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Charting the Complexity of Diabetes Risk using Network-based Exploration of Nonlinear Interactions
Diabetes mellitus is a global health challenge shaped by complex clinical, demographic, and socioenvironmental factors. Traditional linear models often overlook the non-linear dependencies that drive diabetes risk. This study adopts a systems-thinking approach by integrating mutual information (MI)-based network modeling with machine learning to improve prediction, interpretability, and fairness. Using a nationally representative CDC dataset, we build a weighted undirected network where variables are nodes connected by MI-derived edges. Centrality analysis identifies age, HbA1c, and BMI as key hubs. Community analysis reveals clinical, demographic, and racial modules, reflecting the multidimensional nature of diabetes risk. These network insights inform feature selection for training logistic regression, random forest, and XGBoost models. XGBoost achieves the highest accuracy (95.3%) and AUC (0.939), while logistic regression offers the best calibration (Brier score = 0.045), enhancing clinical usability. Subgroup analysis shows stable predictions across racial groups, supporting fairness. This integrated framework uncovers latent, non-linear associations and offers a robust, interpretable, and equitable tool for precision diabetes risk modeling. 2025 IEEE. -
Face-Based Kinship Verification using Deep Embeddings for Low-Cost Health Record Linkage
Precise linkage of health records is essential for continuity of care, reducing duplicate health records, and accurately documenting family medical histories. Genomic testing offers the evidence-based biological 'gold standard' for verifying kinship; however, access to testing is either impossible or unavailable in most low-resourced environments due to prohibitive costs, long timelines, and/or lack of infrastructure. This study provides a low cost and interpretable pipeline for kinship verification in the form of Siamese deep embeddings. The processed facial image embeddings produced by a ResNet-18 backbone using 256-dimensional and L2-normalized embeddings, are then compared using cosine similarity. A validation-based calibration process selects the logit polarity and decision threshold that support stable deployment decisions. Grad-CAM visualizations can be interpreted frame-by-frame and allow for pair-specific attributions of faces that were more relevant or important in decisions of similarity. In experiments on the Families in the Wild (FIW) dataset (family-disjoint splits), we report ROC-AUC of 0.834, target balanced accuracy of ?0.88, with similar precision, recall, and specificity. The confusion matrices also illustrate a near symmetric distribution of errors by family and both Grad-CAM explanations highlight how the model came to a decision for true cases and hard cases. The above results illustrate how we can deploy a lightweight, explainable, and face-based kinship verification pipeline on a CPU-only system. Our study therefore provides a feasible assistive tool for health record linkage where genomic validation is not possible. 2025 IEEE. -
Transforming towards 6G: Critical Review of Key Performance Indicators
With the experiences acquired upon the successful implementation of 5G networks academia, researchers, and industry are envisioning the need for 6G networks. The vision of the 6G communication network is supposed to completely assist the creation of a Ubiquitous Intelligent Mobile Society. Already 5G technologies are in place and still few extended features of 5G are continuously being introduced. Even though the 6G communication network is expected to have greater capabilities than the existing 5G, there are no clear specifications on how far these capabilities shall be capitalized in 6G. The 6G technologies shall move past ordinary mobile internet services and advance to support ubiquitous Artificial Intelligent (AI) services from the network's core to end-to-end service devices/applications. The architecture, protocols, and operations which are the primary constituents of the 6G network shall implement AI technologies for self-optimization and actualization. This article brings an all-inclusive deliberation of 6G based on an assessment of preceding generations' evolving technology developments. 2022 IEEE. -
Resource Aware Weighted Least Connection Load Balancing Technique in Cloud Computing
Cloud computing became a pivotal for the most of the real time applications. In cloud computing, the customer demands the services with the best performance even when the application is expanding rapidly. Therefore, it is essential to manage the resources effectively because the number of users and services growing proportionately. The main aim of the load balancing technique is to allocate the customers' requests with the large pool of resources efficiently. The problem is how to evenly distribute the load of requests among the compute nodes according to their capacity. Therefore, there is a need for an effective load balancing technique for smooth continuity of operations in a distributed environment with a heterogeneous server configuration. This paper presents a novel load balancing technique, namely, Resource aware weighted least connection load balancing which addresses the above said problem efficiently. The essence of this work is to assign the requests across multiple servers based on the requested resource and the status of the number of connections presently served by each server. This work used standard score technique to enumerate the weight of each node. Experiments were conducted using Cloud Analyst, a famous cloud simulator breed from CloudSim. Appropriate performance parameters were analysed to measure the effectiveness of the proposed technique. Future directions for the extension of the implemented technique also identified. 2023 IEEE. -
