Search results for: learning difficulties

Authors: D. Hişam, S. İkizoğlu

Abstract:

Identifying the problem behind balance disorder is one of the most interesting topics in medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three ML models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest (RF) Classifier was the most accurate model.

Keywords: Vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting.

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluates the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: lexical semantics, feature representation, semantic decision, convolutional neural network, electronic medical record

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Authors: K´evin Fernagut, Olivier Flauzac, Erick M. Gallegos R, Florent Nolot

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The adoption of modern lightweight virtualization often comes with new threats and network vulnerabilities. This paper seeks to assess this with a different approach studying the behavior of a testbed built with tools such as Kernel-based Virtual Machine (KVM), LinuX Containers (LXC) and Docker, by performing stress tests within a platform where students experiment simultaneously with cyber-attacks, and thus observe the impact on the campus network and also find the best solution for cyber-security learning. Interesting outcomes can be found in the literature comparing these technologies. It is, however, difficult to find results of the effects on the global network where experiments are carried out. Our work shows that other physical hosts and the faculty network were impacted while performing these trials. The problems found are discussed, as well as security solutions and the adoption of new network policies.

Keywords: Containerization, containers, cyber-security, cyber-attacks, isolation, performance, security, virtualization, virtual machines.

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Authors: L. Mortari, A. Bevilacqua, R. Silva

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The experience presented in this paper aims to understand how educational research can support the introduction and optimization of teaching innovations in legal education. In this increasingly complex context, a strong need to introduce paths aimed at acquiring not only professional knowledge and skills but also reflective, critical and problem-solving skills emerges. Through a peer observation intertwined with an analysis of discursive practices, researchers and the teacher worked together through a process of participatory and transformative accompaniment whose objective was to promote the active participation and engagement of students in learning processes, an element indispensable to work in the more specific direction of strengthening key competences. This reflective faculty development path led the teacher to activate metacognitive processes, becoming thus aware of the strengths and areas of improvement of his teaching innovation.

Keywords: Discursive analysis, faculty development, legal education, peer observation, teaching innovation.

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Authors: Nripjit, Anand K Tyagi, Nirmal Singh

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The present work consecutively on synthesis and characterization of composites, Al/Al alloy A 384.1 as matrix in which the main ingredient as Al/Al-5% MgO alloy based metal matrix composite. As practical implications the low cost processing route for the fabrication of Al alloy A 384.1 and operational difficulties of presently available manufacturing processes based in liquid manipulation methods. As all new developments, complete understanding of the influence of processing variables upon the final quality of the product. And the composite is applied comprehensively to the acquaintance for achieving superiority of information concerning the specific heat measurement of a material through the aid of thermographs. Products are evaluated concerning relative particle size and mechanical behavior under tensile strength. Furthermore, Taguchi technique was employed to examine the experimental optimum results are achieved, owing to effectiveness of this approach.

Keywords: MMC, Thermographs, Tensile strength, Taguchi technique, Optimal parameters

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Authors: Y. A. Adla, R. Soubra, M. Kasab, M. O. Diab, A. Chkeir

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Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals out of which 11 were chosen based on their Intraclass Correlation Coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, five features were introduced to the Linear Discriminant Analysis classifier and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90% respectively.

Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification

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Authors: Khalid Ghoulam, Belaid Bouikhalene, Zakaria Harmouch, Hicham Mouncif

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The goal of engineering education is to prepare students to cope with problems of real devices and systems. Usually there are not enough devices or time for conducting experiments in a real lab. Other factors that prevent the use of lab devices directly by students are inaccessible or dangerous phenomena, or polluting chemical reactions. The technology brings additional strategies of learning and teaching, there are two types of online labs, virtual and remote labs RL. We present an example of a successful development and deployment of a remote lab in the field of engineering education, integrated in the Moodle platform, using very low-coast, high documented devices and free software. The remote lab is user friendly for both teachers and students. Our web 2.0 based user interface would attract and motivate students, as well as solving the problem of larger classes and expensive lab devices.

Keywords: Remote lab, online learning, Moodle, Arduino, SMSU, lab experimentation, engineering education, online engineering education.

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Authors: R. Sridevi, G. Jagajothi

Abstract:

Artificial Immune Systems (AIS), inspired by the human immune system, are algorithms and mechanisms which are self-adaptive and self-learning classifiers capable of recognizing and classifying by learning, long-term memory and association. Unlike other human system inspired techniques like genetic algorithms and neural networks, AIS includes a range of algorithms modeling on different immune mechanism of the body. In this paper, a mechanism of a human immune system based on apoptosis is adopted to build an Intrusion Detection System (IDS) to protect computer networks. Features are selected from network traffic using Fisher Score. Based on the selected features, the record/connection is classified as either an attack or normal traffic by the proposed methodology. Simulation results demonstrates that the proposed AIS based on apoptosis performs better than existing AIS for intrusion detection.

