174 research outputs found
Analysis of Healthcare Systems Using Computational Approaches: Concepts, Methodologies, Tools and Applications
No full textA medical database of different diseases in the healthcare system was essential. The role of computer-supported systems is essential to accurately detect diseases by examining the various components of the human body using radiological or several X-ray, MRI, CT scans, etc. Although various approaches were used to improve healthcare systems. Several soft computing techniques were used to develop new diagnostic systems for any illness with improved performance, like an artificial neural networks approach, fuzzy logic, genetic algorithms etc. Based on disease diagnosis, the accuracy medicine was aimed at developing the powerful pharmaceutical drug system for health solutions. Therefore, it has focused on diverse approaches to artificial intelligence and machine learning in the updated data-centered era of the healthcare system. A vast quantity of health data is routinely collected and hard to obtain any helpful information every day. Currently, BDA offers several services satisfaction with the clinical system to identify censorious diseases at an early stage and deliver appropriate services to all patients on time. Various BDA tools play an essential role in quickly examining several clinical data
An Epidemic Graph's Modeling Application to the COVID‐19 Outbreak: Concepts, Methodologies, Tools and Applications
No full textThe furious disease named COVID-19 is an outbreak in the current scenario. To control the spreading of this disease, new models were developed which utilized established methodologies to analyze how different containment strategies will influence the spread of the virus. It presents a novel machine learning approach that can estimate any epidemiological model's parameters based on two types of information: either static or dynamic. It primarily utilizes the Graph model using deep learning approaches and Long-term memories (LSTMs) to obtain mobility data's spatial and temporal properties of SIR and SIRD models. It runs and simulates using data on the Italian COVID dynamics and compares the model predictions to previously observed epidemics
A new balloon dissector for totally extraperitoeneal hernia repair
No full textBackground: Balloon dissectors (BD) find their use in totally extraperitoneal (TEP) and retroperitoneoscopic procedures. Commercial BD is prohibitively expensive. The author uses an indigenously assembled BD and describes the same. Material and Methods: The author assembles the BD by tying glove-fingers on an NG tube and then tying this assembly in the concavity of a Kelly′s clamp, premounted with peanut gauze (KC-BD). Results: The author has used it in the last 75 cases of TEP. A large working space is created, without any iatrogenic injuries or balloon rupture. This cheap indigenous BD can be assembled easily and in no time at all. Conclusions: KC-BD offers several advantages because of its unique design. It is effective, totally nontraumatic, inexpensive, and easy to assemble
Techniques for the performance analysis of queueing networks
No full textETDs are only available to UIUC Users without author permissionU of I OnlyAnalyzing the performance of queueing networks that do not admit a product form solution is a challenging problem. In this thesis we present some tools for doing so. Our attention is restricted to Markovian queueing networks.We first present a technique for bounding the performance of such networks. Assuming a steady state for functionals of the state, we obtain linear programs which bound the performance. This technique is illustrated using quadratic functionals to bound the performance of a class of Markovian queueing networks called reentrant lines. We also show how this technique may be applied to bound throughput and blocking probabilities in networks with buffer capacity constraints. In some cases bounds obtained using multimedial functional of the state are shown to approach the exact value when the degree of the multimedial increases.We also study another important technique for the analysis of queueing networks, namely, the fluid limit approach. This approach is used to establish the stability of a class of policies called Fluctuation Smoothing policies for open reentrant lines. We also show how the fluid limit approach can be used to obtain the asymptotic performance of closed queueing networks in heavy traffic. We then use fluid limits to establish the efficiency of Fluctuation Smoothing policies for closed reentrant lines, as well as the Harrison-Wein policy for two station closed reentrant lines.Made available in DSpace on 2011-05-07T14:22:46Z (GMT). No. of bitstreams: 2
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Efficient uniformly convergent numerical methods for singularly perturbed parabolic reaction–diffusion systems with discontinuous source term
No full textFinanciación de acceso abierto proporcionada por los Fondos Europeos FEDER y la Junta de Castilla y León en el marco de la Estrategia de Investigación e Innovación para la Especialización Inteligente (RIS3) de Castilla y León 2021-2027[EN] This article is concerned with the construction and analysis of efficient uniformly convergent methods for a class of parabolic systems of coupled singularly perturbed reaction–diffusion problems with discontinuous source term. Due to the discontinuity in the source term, the solution to this problem exhibits interior layers along with boundary layers, which are overlapping and interacting in nature. To achieve an efficient numerical solution for the coupled system under consideration, at interior points (excluding the interface point) we employ a special finite difference scheme in time (where the components of the approximate solution are decoupled at each time level) and the central difference scheme in space; for mesh points on the interface, a special finite difference scheme decoupling the components of the approximate solution is developed. A rigorous error analysis is provided, establishing the method’s uniform convergence. In terms of computational cost, our numerical methods are more efficient than existing approaches for solving this class of problems. Finally, we provide numerical results to substantiate the theory and showcase the efficiency of our methods.The first author expresses gratitude to the Indian Institute of Technology (BHU) for