Research Perspectives on Load Balancing Strategies in Serverless Computing
Serverless computing, a groundbreaking trend in cloud computing, has transformed how applications are deployed and managed by abstracting the infrastructure layer. Serverless computing enables developers to concentrate exclusively on their code while cloud providers care for server provisioning, maintenance, and scaling. Services like AWS Lambda, Google Cloud Functions, and Azure Functions exemplify this model, offering s ubstantial advantages in terms of reduced operational complexity and cost. However, one persistent challenge in this domain is load balancing. Effective load balancing in serverless computing ensures efficient resource utilization, optimal performance, and cost-effectiveness. Unlike traditional load balancing, which typically relies on long-lived server instances, load balancing in serverless environments must accommodate the stateless and ephemeral nature of serverless functions. Traditional techniques are not directly applicable because serverless architectures functions that are instantiated on-demand in response to incoming requests. This paper surveys various strategies and approaches developed to address the unique load balancing challenges in serverless computing, providing a comprehensive overview of the current state of research and practice. The paper extends further research on serverless computing by analyzing the survey papers. The paper highly focuses the research areas in the field of edge computing, hybrid cloud models and distributed load balancing for the future usage. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A frequent itemset generation approach in data mining using transaction-labelling dynamic itemset counting method
A significant amount of data is generated, gathered, stored, and evaluated in real-world applications as a result of technology breakthroughs. Data mining (DM) combines a number of disciplines to efficiently discover hidden patterns from vast archives of historical information. To significantly reduce complexities associated with data, the proposed method, transaction-labelling dynamic itemset counting (TL-DIC), utilises a labelling approach on the given transactional database to logically arrange and process the underlying transactions. This method generates frequent itemsets thereby improving the performance of conventional dynamic itemset counting (DIC) method. Based on experimental findings, the average scan count in DIC and M-Apriori is 4% and 3.66%, respectively higher than TL-DIC, for different support counts. TL-DIC executes 20% and 16% quicker than DIC and M-Apriori, respectively, in terms of execution time. These results validate the proposed approachs efficacy in creating frequent itemsets from large datasets. Copyright 2025 Inderscience Enterprises Ltd. -
Exosomes as an emerging nanoplatform for functional therapeutics
The release of a division of extracellular entities with 40-100 nm dimension from both tumor and varied mesenchymal stem cells during pathologic conditions is termed as exosomes that are shown to actively participate in chemical signaling events occurring in multicellular organisms infection. Exosomes act as a vehicle for shifting amino acids, lipids, and genetic components that are readily engulfed by far-flanging cells (or adjacent cells) at the site of release for remolding the receptor cell functions once the biological contents get activated. Furthermore, pathogens too display the dispense of exosomes to temper the hosts immune response and trigger the infection rate, making them apt investigation markers for diseases. Additionally, aiding in antigen presentation and immune response stimulation, exosomes are significant in showing contrasting role as initiating anticarcinogenic responses and involving in promoting tumorigenesis as they are released from tumor cells. Due to their site specificity, cell lineage property, and encapsulation of specific constituents, exosomes can be potentially utilized as a precious investigatory and prognostic tool along with a possible carrier of drugs and gene shipment for curative goal. Since exosome groundwork is at the infancy stage, deeper insight is required to know its composition, formation, and targeting mechanism along with its significant role in disease diagnosis and treatment. This states, as mentioned previously, we have tried to focus solely on the functional and clinical implications of exosomes in-depth in this review. 2021 Elsevier B.V. All rights reserved. -
Customer preferences to select a restaurant through smart phone applications: An exploratory study