Keywords: Apoptosis, Artificial Immune System (AIS), Fisher Score, KDD dataset, Network intrusion detection.

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Authors: Revanth Sai Kosaraju, Michael Ramscar, Melody Dye

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Accounts of language acquisition differ significantly in their treatment of the role of prediction in language learning. In particular, nativist accounts posit that probabilistic learning about words and word sequences has little to do with how children come to use language. The accuracy of this claim was examined by testing whether distributional probabilities and frequency contributed to how well 3-4 year olds repeat simple word chunks. Corresponding chunks were the same length, expressed similar content, and were all grammatically acceptable, yet the results of the study showed marked differences in performance when overall distributional frequency varied. It was found that a distributional model of language predicted the empirical findings better than a number of other models, replicating earlier findings and showing that children attend to distributional probabilities in an adult corpus. This suggested that language is more prediction-and-error based, rather than on abstract rules which nativist camps suggest.

Keywords: Abstractness, child psychology, language acquisition, prediction and error.

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Authors: Smko Zangana, Ergun Ercelebi

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The alternating current is the main power in all industries and other aspects especially for the short and mid distances, but as far as long a distance which exceeds 500 KMs, using the alternating current technically will face many difficulties and more costs because it's difficult to control the current and also other restrictions. Therefore, recently those reasons led to building transmission lines HVDC to transmit power for long distances. This document presents technical comparison and assessments for power transmission system among distances either ways and studying the stability of the system regarding the proportion of losses in the actual power sent and received between both sides in different systems and also categorizing filters used in the HVDC system and its impact and effect on reducing Harmonic in the power transmission. MATLAB /Simulink simulation software is used to simulate both HVAC & HVDC power transmission system topologies.

Keywords: HVAC power system, HVDC power system, power system simulation (MATLAB), the alternating current, voltage stability.

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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: Alisha Fisher

Abstract:

Healing from sexual trauma can be a tumultuous process, filled with loneliness, confusion, and many unknowns or surprising road bumps. Survivors of sexual violence will often battle with the post traumatic difficulties following the trauma, some of which involve struggles with reconnecting with sexual pleasure. The goal of this paper is to analyze various papers to identify if there is a connection to survivors navigating the symptom of dissociation during self-sexual pleasuring care through grounding and mindfulness strategies. We conclude that there can be benefits to engaging in strategies of grounding and mindfulness can bring a level of presence to survivor’s mind, and body that can assist with reducing the anxieties and dissociation events dur solo sexual play. As such, service providers of survivors of sexual violence should be discussing the options of solo masturbation experiences mixed with grounding and mindfulness processes for survivors to heal and re-claim their sexual lifestyles.

Keywords: Masturbation healing, sexual violence survivor, survivor healing, survivor masturbation.

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Authors: Rahib Hidayat Abiyev

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This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The neuro-fuzzy system is used for the identification and control of nonlinear dynamic plant. The simulation results of identification and control systems based on recurrent neuro-fuzzy network are compared with the simulation results of other neural systems. It is found that the recurrent neuro-fuzzy based system has better performance than the others.

Keywords: Fuzzy logic, neural network, neuro-fuzzy system, control system.

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Authors: Chonmapat Torasa

Abstract:

This paper presents the performance of electricity generation and consumption from solar generator installed at Rajabhat Suan Sunandha’s learning center in Samutsongkram. The result from the experiment showed that solar cell began to work and distribute the current into the system when the solar energy intensity was 340 w/m2, starting from 8:00 am to 4:00 pm (duration of 8 hours). The highest intensity read during the experiment was 1,051.64w/m2. The solar power was 38.74kWh/day. The electromotive force from solar cell averagely was 93.6V. However, when connecting solar cell with the battery charge controller system, the voltage was dropped to 69.07V. After evaluating the power distribution ability and electricity load of tested solar cell, the result showed that it could generate power to 11 units of 36-watt fluorescent lamp bulbs, which was altogether 396W. In the meantime, the AC to DC power converter generated 3.55A to the load, and gave 781VA.

Keywords: Solar Cell, Solar-cell power generating system.

