DIVERGENCE IN STAKEHOLDER PERCEPTIONS OF SECURITY POLICIES: A REPGRID ANALYSIS FOR NORM-RULE COMPLIANCE

Many organizations have a problem with synchronizing individual values regarding information security with expectations set by the relevant security policy. Such discordance leads to failure in compliance or simply subversion of existing or imposed controls. The problem of the mismatch in understanding the security policies amongst individuals in an organization has devastating effect on security of the organization. Different individuals hold different understanding and knowledge about IS security, which is reflected on IS security policies design and practice (Vaast, 2007). Albrecthsen and Hovdena (2009) argue that users and managers practice IS security differently because they have different rationalities. This difference in rationalities may reflect the mismatch between the security policies and individuals’ values. In this research, we argue that occurrence of security breach can change individuals’ values in light of security policy of organization. These changes in the values can be reflected on the compliance between individuals’ norms and security rules and standards. Indeed, organizations need to guarantee the compliance between security policy and values of their employees. Thus, they can alleviate or prevent violations of security of organization. However, it is difficult to find a common method that all organizations can adopt to guarantee the synch between security rules and individuals’ norms. The main aim of this research is to investigate how people perceive information security policy and how their perceptions change in response to security breaches. Besides, this research aims to investigate the relationship between individuals’ values and security policy. Thus, organizations can have the intended level of compliance between individual norms and security rules and standards. With the aid of the Repertory Grid technique, this research examines how a security breach shapes people’s values with respect to security policy of an organization. To conduct the argument, this research offers an assessment mechanism that aids the organization to evaluate employees’ values in regard to security policy. Based on that evaluation, the organization can develop a proper mechanism to guarantee compliance between individuals’ norms and security rules. The results of this research show that employees in an organization hold different perceptions regarding the security policy. These perceptions change in response to security incident. This change in perceptions dose not necessarily result in better compliance with the security policy. Factors like the type of breach and people’s experience can affect the amount of change in the perceptions. Contributions, implications, and directions for future research of this study will be discussed

Mismatched Understanding of IS Security Policy: A RepGrid Analysis

Professional and academic literature indicates that organizational stakeholders may hold different perceptions of security rules and policies. This discrepancy of perceptions may be rooted into a conflict between the compliance of stakeholders to organizational norms on the one hand, and security rules on the other. The paper argues that a mismatched understanding of security policy can have a devastating effect on the security of organizations, and should therefore be treated as a key reason for non-compliance to security policy. Using Personal Construct Theory and Repertory Grids we explore how different stakeholder groups within an organization can hold divergent views on the same security policies. Our findings have implications for the design of security policy training and awareness programs, as well as for the institution and internalization of good IS governance practices

Resistance of multiple stakeholders to e-health innovations: Integration of fundamental insights and guiding research paths

Consumer/user resistance is considered a key factor responsible for the failure of digital innovations. Yet, existing scholarship has not given it due attention while examining user responses to e-health innovations. The present study addressed this need by consolidating the existing findings to provide a platform to motivate future research. We used a systematic literature review (SLR) approach to identify and analyze the relevant literature. To execute the SLR, we first specified a stringent search protocol with specific inclusion and exclusion criteria to identify relevant studies. Thereafter, we undertook an in-depth analysis of 72 congruent studies, thus presenting a comprehensive structure of findings, gaps, and opportunities for future research. Specifically, we mapped the relevant literature to elucidate the nature and causes of resistance offered by three key constituent groups of the healthcare ecosystem—patients, healthcare organizational actors, and other stakeholders. Finally, based on the understanding acquired through our critical synthesis, we formulated a conceptual framework, classifying user resistance into micro, meso, and macro barriers which provide context to the interventions and strategies required to counter resistance and motivate adoption, continued usage, and positive recommendation intent. Being the first SLR in the area to present a multi-stakeholder perspective, our study offers fine-grained insights for hospital management, policymakers, and community leaders to develop an effective plan of action to overcome barriers that impede the diffusion of e-health innovations.publishedVersionPaid open acces

Exploring the impact of smart cities on improving the quality of life for people with disabilities in Saudi Arabia