The increasing number of Smart Phone Applications (SPA) user and fast growing restaurant industry proves the great potential of using SPA as business marketing opportunity in Malaysia. The constant growth in mobile technology has created a prospect for the restaurant industry to use SPA as a restaurant promotion tool. The growing attention of use of SPA among the Malaysian customer, marketing research remains understudied in the field of SPA based restaurant promotion activities. The aim of this study is to explore the increase in customer acceptance to use SPA based restaurant promotion and to identify the customer preference to use SPA to select the restaurant. Thus, this paper mainly focuses on restaurant information on product and promotion as antecedents of customer acceptance of smart phone apps by underpinning the Unified theory of acceptance and use of technology (UTAUT) model. A conceptual model and hypotheses are tested with a sample of 116 students from a private university at Selangor district, Malaysia. The findings indicate that there is a positive relationship to increase customer acceptance level through SPA based restaurant product information and also strong relationship with the restaurant promotion information. It also indicates that customer acceptance of SPA through experience and satisfaction has a positive significant effect on customer preference to select a restaurant. Based on the results, this paper rounds off with conclusion, recommendations for future marketing research and provides a new marketing strategy to formulate among the restaurant business sector. 2015 American Scientific Publishers. All rights reserved. -
RF-ShCNN: A combination of two deep models for tumor detection in brain using MRI
The tumor in the brain is the reason for jagged cell enlargement in the brain. Magnetic resonance imaging (MRI) is a common scheme to identify tumor existence in the brain. With these MRIs, the medical practitioner can examine and detect the abnormal growth of tissues and corroborate if the brain is influenced by a tumor or not. Due to the appearance of artificial intelligence models, the discovery of brain tumor is performed by adapting different models which thereby help in making decisions and selecting the most suitable diagnosis for patients. The main motivation of this work is to reduce the death rate. If they are not adequately treated, the survival rate of the patient decreases. The correct diagnoses help patients receive accurate treatments and survive for a long time. This paper develops a hybrid model, namely the Residual fused Shepherd convolution neural network (RF-ShCNN) for discovering tumor in the brain considering MRI. Thus, the Adaptive wiener filtering is adapted to filter image-commencing noise. Thereafter, Conditional Random Fields-Recurrent Neural Networks (CRF-RNN) are adapted for segmentation followed by the mining of essential features. Lastly, the features employed in RF-ShCNN for making effective brain tumor detection by means of MRI. Thus, the RF-ShCNN is built by unifying the deep residual network and Shepherd convolution neural network. The hybridization is done by adding a regression layer wherein the regression is fused with Fractional calculus (FC) to make effective detection. The RF-ShCNN provided better accuracy of 94%, sensitivity of 95% and specificity of 94.9%. 2023 -
Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection
In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of the digital content. The computer vision features based on the frame change are extracted using a proposed deep learning model called the Cascaded Deep Sparse Auto Encoder (CDSAE) trained by temporal CNN. The detection process is performed using a Deep Neural Network (DNN) to classify the deep fake image/video from the real image/video. The proposed model is implemented using Face2Face, FaceSwap, and DFDC datasets which have secured an improved detection rate when compared to the traditional deep fake detection approaches. 2022. Balasubramanian et al. -
Microlearning and Learning Performance in Higher Education: A Post-Test Control Group Study
This study aimed at evaluating the effectiveness of microlearning in higher education. The sample consisted of first-year MBA students, and a post-test control group design was used to assess the effectiveness of a microlearning module. The results indicated that the use of microlearning was significantly related to learning performance and participants' reactions to the module. Moreover, the microlearning group scored significantly higher than the control group. The findings suggest that microlearning has the potential to improve learning outcomes and enhance participant engagement. However, the study has certain limitations, and future research is needed to gain a comprehensive understanding of the optimal design and delivery of microlearning modules. The study supports the use of microlearning in higher education as an effective instructional strategy. 2024, Commonwealth of Learning. All rights reserved. -
Jugaad in organizational settings: exploring the Jugaad leadership competencies