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Authors: Siyao Zhu, Yifang Xu

Abstract:

After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. Brain-computer interface is a promising option to overcome the limitations of tedious manual control and operation of robots in the urgent search-and-rescue tasks. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: Consensus assessment, electroencephalogram, EEG, emergency response, human-robot collaboration, intention recognition, search and rescue.

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Authors: Oke Oktavianty, Tadayuki Kyoutani, Shigeyuki Haruyama, Junji Kaneko, Ken Kaminishi

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Modelica has many advantages and it is very useful in modeling and simulation especially for the multi-domain with a complex technical system. However, the big obstacle for a beginner is to understand the basic concept and to build a new system model for a real system. In order to understand how to solve the simple circuit model by hand translation and to get a better understanding of how modelica works, we provide a detailed explanation about solver ordering system in horizontal and vertical sorting and make some proposals for improvement. In this study, some difficulties in using modelica software with the original concept and the comparison with Finite Element Method (FEM) approach is discussed. We also present our textual modeling approach using FEM concept for acausal and causal model construction. Furthermore, simulation results are provided that demonstrate the comparison between using textual modeling with original coding in modelica and FEM concept.

Keywords: FEM, acausal model, modelica, horizontal and vertical sorting.

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Authors: Quratulain N. Rajput, Sajjad Haider

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This paper applies Bayesian Networks to support information extraction from unstructured, ungrammatical, and incoherent data sources for semantic annotation. A tool has been developed that combines ontologies, machine learning, and information extraction and probabilistic reasoning techniques to support the extraction process. Data acquisition is performed with the aid of knowledge specified in the form of ontology. Due to the variable size of information available on different data sources, it is often the case that the extracted data contains missing values for certain variables of interest. It is desirable in such situations to predict the missing values. The methodology, presented in this paper, first learns a Bayesian network from the training data and then uses it to predict missing data and to resolve conflicts. Experiments have been conducted to analyze the performance of the presented methodology. The results look promising as the methodology achieves high degree of precision and recall for information extraction and reasonably good accuracy for predicting missing values.

Keywords: Information Extraction, Bayesian Network, ontology, Machine Learning

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Authors: Akella Amarendra Babu, Rama Devi Yellasiri, Natukula Sainath

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Where human beings can easily learn and adopt pronunciation variations, machines need training before put into use. Also humans keep minimum vocabulary and their pronunciation variations are stored in front-end of their memory for ready reference, while machines keep the entire pronunciation dictionary for ready reference. Supervised methods are used for preparation of pronunciation dictionaries which take large amounts of manual effort, cost, time and are not suitable for real time use. This paper presents an unsupervised adaptation model for building agile and dynamic pronunciation dictionaries online. These methods mimic human approach in learning the new pronunciations in real time. A new algorithm for measuring sound distances called Dynamic Phone Warping is presented and tested. Performance of the system is measured using an adaptation model and the precision metrics is found to be better than 86 percent.

Keywords: Pronunciation variations, dynamic programming, machine learning, natural language processing.

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Authors: R.Mostafaei, A.Habiboghli, M.R.Meybodi

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In distributed resource allocation a set of agents must assign their resources to a set of tasks. This problem arises in many real-world domains such as distributed sensor networks, disaster rescue, hospital scheduling and others. Despite the variety of approaches proposed for distributed resource allocation, a systematic formalization of the problem, explaining the different sources of difficulties, and a formal explanation of the strengths and limitations of key approaches is missing. We take a step towards this goal by using a formalization of distributed resource allocation that represents both dynamic and distributed aspects of the problem. In this paper we present a new idea for target tracking in sensor networks and compare it with previous approaches. The central contribution of the paper is a generalized mapping from distributed resource allocation to DDCSP. This mapping is proven to correctly perform resource allocation problems of specific difficulty. This theoretical result is verified in practice by a simulation on a realworld distributed sensor network.