the assistance provided during the work tenure. Sunil Kumar extends thanks to the Science and Engineering
Research Board (SERB) for awarding the research support grant CRG/2023/003228 for this work
Employee’s Attitude towards ERP Implementation at Work Place: A Case Study
No full textIn the first growing environment, the business will grow with the technology and the technological implementation. There must be an alignment between business strategies and IT strategies. ERP is integrated software which support to all functional domain. This software helps from raw material management, inventory management, and production management, Marketing Management, Human resource management, Finance management and customer management etc. The leading corporate world is not able to implement the ERP software in their company. This is the study through which author interested to find out the factors affecting employees attitude towards ERP implementation
DOI: 10.17762/ijritcc2321-8169.15023
Overview of WSN Infrastructure Models, Design & Management
No full textNetwork management for Wireless Sensor Network Infrastructure is a challenging area where the management operation is to run on very minimal or zero cost. The network data packets routing costs more, hence managing this unstructured network improves the network efficiency and extend the network life time. The deployed sensor nodes have a fixed battery life and there are also some attempts made to manage this WSN network efficiently. In this paper we focus on different networking parameters used to measure efficiency, different network functionality and different design structure evolved in this area
Two approaches to the hierarchical solution of constraint satisfaction problems
No full textConstraint Satisfaction Problems (CSPs) involve assigning values to a finite set of variables from their finite domains such that a finite set of constraints is satisfied. Graph coloring, Scheduling and Time-table design are some of the commonly occurring CSPs. All general solution procedures for CSPs are based upon a combinatorial enumeration of variable bindings, with the addition of clever devices to reduce the number of nodes explored in the corresponding search tree. CSPs, however, are NP-hard, and general-purpose search algorithms are slow. This research explores the application of problem decomposition to construct faster CSP solvers
Previous applications of Problem Decomposition to CSPs have been based upon the representation of a CSP as a Constraint Graph, where nodes represent variables, and arcs represent constraints. A tree-shaped constraint graph, after some preprocessing on the nodes, can be solved without backtrack (Freuder, 82). Researchers have proposed different methods for reducing a constraint graph into a tree of node-clusters, with the cost of solution dependent on the size of the largest such cluster.
Tree-clustering based methods of decomposition fail on CSPs with global constraints, where any cluster including the global constraint has to include all the problem's variables. This thesis proposes reducing redundant constraint-checks as an alternative motivation for problem decomposition in CSPs. The decomposition algorithms developed here are applicable to CSPs unrestricted on constraint arity.
There are three major components to this research. The first is the development of a framework, named bottom-up solution, for solving a CSP through its decomposition. It is aimed at handling decompositions that do not completely partition a problem into independent components. The framework allows for the efficient handling of problem components dependent upon other subproblems. The bottom-up solution framework has been implemented on top of the basic Backtrack and Forward-Check search algorithms.
The thesis then introduces two problem decomposition algorithms aimed directly at reducing redundant constraint checks. The main insight here is that redundant constraint tests are caused by artificial dependencies of constraints on non-argument variables, set up by the serial nature of combinatorial enumeration used as the basis for search algorithms. The decomposition algorithms seek to reduce artificial dependencies in a problem, and the resulting decompositions can be solved in the bottom-up solution framework.
The third component of this thesis focuses on global and high-arity constraints. The complexity of a class of problems with a particular constraint topology is defined by the highest arity of its constraint set. This is reflected in the inability of search algorithms to take advantage of global constraints to reduce the size of the explored search space. This thesis proposes a procedure for the syntactic decomposition of a global constraint in a problem. This decomposition is used to define an abstract problem layer, and a new hierarchical problem which is equivalent to the original problem, and in which the global constraint is replaced with a set of smaller arity constraints.
The problem decomposition techniques are evaluated on random problems and on some sample application domains. In general the decomposition is shown to be more beneficial for problems which, when solved in their original form, exhibit high artificial serial dependencies and produce bushier search trees. Global constraint decomposition is demonstrated on some sample application domains, and shown to significantly reduce search effort.
Constraint satisfaction problems are difficult enough that there do not exist any reliable and effective general purpose problem solving heuristics and evaluation functions. It is therefore significant that the DOI decomposition algorithm proposed in this research is guaranteed never to make the problem harder to solve.
The primary contribution of this research is in the form of new problem solving methods for general constraint satisfaction problems which significantly improve performance, particularly for harder problems. It also extends the current understanding of what makes constraint satisfaction problems difficult to solve and where search algorithms spend their effort. On the more general side, this thesis promotes a deeper understanding of the application and benefits of problem decomposition as a problem solving strategy.Technical report LCSR-TR-25
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