By using advanced technologies and data analytics, smart cities can establish conditions that are both inclusive and accessible, addressing the distinctive needs of disabled people. This research aims to examine the benefits of smart city technologies and develop strategies for developing environments that serve the requirements of individuals with disabilities in Saudi Arabia. Using a sequential mixed method, the study uses the social disability model. The initial phase involves gathering quantitative data from 427 individuals with disabilities in Saudi Arabia. Further, qualitative data was obtained through semi-structured interviews with a sample of four professionals employed in Saudi smart city initiatives. Quantitative data is analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM), while qualitative data is analyzed using thematic analysis. Quantitative findings revealed the robustness of the measurement model, confirming the significant effects of Smart City Initiatives on Accessibility Enhancement, Inclusive Information, and Health and Wellbeing Improvement. The respondents indicated that they are satisfied with the initiatives and their effectiveness, providing them with equal services and opportunities without discrimination. The qualitative analysis further revealed themes, i.e., Technology Integration for Accessibility, Inclusive Design, Inclusive Planning for Health, and others. Participants indicated special consideration for implementing the designs and approaches to ensure inclusivity and availability of services to disabled people. Besides, implementing infrastructure and policies to ensure the health and wellbeing of disabled people also remained prevalent. Hence, it is concluded that smart city initiatives break obstacles and improve the wellbeing of individuals with disabilities. Improved healthcare services and inclusive urban planning highlight the transformative effect of these initiatives on health and wellbeing, promoting an equitable and sustainable services environment. Finally, research implications and limitations are discussed

Decoupled SculptorGAN Framework for 3D Reconstruction and Enhanced Segmentation of Kidney Tumors in CT Images

Our proposed work, SculptorGAN, represents a novel advancement in the domain of medical imaging, for the accurate and automatic diagnosis of renal tumors, using the techniques and principles of Generative Adversarial Network (GAN). This dichotomous framework forms a contrast to the normal segmentation models like that of U-Net model but, instead, founded on a strategy that is aimed towards reconstruction and segmentation of CT images, particularly of renal malignancies. The core of the SculptorGAN methodology is a GAN-based approach for precise three-dimensional rendering of renal anatomies from CT scans, followed by a segmentation phase to correctly separate the neoplastic from non-neoplastic tissues. In fact, SculptorGAN was designed to circumvent limitations that come as inherent in the segmentation techniques, and in this case to eliminate them. In fact, by including such an advanced algorithmic architecture, accuracy of diagnosis in SculptorGAN has increased to 96.5%, which is the primary aspect behind early detection and thus proper curing of renal tumors. The better results were ascribed to more accurate and detailed reconstruction of renal structures that the framework allowed, apart from the better segmentation. The performance analyses show quantitative results with respect to the presented datasets, while the validation shows that SculptorGAN outperforms most of the traditional models such as U-Net. In particular, SculptorGAN decreased the time taken for 3D reconstruction by about 35% while increasing the accuracy of segmentation by 20% or more. The outcome, in their turn, may suggest this improvement in efficiency and the level of reliability for renal tumor diagnosis as of having far-reaching implications for the patient treatment and its outcomes. In conclusion, the framework deals with all the challenges with an accurate diagnosis of renal tumors and brings betterment in the overall field of medical image analysis by providing the abilities of GANs for the betterment in image reconstruction and segmentation

Water quality level estimation using IoT sensors and probabilistic machine learning model

Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based analysis of water quality in specific environments is done concerning certain parameters including the PH level, hardness, TDS, etc. The outcome of such methods analyzes whether the environment provides potable water or not. Potable denotes the purified water that is free from all contaminations. This analysis gives an absolute solution whereas the demand for drinking water is a growing problem where the multiple-level estimations are essential to use the available water resources efficiently. In this article, we used a benchmark water quality assessment dataset for analysis. To perform a level assessment, we computed three major features namely correlation-entropy, dynamic scaling, and estimation levels, and annexed with the earlier feature vector. The assessment of the available data was performed using the statistical machine learning model that ensemble the random forest and light gradient boost model (GBM). The probability of the ensemble model was done by the Kullback Libeler Divergence model. The proposed probabilistic model has achieved an accuracy of 96.8%, a sensitivity of 94.55%, and a specificity of 98.29%