Keywords: Distributed CSP, Target Tracking, Sensor Network

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Authors: Yun-Husan Huang

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Many English for Specific Purposes (ESP) textbooks are distributed globally as the content development is often obliged to compromises between commercial and pedagogical demands. Therefore, the issue of regional application and usefulness of globally published ESP textbooks has received much debate. For ESP instructors, textbook selection is definitely a priority consideration for curriculum design. An appropriate ESP textbook can facilitate teaching and learning, while an inappropriate one may cause a disaster for both teachers and students. This study aims to investigate the regional application and usefulness of an ESP textbook for information technology (IT). Participants were 51 sophomores majoring in Applied Informatics and Multimedia at a university in Taiwan. As they were non-English majors, their English proficiency was mostly at elementary and elementary-to-intermediate levels. This course was offered for two semesters. The textbook selected was Oxford English for Information Technology. At class end, the students were required to complete a survey comprising five choices of Very Easy, Easy, Neutral, Difficult, and Very Difficult for each item. Based on the content design of the textbook, the survey investigated how the students viewed the difficulty of grammar, listening, speaking, reading, and writing materials of the textbook. In terms of difficulty, results reveal that only 22% of them found the grammar section difficult and very difficult. For listening, 71% responded difficult and very difficult. For general reading, 55% responded difficult and very difficult. For speaking, 56% responded difficult and very difficult. For writing, 78% responded difficult and very difficult. For advanced reading, 90% reported difficult and very difficult. These results indicate that, except the grammar section, more than half of the students found the textbook contents difficult in terms of listening, speaking, reading, and writing materials. Such contradictory results between the easy grammar section and the difficult four language skills sections imply that the textbook designers do not well understand the English learning background of regional ESP learners. For the participants, the learning contents of the grammar section were the general grammar level of junior high school, while the learning contents of the four language skills sections were more of the levels of college English majors. Implications from the findings are obtained for instructors and textbook designers. First of all, existing ESP textbooks for IT are few and thus textbook selections for instructors are insufficient. Second, existing globally published textbooks for IT cannot be applied to learners of all English proficiency levels, especially the low level. With limited textbook selections, third, instructors should modify the selected textbook contents or supplement extra ESP materials to meet the proficiency level of target learners. Fourth, local ESP publishers should collaborate with local ESP instructors who understand best the learning background of their students in order to develop appropriate ESP textbooks for local learners. Even though the instructor reduced learning contents and simplified tests in curriculum design, in conclusion, the students still found difficult. This implies that in addition to the instructor’s professional experience, there is a need to understand the usefulness of the textbook from learner perspectives.

Keywords: ESP textbooks, ESP materials, ESP textbook design, learner perspectives on ESP textbooks.

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Authors: Ahmad Rouhani, Masoud Jabbari, Sima Honarmand

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This paper introduces a method to optimal design of a hybrid Wind/Photovoltaic/Fuel cell generation system for a typical domestic load that is not located near the electricity grid. In this configuration the combination of a battery, an electrolyser, and a hydrogen storage tank are used as the energy storage system. The aim of this design is minimization of overall cost of generation scheme over 20 years of operation. The Matlab/Simulink is applied for choosing the appropriate structure and the optimization of system sizing. A teaching learning based optimization is used to optimize the cost function. An overall power management strategy is designed for the proposed system to manage power flows among the different energy sources and the storage unit in the system. The results have been analyzed in terms of technical and economic. The simulation results indicate that the proposed hybrid system would be a feasible solution for stand-alone applications at remote locations.

Keywords: Hybrid energy system, optimum sizing, power management, TLBO.

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: Deep learning, long-short-term memory, energy, renewable energy load forecasting.

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Authors: R. Sharma, M. Gopal

Abstract:

This paper presents a new problem solving approach that is able to generate optimal policy solution for finite-state stochastic sequential decision-making problems with high data efficiency. The proposed algorithm iteratively builds and improves an approximate Markov Decision Process (MDP) model along with cost-to-go value approximates by generating finite length trajectories through the state-space. The approach creates a synergy between an approximate evolving model and approximate cost-to-go values to produce a sequence of improving policies finally converging to the optimal policy through an intelligent and structured search of the policy space. The approach modifies the policy update step of the policy iteration so as to result in a speedy and stable convergence to the optimal policy. We apply the algorithm to a non-holonomic mobile robot control problem and compare its performance with other Reinforcement Learning (RL) approaches, e.g., a) Q-learning, b) Watkins Q(λ), c) SARSA(λ).

Keywords: Markov Decision Process (MDP), Mobile robot, Policy iteration, Simulation.

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Authors: Cristina-Maria Păcurar

Abstract:

The paper presents an original Python-based application that outlines the advantages of combining some elementary notions of mathematics with the study of a programming language. The application support refers to some of the first lessons of analytic geometry, meaning conics and quadrics and their reduction to a standard form, as well as some related notions. The chosen programming language is Python, not only for its closer to an everyday language syntax – and therefore, enhanced readability – but also for its highly reusable code, which is of utmost importance for a mathematician that is accustomed to exploit already known and used problems to solve new ones. The purpose of this paper is, on one hand, to support the idea that one of the most appropriate means to initiate one into programming is throughout mathematics, and reciprocal, one of the most facile and handy ways to assimilate some basic knowledge in the study of mathematics is to apply them in a personal project. On the other hand, besides being a mean of learning both programming and analytic geometry, the application subject to this paper is itself a useful tool for it can be seen as an independent original Python package for analytic geometry.

