Forthcoming and Online First Articles

International Journal of Applied Systemic Studies

International Journal of Applied Systemic Studies (IJASS)

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International Journal of Applied Systemic Studies (6 papers in press)

Regular Issues

  • Key barriers in the growth of engineering education in the context of Chhattisgarh state: a Fuzzy Kano and TISM integrated approach   Order a copy of this article
    by NAVEEN JAIN, Prateek Sharma, Bhagwaticharan Patel 
    Abstract: Today, countries depend on knowledge-driven innovation for economic growth which depends on the quality of the engineering education system and management. In India, the All India Council for Technical Education is the flag bearer of planning, formulation, and dissemination of technical education in the country. Since 20122013, there is a drastic decline in admissions in engineering courses across the country. Hence, there is a need to identify the barriers that dominate engineering education and hinder the students from opting for engineering as a career option. The proposed work focuses on determining the key barriers to the growth of technical education in the state of Chhattisgarh by an integrated fuzzy Kano model and total interpretive structural modelling approach. The result of the study will provide deeper insight and a better understanding of the higher engineering scenario in the state and will help decision-makers to take constructive and progressive steps to improve the engineering education scenario.
    Keywords: Kano model; total interpretive structural modelling; TISM; fuzzy Kano; engineering education management; key barriers; interpretive structural modelling; ISM; technical education; critical failure factors; CFFs; reachability matrix; HEI; digraph.
    DOI: 10.1504/IJASS.2023.10047241
     
  • Sentiment analysis of positive and negative comments, extracted from social networks and web in Albanian language   Order a copy of this article
    by Mërgim H. Hoti, Hamdi Hoti, Edisona Kurhasku 
    Abstract: Nowadays, massive texts that contain a variety of viewpoints, attitudes, and emotions for products and services are generated on the web. Sentiment analysis is one of the fastest-growing research areas in computer science, making it difficult to keep track of all activities in the area, including data we made in social networks, the web as well as communicating with each other, and so on. This paper explains sentiment analysis of positive and negative comments taken from social networks and news sites of low resources languages such as Albanian. These comments are taken from different topics and are analysed with a support vector machine, using Sklearn and other algorithms. As we know, social networks and online communication involve both positive and negative relationships between each other's. This research measures and shows how people are prone to express positive or negative opinions about any important case. Being aware of these problems, the edge sign prediction problem that aims to predict whether an interaction between a pair of nodes will be positive or negative should be considered. Therefore, this study provides theoretical results regarding this problem that motivates natural improvements.
    Keywords: sentiment analysis; predictable data; Albanian language.
    DOI: 10.1504/IJASS.2024.10063597
     
  • Life cycle development and approach of management consulting projects   Order a copy of this article
    by Renato Lopes da Costa, Leandro F. Pereira, Álvaro Dias, Rui Gonçalves, Rui Vinhas da Silva, Natália Teixeira 
    Abstract: Management consulting projects are nowadays important support for companies that face a very difficult context and the mandatory step to change their business model to adapt and survive. However, the subject of this emerging area is not well studied and there is a lack of scientific literature. This research aims to develop a detail model of steps and objectives that must be implicit in the development of a project in management consulting. The results of the empirical analysis in the form of semi-structured interviews and questionnaires applied to management consultants and SME managers in Portugal reveal that a project in management consulting should be developed in nine steps starting from the pre-proposal and finishing in the measurement of five stages of development, including identification, characterisation, formulation, implementation, and evaluation.
    Keywords: management consulting; strategy-as-practice; SAP; business model; change.
    DOI: 10.1504/IJASS.2024.10063598
     
  • A new risk evaluation method for supply chain based on convolution neural network   Order a copy of this article
    by Huanle Han, Lianguang Mo 
    Abstract: In the traditional supply chain risk assessment methods, the significance of selecting evaluation indexes is low, which leads to the problems of low fitting and poor accuracy of risk assessment results. This paper proposes a new convolution neural network method to measuring the risk of supply chain. All risk factors in the supply chain are analysed to clarify the relationship between different factors. The determination of the overall risk assessment index is done by identifying the coordination risk, logistics risk, information risk, and capital risk. The determination of the individual risk index is done by identifying the cost-profit rate, product qualification rate, and order lead time of the supplier risk. The manufacturing cost, product development cycle, and product flexibility are determined in the manufacturer's risk. On this basis, the measurement index system of this link is designed, though the convolution neural network to measure this measurement index system. Through analysis the highest fitting degree of this method was about 94%, the highest true rate is about 0.91, the lowest false positive rate is about 0.09.
    Keywords: convolution neural network; influencing factors; supply chain; risk evaluation; evaluation index.
    DOI: 10.1504/IJASS.2024.10063975
     
  • Identifying diagnosis and mortality of COVID-19 by learning a sequence-to-sequence ARIMA-based model   Order a copy of this article
    by You-Shyang Chen, Jerome Chih Lung Chou, Naiying Hsu, Ting Yi Kuo 
    Abstract: COVID-19 impacted the overall economy and social order in any country from 2019, and Taiwan firstly setup a control centre which turned out to an excellent policy for the prevention and stemmed the spread of the disease by strengthening the publicity of patients' health to prevent the pandemic. Thus, the study is motivated to identify COVID-19 and Taiwan as research subjects. This study utilises the pandemic data (from January 2020 to May 2020) of five countries and proposes a hybrid time series-based method to analyse the diagnosis rates and mortality rates. Consequently, the USA, Russia, Spain, and Taiwan's forecast results fall within the confidence interval; Brazil's forecast results exceed the confidence interval. Despite the limitations, the proposed model can still be used as a viable alternative for predicting future pandemics. The empirical results of this study benefit researchers by avoiding the prodigality of medical resources from proper forecasting.
    Keywords: diagnosis rate; mortality rate; new coronary pneumonia - COVID-19; time series forecasting; smoothing index; ARIMA model.
    DOI: 10.1504/IJASS.2024.10063972
     
  • Autism spectrum disorder prediction system using machine learning and deep learning   Order a copy of this article
    by Anshu Sharma, Poonam Tanwar 
    Abstract: Autism spectrum disease (ASD) is a neuro developmental illness that is both complicated and degenerative. A majority of known approaches use autism detection observation schedule (ADOS), pattern recognition, etc. to detect ASD with a small dataset, resulting in high accuracy but low generality. In this research, an ASD detection hybrid model is presented which is works on two different types of datasets. Firstly, behavioural datasets which works on logistic regression technique and secondly, facial dataset which works on based on convolutional neural network (CNN) Classifier in order to predict whether a person is suffering from autism or not. The suggested hybrid model which works on behavioural and facial dataset of a person beats state-of-the-art approaches in terms of accuracy, according to simulation findings. The proposed hybrid model had an average accuracy of 88% for the logistic regression model while it achieved an accuracy of 82.76% for the CNN model.
    Keywords: autism; convolutional neural network; CNN; logistic regression; classification; behaviour dataset; facial expression dataset.
    DOI: 10.1504/IJASS.2024.10063974