Multiclass microarray gene expression classification based on fusion of correlation features

In this paper, we propose novel algorithmic models based on fusion of independent and correlated gene features for multiclass microarray gene expression classification. It is possible for genes to get co-expressed via different pathways. Moreover, a gene may or may not be co-active for all samples. In this paper, we approach this problem with a optimal feature selection technique using analysis based on statistical techniques to model the complex interactions between genes. The two different types of correlation modelling techniques based on the cross modal factor analysis (CFA) and canonical correlation analysis (CCA) were examined. The subsequent fusion of CCA/CFA features with principal component analysis (PCA) features at feature-level, and at score-level result in significant enhancement in classification accuracy for different data sets corresponding to multiclass microarray gene expression data

Factors affecting the organizational adoption of blockchain technology : an Australian perspective

Blockchain Technology (BCT) is a novel innovation that has the potential to transform industries, for instance, supply chain, energy, finance, and healthcare. However, despite the potential and the wide range of benefits reported, organizational adoption of BCT is low in several countries including Australia. Some studies investigated the adoption of BCT in different countries, however, there is a lack of research that examines the organizational adoption of BCT in Australia. This study fills this gap by exploring the factors, which influence BCT adoption among Australian organizations. To achieve this, we used an interpretative qualitative research approach based on the Technology, Organization, and Environment (TOE) framework and the Institutional Theory. The findings show that organizational adoption of BCT in Australia is influenced by perceived novelty, complexity, cost, and disintermediation feature of BCT; top management knowledge and support; government support, customer pressure, trading partner readiness, and consensus among trading partners. © 2021 IEEE Computer Society. All rights reserved

Information Technology and Organizational Learning Interplay: A Survey

The objective of this paper is to provide a systematic review of the evolutionary trends in the research domain of information technology and organizational learning. Having surveyed various journals and key conferences between 2000 and 2018 on the topic, we observe that information technology (IT) has expanded from its general form to various contemporary information systems, e.g. knowledge organization systems, communication and collaborative systems and decision support systems. However, organization learning (OL) now essentially occurs through knowledge management activities, e.g. knowledge acquisition, storing, sharing and application of knowledge. The survey reported here not only validates the interplay of IT and OL but also reveals some important intervening factors between IT and OL, e.g. absorptive capacity, organization culture, user trust, acceptance and satisfaction that work as deterministic elements in the reciprocal relationship of IT and OL. We propose future research to explore interaction between big data analytical systems and organizational learning

Factors Affecting the Organizational Adoption of Blockchain Technology: An Australian Perspective

Blockchain Technology (BCT) is a novel innovation that has the potential to transform entire sectors, for instance, supply chain, energy, finance, and healthcare. However, despite the potential and the wide range of benefits reported, organizational adoption of BCT is low in several countries including Australia. Some studies investigated the adoption of BCT in different countries, however, there is a lack of research that examines the organizational adoption of BCT in Australia. This study fills this gap by exploring the factors, which influence BCT adoption among Australian organizations. To achieve this, we used an interpretative qualitative research approach based on the Technology, Organization, and Environment (TOE) framework and the Institutional Theory. The findings show that organizational adoption of BCT in Australia is influenced by perceived novelty, complexity, cost, and disintermediation feature of BCT; top management knowledge and support; government support, customer pressure, trading partner readiness, and consensus among trading partners

Non-isomorphic coding in lattice model and its impact for protein folding prediction using genetic algorithm

Traditional encodings for hydrophobic(H)-hydrophilic(P) model or HP lattice models is isomorphic, which adds unwanted variations for the same solution, thereby slowing convergence. In this paper a novel non-isomorphic encoding scheme is presented for HP lattice model, which constrains the search space. In addition, similarity comparisons are made easier and more consistent and it will be shown that non-deterministic search approach such as genetic algorithm (GA) converges faster when non-isomorphic encoding is employed.Full Tex

Towards machine learning approach for digital-health intervention program

Digital-Health intervention (DHI) are used by health care providers to promote engagement within community. Effective assignment of participants into DHI programs helps increasing benefits from the most suitable intervention. A major challenge with the roll-out and implementation of DHI, is in assigning participants into different interventions. The use of biopsychosocial model [18] for this purpose is not wide spread, due to limited personalized interventions formed on evidence-based data-driven models. Machine learning has changed the way data extraction and interpretation works by involving automatic sets of generic methods that have replaced the traditional statistical techniques. In this paper, we propose to investigate relevance of machine learning for this purpose and is carried out by studying different non-linear classifiers and compare their prediction accuracy to evaluate their suitability. Further, as a novel contribution, real-life biopsychosocial features are used as input in this study. The results help in developing an appropriate predictive classication model to assign participants into the most suitable DHI. We analyze biopsychosocial data generated from a DHI program and study their feature characteristics using scatter plots. While scatter plots are unable to reveal the linear relationships in the data-set, the use of classifiers can successfully identify which features are suitable predictors of mental ill health