The Hindi term 'jugaad' is closely linked to frugal innovation. In resource-scarce environments, organizations can thrive by developing jugaad-related leadership abilities. Previous research on jugaad has focused primarily on individual problem-solving and overlooked the leadership skills necessary to implement it in organizational settings. This study employs a theoretical lens of leadership competency models, interpretive phenomenology, purposive sampling, and an inductive data-driven coding approach to explore the jugaad leadership competencies of 28 Indian business leaders and managers. The study presents the Jugaad Leadership Competency (JLC) model, identifying ten competency clusters exhibited by jugaad leaders. This is the first study to develop a model for jugaad leadership in organizational settings. In environments characterized by scarcity and intense competition, the JLC model can aid individuals and organizations in acquiring the necessary competencies for frugal innovation. The study evaluates the theoretical and practical implications of the findings, their transferability, and limitations and offers suggestions for future research. 2023, Springer Nature Limited. -
A structured approach to implementing Robotic Process Automation in HR
Technological innovations are changing the industrial landscape. As technology transforms the world, the HR function needs to focus on embracing automation and other technologies that promise efficiency, service effectiveness and cost savings. Deployment of robotic process automation (RPA) can help (a) to offer better service to employees and managers (b) ensure compliance of HR processes with standards and regulations (c) facilitate rapid initiation and completion of HR processes (d) enhance efficiencies by digitizing data and auditing process data (e) improve HR productivity and cost savings by automating manual and repetitive tasks. A robust and structured approach needs to be in place to identify HR processes that can be automated using the RPA approach. In this paper the authors (a) suggest a four step approach - validation, assessment, evaluation and classification - to analyze processes and verify their suitability for automation using RPA (b) identify HR processes that has relevance for the RPA approach within the broad areas of HR Strategy, Talent Acquisition, Talent Development & Performance Management, Compensation & Benefits, HR Operations and Employee Relations (c) recommend a process for mapping HR RPA propensity. A case study is also presented for greater clarity on adoption of RPA in HR processes. Published under licence by IOP Publishing Ltd. -
The 90-h workweek controversy at L&T: leadership, culture and the future of work in India
Learning outcomes Upon completion of this case, students will be able to understand the implications of psychological contract theory in organizational contexts. It will allow them to analyse how leadership communication affects internal trust and public perception and critically evaluate the reputational consequences of symbolic leadership behaviour. Case overview/synopsis S.N. Subrahmanyan, Chairman and Managing Director of Larsen & Toubro (L&T), Indias leading engineering conglomerate, made a public statement encouraging employees to work 90?h a week and use Sundays to achieve global excellence. Although intended as a motivational message, the statement triggered nationwide backlash. Employees, industry leaders and the public interpreted the comment as reflecting an outdated, unsustainable work culture, sparking debates around worklife balance and generational shifts in employee values. Internally, the comment created anxiety and ambiguity regarding the companys expectations, especially among younger professionals. The case will allow students to examine how leadership communication can reshape psychological contracts, explore generational tensions in the workplace and evaluate how organizations should respond to reputational challenges while preserving performance culture. The case dilemma centres on whether L&T should clarify, retract or reinforce the chairmans statement. Complexity academic level This case is appropriate for upper undergraduate- and graduate-level programs in organizational behaviour, strategic human resource management, business ethics and leadership studies. Potential programs include, BBA, MBA HR and industrial and organizational psychology. Supplementary Material Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management. 2025 Emerald Publishing Limited -
A Review on Utilization of Construction and Demolition Waste (CDW) Toward Green and Circular Economy
Globally, policy makers have realized the significance of infrastructure development with respect to safety and environment-friendly approach. This has resulted in reuse and recycling initiatives in various industries including construction and building sector. Further, it is imperative to understand new techniques and methods to improve the effectiveness of recycling, keeping environment and carbon emissions in check. Recently, utilization of construction and demolition waste (CDW) as precursors in synthesizing alkali-activated and geopolymer binders have caught attention of researchers as green building material. This review paper discusses the findings of the latest research and promotes the use of CDW as a potential starting or precursor material in alkali-activated or geopolymer concrete toward green and circular economy. If processed appropriately, CDW can be used to produce environment-friendly binders that can reduce our dependence on conventional binders like Portland cement, thus promoting recycling in sustainable and eco-friendly manner. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Development and validation of gaming disorder and hazardous gaming scale (GDHGS) based on the WHO framework (ICD-11 criteria) of disordered gaming
