-sale competition rate and independent variables is analyzed through machine learning. This analytical model can be used to review the apartment unit plan produced by the designer and to assist the designer. Therefore, it is possible to make a floor plan of apartment housing with high preference because it is possible to feedback apartment unit plan by using trained model when it is used in floor plan design of apartment housing.

Keywords: apartment unit plan, data-driven design, design methodology, machine learning

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Authors: Dharmendra Jariwala, Robin Christian

Abstract:

Thermal exposure is a common problem in every manufacturing industry where heat is used in the manufacturing process. In developing countries like India, a lack of awareness regarding the proper work environmental condition is observed among workers. Improper planning of factory building, arrangement of machineries, ventilation system, etc. play a vital role in the rise of temperature within the manufacturing areas. Due to the uncontrolled thermal stress, workers may be subjected to various heat illnesses from mild disorder to heat stroke. Heat stress is responsible for the health risk and reduction in production. Wet Bulb Globe Temperature (WBGT) index and relative humidity are used to evaluate heat stress conditions. WBGT index is a weighted average of natural wet bulb temperature, globe temperature, dry bulb temperature, which are measured with standard instrument QuestTemp 36 area stress monitor. In this study textile processing units have been selected in the industrial estate in the Surat city. Based on the manufacturing process six locations were identified within the plant at which process was undertaken at 120°C to 180°C. These locations were jet dying machine area, stenter machine area, printing machine, looping machine area, washing area which generate process heat. Office area was also selected for comparision purpose as a sixth location. Present Study was conducted in the winter season and summer season for day and night shift. The results shows that average WBGT index was found above Threshold Limiting Value (TLV) during summer season for day and night shift in all three industries except office area. During summer season highest WBGT index of 32.8°C was found during day shift and 31.5°C was found during night shift at printing machine area. Also during winter season highest WBGT index of 30°C and 29.5°C was found at printing machine area during day shift and night shift respectively.

Keywords: relative humidity, textile industry, thermal stress, WBGT

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Authors: Pacharaporn Lekyan

Abstract:

The purpose of this study is to explore the conflict management affecting the workplace and analyze the ability of the prediction of leadership of the headman and the methods to handle the conflict in an organization. The quantitative research and developed the questionnaire in order to collect information from the respondents from 200 samples from leader or manager who worked in frozen food factories in Thailand. The result analysis shows about the problem of the relationship between conflict management factors, leadership, and the confliction in organization. The emotion of the leader in the organization is not the only factor that can affect conflict management but also the emotion of surrounding people which this factor can happen all the time and shows that four out of five factors of interpersonal conflict management have affected on emotion intelligence and also shows that the behaviors of leadership have an influence on conflict management.

Keywords: conflict management, emotional intelligence, leadership, factories' employee

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Authors: Thomas Schalow

Abstract:

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

Keywords: artificial intelligence, CETI, communication, culture, language

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Authors: Newton Muhury, Armando A. Apan, Tek N. Marasani, Gebiaw T. Ayele

Abstract:

The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

Procedia PDF Downloads 81