Factors affecting the organizational adoption of blockchain technology : extending the technology–organization– environment (TOE) framework in the Australian context

Blockchain technology (BCT) has been gaining popularity due to its benefits for almost every industry. However, despite its benefits, the organizational adoption of BCT is rather limited. This lack of uptake motivated us to identify the factors that influence the adoption of BCT from an organizational perspective. In doing this, we reviewed the BCT literature, interviewed BCT experts, and proposed a research model based on the TOE framework. Specifically, we theorized the role of technological (perceived benefits, compatibility, information transparency, and disintermediation), organizational (organization innovativeness, organizational learning capability, and top management support), and environmental (competition intensity, government support, trading partners readiness, and standards uncertainty) factors in the organizational adoption of BCT in Australia. We confirmed the model with a sample of adopters and potential adopter organizations in Aus-tralia. The results show a significant role of the proposed factors in the organizational adoption of BCT in Australia. Additionally, we found that the relationship between the influential factors and BCT adoption is moderated by “perceived risks”. The study extends the TOE framework by adding factors that were ignored in previous studies on BCT adoption, such as perceived information trans-parency, perceived disintermediation, organizational innovativeness, organizational learning capa-bility, and standards uncertainty. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Challenges and opportunities for Blockchain Technology adoption: A systematic review

Blockchain technology promises to significantly impact current business processes in industries from various sectors and reduce transactional cost. Firms, suppliers, government, financial institutions etc. are anticipating a business model transformation through blockchain by accomplishing a decentralized architecture of interorganizational dealings without intermediaries. In spite of its immense potential, however, there are key challenges of blockchain implementation which need to be studied for identifying the opportunities arising and for its successful implementations in future. In this paper, we aim to identify these challenges for blockchain adoption and classify them for clearer understanding. To pursue this effectively, this paper follows a hybrid model of systematic literature review. This paper also explicitly enumerates future research opportunities to lead industry and researchers in correct directions

Adoption of blockchain technology : exploring the factors affecting organizational decision

Blockchain (BCT) is an emerging technology that promises many benefits for organizations, for instance, disintermediation, data security, data transparency, a single version of the truth, and trust among trading partners. Despite its multiple benefits, the adoption rate of BCT among organizations has not reached a significantly high level worldwide, thus requiring further research in this space. The present study addresses this issue in the Australian context. There is a knowledge gap in what specific factors, among the plethora of factors reported in the extant literature, affect the organizational adoption of BCT in Australia. To fill this gap, the study uses the qualitative interpretative research approach along with the technology-organization-environment (TOE) framework as a theoretical lens. The data was mainly drawn from the literature review and semi-structured interviews of the decision-makers and senior IT people from the BCT adopter and potential adopter organizations in Australia. According to the findings, perceived information transparency, perceived risks, organization innovativeness, organization learning capability, standards uncertainty, and competition intensity influence organizational adoption of BCT in Australia. These factors are exclusively identified in this study. The study also validates the influence of perceived benefits and perceived compatibility on BCT adoption that are reported in the past studies. Practically, these findings are helpful for the Australian government and public and private organizations to develop better policies and make informed decisions for the organizational adoption of BCT. The findings would guide decision-makers to think about the adoption of BCT strategically. The study also has theoretical implications explained in the discussion section. © 2022 Saleem Malik et al

From general language understanding to noisy text comprehension

Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The proposed research establishes effective methods for improving the comprehension of noisy texts. For this, we propose a new generic methodology to derive a diverse set of sentence vectors combining and extracting various linguistic characteristics from latent representations of multi-layer, pre-trained language models. Further, we clearly establish how BERT, a state-of-the-art pre-trained language model, comprehends the linguistic attributes of Tweets to identify appropriate sentence representations. Five new probing tasks are developed for Tweets, which can serve as benchmark probing tasks to study noisy text comprehension. Experiments are carried out for classification accuracy by deriving the sentence vectors from GloVe-based pre-trained models and Sentence-BERT, and by using different hidden layers from the BERT model. We show that the initial and middle layers of BERT have better capability for capturing the key linguistic characteristics of noisy texts than its latter layers. With complex predictive models, we further show that the sentence vector length has lesser importance to capture linguistic information, and the proposed sentence vectors for noisy texts perform better than the existing state-of-the-art sentence vectors. © 2021 by the authors. Licensee MDPI, Basel, Switzerland