Keywords: Analytic geometry, conics, Python programming language, quadrics.

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Authors: Aldy Gunawan, Kien Ming Ng, Kim Leng Poh

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This paper presents a hybrid algorithm for solving a timetabling problem, which is commonly encountered in many universities. The problem combines both teacher assignment and course scheduling problems simultaneously, and is presented as a mathematical programming model. However, this problem becomes intractable and it is unlikely that a proven optimal solution can be obtained by an integer programming approach, especially for large problem instances. A hybrid algorithm that combines an integer programming approach, a greedy heuristic and a modified simulated annealing algorithm collaboratively is proposed to solve the problem. Several randomly generated data sets of sizes comparable to that of an institution in Indonesia are solved using the proposed algorithm. Computational results indicate that the algorithm can overcome difficulties of large problem sizes encountered in previous related works.

Keywords: Timetabling problem, mathematical programming model, hybrid algorithm, simulated annealing.

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Authors: Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C. Ardil

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Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuzzy weight sets (QFWS) using repeated simulation of the crisp neural network. Such type of fuzzy weight functions may be applied where we have multivariate crisp input that needs to be adjusted after iterative learning, like claim amount distribution analysis. As real data is subjected to noise and uncertainty, therefore, QFWS may be helpful in the simplification of such complex problems. Secondly, these QFWS provide good initial solution for training of fuzzy neural networks with reduced computational complexity.

Keywords: Crisp neural networks, fuzzy systems, extraction of logical rules, quasi-fuzzy numbers.

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Authors: S. Chee Choy, Pauline Swee Choo Goh, Yow Lin Liew

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The purpose of this study is to evaluate the English version and a Malay translation of the 21-item Learner Awareness Questionnaire for its application to assess student learning in higher education. The Learner Awareness Questionnaire, originally written in English, is a quantitative measure of how and why students learn. The questionnaire gives an indication of the process and motives to learn using four scales: survival, establishing stability, approval and loving to learn. Data in the present study came from 680 university students enrolled in various programmes in Malaysia. The Malay version of the questionnaire supported a similar four factor structure and internal consistency to the English version. The four factors of the Malay version also showed moderate to strong correlations with those of the English versions. The results suggest that the Malay version of the questionnaire is similar to the English version. However, further refinement to the questions is needed to strengthen the correlations between the two questionnaires.

Keywords: Student learning, learner awareness, instrument validation.

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Authors: Arun Prasad Jaganathan

Abstract:

In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper presents 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: Swadhin Kumar Mishra, Sidhartha Panda, C. Ardil

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Multiple-Input-Multiple-Output (MIMO) is one of the most important communication techniques that allow wireless systems to achieve higher data rate. To overcome the practical difficulties in implementing Dirty Paper Coding (DPC), various suboptimal MIMO Broadcast (MIMO-BC) scheduling algorithms are employed which choose the best set of users among all the users. In this paper we discuss such a sub-optimal MIMO-BC scheduling algorithm which employs antenna selection at the receiver side. The channels for the users considered here are not Identical and Independent Distributed (IID) so that users at the receiver side do not get equal opportunity for communication. So we introduce a method of applying weights to channels of the users which are not IID in such a way that each of the users gets equal opportunity for communication. The effect of weights on overall sum-rate achieved by the system has been investigated and presented.

Keywords: Antenna selection, Identical and Independent Distributed (IID), Sum-rate capacity, Weighted sum rate.

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Authors: Towards Better Quality in Healthcare, Operations Management: A Developmental Literature Review

Abstract:

This work presents the various perspectives, dimensions, components and definitions given to quality in the operations management (OM) and healthcare services (HCS) literature in time, highlighting gaps and learning opportunities between the two disciplines through a thorough search into their rich and distinct body of knowledge. Greater and new insights about the general nature of quality are obtained with findings such as in OM, quality has been approached in six fairly distinct paradigms (excellence, value, conformity to specifications, attributes, satisfaction and meeting or exceeding customer expectations), whereas in HCS, two approaches are prominent (Donabedian’s structure, process and outcomes model and Lohr and Schroeder’s circumscribed definition). The two disciplines views on quality seem to have progressed much in parallel with little cross-learning from each other. This work then proposes an encompassing definition of quality as a lever and suggests further research and development avenues for a better use of the concept of quality by academics and practitioners alike toward the goals of greater organizational performance and improved management in healthcare and possibly other service domains.

Keywords: Healthcare, management, operations, quality, services.

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