A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the expertise needed in reporting confirmatory tests. The present study made use of the potential morphological and immature fraction-related parameters (research items or cell population data) generated during complete blood cell count (CBC), through artificial intelligence (AI)/machine learning (ML) predictive modeling for early (at the pre-microscopic level) differentiation of various types of leukemias: acute from chronic as well as myeloid from lymphoid. The routine CBC parameters along with research CBC items from a hematology analyzer in the diagnosis of 1577 study subjects with hematological neoplasms were collected. The statistical and data visualization tools, including heat-map and principal component analysis (PCA,) helped in the evaluation of the predictive capacity of research CBC items. Next, research CBC parameter-driven artificial neural network (ANN) predictive modeling was developed to use the hidden trend (disease’s signature) by increasing the auguring accuracy of these potential morphometric parameters in differentiation of leukemias. The classical statistics for routine and research CBC parameters showed that as a whole, all study items are significantly deviated among various types of leukemias (study groups). The CPD parameter-driven heat-map gave clustering (separation) of myeloid from lymphoid leukemias, followed by the segregation (nodding) of the acute from the chronic class of that particular lineage. Furthermore, acute promyelocytic leukemia (APML) was also well individuated from other types of acute myeloid leukemia (AML). The PCA plot guided by research CBC items at notable variance vindicated the aforementioned findings of the CPD-driven heat-map. Through training of ANN predictive modeling, the CPD parameters successfully differentiate the chronic myeloid leukemia (CML), AML, APML, acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other related hematological neoplasms with AUC values of 0.937, 0.905, 0.805, 0.829, 0.870, and 0.789, respectively, at an agreeably significant (10.6%) false prediction rate. Overall practical results of using our ANN model were found quite satisfactory with values of 83.1% and 89.4.7% for training and testing datasets, respectively. We proposed that research CBC parameters could potentially be used for early differentiation of leukemias in the hematology–oncology unit. The CPD-driven ANN modeling is a novel practice that substantially strengthens the predictive potential of CPD items, allowing the clinicians to be confident about the typical trend of the “disease fingerprint” shown by these automated potential morphometric items.

Leucocytes, especially neutrophils featuring pro- and anti-cancerous characteristics, are involved in nearly every stage of tumorigenesis. Phenotypic and functional differences among mature and immature neutrophil fractions are well reported, and their correlation with tumor progression and therapy has emerging implications in modern oncology practices. Technological advancements enabled modern hematology analyzers to generate extended information (research parameters) during complete blood cell count (CBC) analysis. We hypothesized that neutrophil and lymphocyte fractions-related extended differential leucocytes count (DLC) parameters hold superior diagnostic utility over routine modalities. The present study was carried out over a four-and-a-half-year period wherein extended neutrophil (immature granulocyte [IG] and mature neutrophil [NEUT#&]), and lymphocyte (activated/high fluorescence lymphocyte count [HFLC] and resting lymphocyte [LYMP#&]) parameters were challenged over routine neutrophil [NEUT#] and lymphocyte [LYMP#] items in a study population of 1067 hematological neoplasm patients. Extending the classical statistical approaches, machine-learning-backed data visualization was used to explore trends in the study parameters. As a whole, extended neutrophil and lymphocyte count outperformed and was diagnostically more relevant than routine neutrophil and lymphocyte parameters by showing the least difference from their respective (gold-standard) manual DLC counts. The mature neutrophil count was compared to IG, and resting lymphocyte count was compared to HFLC by calling the function ‘correlation’ as a ‘clustering function’ for heatmap based visualization. The aforementioned study parameters displayed close clustering (rearrangement) for their respective study items by presenting distinct trends of equally valuable weights (deviated values), advocating fractions-based extended DLC reporting. Importantly, using a Bland and Altman analysis analogously to a manual neutrophil count, the mature neutrophil count [NEUT#&] remained unbiased since a routine neutrophil count [NEUT#] was found to be a negatively biased. The extended DLC-parameter-driven fractions-based reporting has superior diagnostic utility over classical routine approaches; this finding can largely minimize labor-intensive manual DLC practices, especially in hematology–oncology departments.

Mission-critical IT systems are utilizing closed source software (CSS) mainly due to reasons related to “quality assurance” and “continued support” despite much better benefits of using Open Source Software (OSS). OSS permits users to access source code for assessment, amendment, and redistribution, which offers low dependency on a vendor without license or maintenance cost. This paper investigates and analyzes OSS adoption factors for “critical IT infrastructure” by conducting a comprehensive review of the relevant literature. Furthermore, this paper proposes a framework that can help the critical IT industry to have increased confidence in OSS. The proposed framework utilizes the organizing logic of the Technology, Organization, and Environment (TOE) framework, recommends factors that were recognized by critically scrutinizing the studies found in the available literature. To validate the framework, a questionnaire-based survey was conducted targeting IT experts in critical sectors. The data integrity of survey results was analyzed using Cronbach's alpha. Framework factors were validated statistically using one sample T-test. The outcome indicated that the factors were statistically significant as the p-value was less than 0.05 for all the factors.

The amount of data is increasing rapidly as more and more devices are being linked to the Internet. Big data has a variety of uses and benefits, but it also has numerous challenges associated with it that are required to be resolved to raise the caliber of available services, including data integrity and security, analytics, acumen, and organization of Big data. While actively seeking the best way to manage, systemize, integrate, and affix Big data, we concluded that blockchain methodology contributes significantly. Its presented approaches for decentralized data management, digital property reconciliation, and internet of things data interchange have a massive impact on how Big data will advance. Unauthorized access to the data is very challenging due to the ciphered and decentralized data preservation in the blockchain network. This paper proposes insights related to specific Big data applications that can be analyzed by machine learning algorithms, driven by data provenance, and coupled with blockchain technology to increase data trustworthiness by giving interference-resistant information associated with the lineage and chronology of data records. The scenario of record tampering and big data provenance has been illustrated here using a diabetes prediction. The study carries out an empirical analysis on hundreds of patient records to perform the evaluation and to observe the impact of tampered records on big data analysis i.e diabetes model prediction. Through our experimentation, we may infer that under our blockchain-based system the unchangeable and tamper-proof metadata connected to the source and evolution of records produced verifiability to acquired data and thus high accuracy to our diabetes prediction model.

Medical imaging is continuing getting interesting research area using Machine Learning (ML) techniques especially for top researchers. Brain tumor detection, Malaria parasites detections are well known known research investigation domains. The datase...