An Approach to Binary Classification of Alzheimer’s Disease Using LSTM

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer’s disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment

Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease

Chronic Kidney Disease (CKD) represents a considerable global health challenge, emphasizing the need for precise and prompt prediction of disease progression to enable early intervention and enhance patient outcomes. As per this study, we introduce an innovative fusion deep learning model that combines a Graph Neural Network (GNN) and a tabular data model for predicting CKD progression by capitalizing on the strengths of both graph-structured and tabular data representations. The GNN model processes graph-structured data, uncovering intricate relationships between patients and their medical conditions, while the tabular data model adeptly manages patient-specific features within a conventional data format. An extensive comparison of the fusion model, GNN model, tabular data model, and a baseline model was conducted utilizing various evaluation metrics, encompassing accuracy, precision, recall, and F1-score. The fusion model exhibited outstanding performance across all metrics, underlining its augmented capacity for predicting CKD progression. The GNN model’s performance closely trailed the fusion model, accentuating the advantages of integrating graph-structured data into the prediction process. Hyperparameter optimization was performed using grid search, ensuring a fair comparison among the models. The fusion model displayed consistent performance across diverse data splits, demonstrating its adaptability to dataset variations and resilience against noise and outliers. In conclusion, the proposed fusion deep learning model, which amalgamates the capabilities of both the GNN model and the tabular data model, substantially surpasses the individual models and the baseline model in predicting CKD progression. This pioneering approach provides a more precise and dependable method for early detection and management of CKD, highlighting its potential to advance the domain of precision medicine and elevate patient care

Multi-class Breast Cancer Classification Using CNN Features Hybridization

Breast cancer has become the leading cause of cancer mortality among women worldwide. The timely diagnosis of such cancer is always in demand among researchers. This research pours light on improving the design of computer-aided detection (CAD) for earlier breast cancer classification. Meanwhile, the design of CAD tools using deep learning is becoming popular and robust in biomedical classification systems. However, deep learning gives inadequate performance when used for multilabel classification problems, especially if the dataset has an uneven distribution of output targets. And this problem is prevalent in publicly available breast cancer datasets. To overcome this, the paper integrates the learning and discrimination ability of multiple convolution neural networks such as VGG16, VGG19, ResNet50, and DenseNet121 architectures for breast cancer classification. Accordingly, the approach of fusion of hybrid deep features (FHDF) is proposed to capture more potential information and attain improved classification performance. This way, the research utilizes digital mammogram images for earlier breast tumor detection. The proposed approach is evaluated on three public breast cancer datasets: mammographic image analysis society (MIAS), curated breast imaging subset of digital database for screening mammography (CBIS-DDSM), and INbreast databases. The attained results are then compared with base convolutional neural networks (CNN) architectures and the late fusion approach. For MIAS, CBIS-DDSM, and INbreast datasets, the proposed FHDF approach provides maximum performance of 98.706%, 97.734%, and 98.834% of accuracy in classifying three classes of breast cancer severities

PrEGAN: Privacy Enhanced Clinical EMR Generation: Leveraging GAN Model for Customer De-Identification

Privacy in medical records while data sharing is a major concern for distributed learning models. The dataset generated and shared via Electronic Medical Records (EMR) consist of sensitive medical information such as patient identify and experts recommendations, and causes setbacks in training larger models, dataset augmentation and polluting datasets with recursive attributes. The information processing and de-identification is proposed in this article to preserve and enhance the privacy of EMR. The proposed technique is termed as PrEGAN (i.e.) Privacy Enhanced Generative Adversarial Network (GAN) for EMR data training and realistic mapping. The proposed model generates and discriminates the ground truth with generated mask via a computation of loss function for de-identification or removal of personal linked/connected data in the records networks. The objective is to generate the mask of EMR, which is realistic and similar to the ground truth. The model is trained and validated with two distinguished discriminators, the CNN based discriminator is used for medical images, whereas Neural Networks are used for textural data generator. The experimental results demonstrate a higher degree of data privacy and de-identification in EMR with 88.32% accuracy in predicting and eliminating via RoI and loss function