This study aimed to develop and validate a brief psychometric scale for gaming disorder and hazardous gaming based on the WHO framework as defined in the ICD-11. The study was carried out among college students using face to face interview. A panel of mental health experts examined the face validity of the new Gaming Disorder and Hazardous Gaming Scale (GDHGS). An Exploratory Factor Analysis (EFA) using the principle component analysis (PCA) method with direct oblimin rotation on the five items of GDHGS was used for assessment of construct validity. The results of Kaiser Meyer Olkin (KMO) measure used for sampling adequacy and Bartlett's test (BT) of sphericity used to show the appropriateness of using factor analysis, confirmed the appropriateness of EFA for the present study sample. The factor analysis extracted single component with an eigenvalue of greater than one, which was further supported by the examination of scree plot. To examine the criterion related validity of the GDHGS, correlation between GDHGS and IGDS-SF scores was assessed. Spearman correlational analysis showed strong positive correlation of GDGHS score with IGDS-SF score (rs = 0.878, p < 0.01). Further, the sum of first four item score of GDHGS among participants diagnosed with GD (median: 15.00; IQR: 15.0015.75) was significantly greater than those without GD (median: 4.00; IQR: 3.006.50) according to the diagnostic interview based on the ICD-11 criteria (U = 0.000, p < 0.001). The internal consistency of GDHGS as measured by the Cronbach's alpha was 0.914. Further, the GDHGS did not have its reliability increased by removal of any of the five items included in the scale. Also, the threshold for significant floor and ceiling effect was not reached. In conclusion, GDHGS is a valid measurement scale for disorders involving gaming behaviour based on the ICD- 11 construct. 2020 Elsevier B.V. -
Development and validation of screening tool for excessive and problematic use of internet and digital devices (STEPS-IDD) based on the WHO framework (ICD-11) for addictive behaviours
Background: The widespread use of internet and digital devices has been accompanied by growing concern regarding harms associated with their excessive or problematic use. The World Health Organization has also formally included some of these in its latest classificatory system (ICD-11) under the category of disorders due to addictive behaviours. However, a validated, comprehensive screening tool aligned with ICD-11 that screens for these potentially addictive behaviours is lacking. This study aimed to develop and validate the Screening Tool for Excessive and Problematic use of Internet and Digital Devices (STEPS-IDD), designed to assess multiple addictive behaviours based on ICD-11 criteria. Methods: STEPS-IDD was developed based on the ICD-11 framework for disorders due to addictive behaviours It was applied to assess well-established behavioural addictions like gaming and gambling disorder, as well as less-established but widely researched ones such as problematic use of social media, online shopping/buying, OTT content watching, and pornography watching. Face validity was established through expert review and feedback. Construct validity was evaluated through exploratory factor analysis (EFA), and Cronbach's alpha coefficients were estimated to assess internal consistency. To examine concurrent validity, correlations between scores obtained on the newly developed STEPS-IDD sub-sections and the previously validated Gaming Disorder and Hazardous Gaming Scale (GDHGS) and modified GDHGS for other behaviours were assessed. Receiver Operating Characteristic (ROC) analyses were conducted to determine optimal STEPS-IDD cut-off scores for different behaviours. Results: Data from a total of 112 college students (64.3% female) with a mean age of 20.5 years were analyzed. STEPS-IDD demonstrated good construct validity, with EFA revealing predominantly unidimensional factor structure for most behavioural domains. Internal consistency was excellent (Cronbach's ? = 0.860.91 across sub-sections). Concurrent validity was supported by moderate to strong positive correlations (r = 0.440.76) of STEPS-IDD sub-sections with corresponding GDHGS and modified GDHGS scores. ROC analyses yielded optimal cut-off scores with high sensitivity and acceptable specificity for different behaviours, and fair to excellent overall diagnostic accuracy. Conclusion: STEPS-IDD is a psychometrically robust, brief yet comprehensive screening tool grounded in the ICD-11 framework, for the risk stratification in the context of addictive behaviours related to the use of the internet and digital devices. 2026 Balhara, Singh, Majumdar, Ayoob and Singh. -
Emerging world of the metaverse: An Indian perspective
[No abstract available]