Wide Range Of Performance Indicators Research Articles

Optimized deep learning networks for accurate identification of cancer cells in bone marrow

Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for distinguishing between malignant and healthy bone is essential. Bones that have cancer have a different texture than bones in unaffected areas. Diagnosing hematological illnesses relies on correct labeling and categorizing nucleated cells in the bone marrow. However, timely diagnosis and treatment are hampered by pathologists' need to identify specimens, which can be sensitive and time-consuming manually. Humanity's ability to evaluate and identify these more complicated illnesses has significantly been bolstered by the development of artificial intelligence, particularly machine, and deep learning. Conversely, much research and development is needed to enhance cancer cell identification—and lower false alarm rates. We built a deep learning model for morphological analysis to solve this problem. This paper introduces a novel deep convolutional neural network architecture in which hybrid multi-objective and category-based optimization algorithms are used to optimize the hyperparameters adaptively. Using the processed cell pictures as input, the proposed model is then trained with an optimized attention-based multi-scale convolutional neural network to identify the kind of cancer cells in the bone marrow. Extensive experiments are run on publicly available datasets, with the results being measured and evaluated using a wide range of performance indicators. In contrast to deep learning models that have already been trained, the total accuracy of 99.7% was determined to be superior.

Ethnicity Detection with Convolutional Neural Network (CNN): Bangladesh Perspectives

Ethnicity detection, or the automatic determination of people's ethnic backgrounds using visual data, has become important in a number of disciplines, including social sciences, demographics, and computer vision. With a focus on Bangladesh's complex cultural landscape, we describe in this paper a novel method for ethnicity detection using convolutional neural networks (CNNs). We gathered a dataset of photographs depicting members of the three main ethnic groups living in the Chittagong hill neighborhood: Chakma, Marma, and Tripura. Utilizing the DenseNet121 model's robust feature extraction skills, we customized the architecture to meet the needs of our particular ethnicity detection application. A wide range of performance indicators were used to train and assess our unique CNN model. The outcomes illustrate accurate ethnicity detection technology's potential for identifying and resolving social inequities while showcasing a promising level of accuracy. To address possible biases in the model's prediction, ethical considerations are also covered. Overall, this work advances knowledge of Bangladesh's ethnic variety and demonstrates the potential of CNNs for ethnicity detection. The results provide up new avenues for investigation and applications that advance a more diverse and inclusive society.

Simultaneous multi-response Jaya optimization and Pareto front visualization in EDM drilling of MoSi2-SiC composites

This study delves into the EDM of the MoSi2-SiC intermetallic ceramic composite. Dielectric pressure (DP), spark gap voltage (Sv), and spark gap speed (v) are all variables that affect the quality of a spark. Many performance metrics, such as material removal rate, surface roughness, over cut, circularity, and cylindricality, are measured. By considering each possible response as a separate target, Mo-Jaya (multi objective) may be used to optimize a wide range of performance indicators. The results of the multi-objective optimization problem are presented in the form of a set of Pareto optimum strategies. Three-dimensional (3D) graphics show how each response changes along the Pareto front. The optimal solutions for each combination of replies are selected from the whole solution. The outcome of a global optimization that factored in all five replies is also shown.

Blockchain technology in supply chains

Aim: Blockchain technology (BCT) is a relatively new technological development, promising strong gains in the areas of product traceability and visibility, end-to-end coordination (E2E), governance, and efficiency of supply chains. The aim of this study is to gain a better understanding of the impact of BCT on these performance measures.
 Design / Research methods: Hypotheses were tested with survey data from 119 supply chain professionals from Northern American SMEs.
 Conclusions / findings: The results confirm the positive impacts of BCT on all performance measures. E2E coordination is the integration of information, goods, and money within an organisation or supply chain. E2E coordination appeared to benefit from the use of BCT, enabling information sharing in a safe way. The findings suggest that BCT use fosters E2E coordination, which in its turn also positively affects financial performance.
 Originality / value of the article: Despite the increasing interest in and use of, BCT, there is little empirical evidence for the effect on supply chain performance. Many studies are anecdotal and lack empirical evidence.
 Implications of the research (if applicable): Companies should acknowledge the impact of BCT use on the various supply chain performance measures. Implementing and using BCT is likely to foster improvement in a wide range of performance indicators. 
 Limitations of the research (if applicable): Companies use different types, versions, varieties, and forks of blockchain, all having their own strong and weak points. Future studies could investigate and include the nuances within different forks of BCT.

Financial Performance Evaluation of Turkish Construction Companies in Istanbul Stock Exchange (BIST)

Financial performance evaluation of construction companies is a kind of multi-criteria decision making (MCDM) problem. The decision makers need a wide range of performance indicators in order to ensure that appropriate decisions can be made. The aim of this study is to evaluate the financial performance of Turkish construction companies whose shares are publicly traded in Istanbul Stock Exchange (Borsa Istanbul-BIST) during 2012-2015 period using multi criteria decision techniques. For this purpose, Grey Relations Analysis and Analytical Network Process were used in examining the data related to these companies. This model is applied to a case study for the financial performance evaluation of 7 construction companies (Anel, ED?P, Enka, Kuyumcukent, ORGE, SAN-EL, Ye?il Yap?) in Turkey. Financial performance indicators namely Asset Growth Rate, Operating Costs/Net Sales, Return on Asset, Net Profit Margin, Return on Equity, Current Ratio, Long Term Assets/Total Assets and Quick Ratio are used for ranking the firms. The findings of this paper would demonstrate the financial performance of the Turkish construction companies and would help construction companies’ managers and investors to evaluate the performance of each company and compare it with their competitors’.

Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis.

Program semantics is a promising recent research thread in Genetic Programming (GP). Over adozen semantic-aware search, selection, and initialization operators for GP have been proposed to date. Some of these operators are designed to exploit the geometric properties of semantic space, while others focus on making offspring effective, that is, semantically different from their parents. Only asmall fraction of previous works aimed at addressing both of these features simultaneously. In this article, we propose asuite of competent operators that combine effectiveness with geometry for population initialization, mate selection, mutation, and crossover. We present atheoretical rationale behind these operators and compare them experimentally to operators known from literature on symbolic regression and Boolean function synthesis benchmarks. We analyze each operator in isolation as well as verify how they fare together in an evolutionary run, concluding that the competent operators are superior on awide range of performance indicators, including best-of-run fitness, test-set fitness, and programsize.

A Scalable and Accurate Nonsaturated IEEE 802.11e EDCA Model for an Arbitrary Buffer Size

IEEE 802.11e EDCA induces service differentiation by appropriate joint tuning of four adjustable contention parameters. Existing and emerging work has devoted considerable attention to the nonsaturated performance of EDCA networks due to the difficulty of predicting the joint influence of the four parameters. However, most existing nonsaturated EDCA models adopt complex extensions of a Markov-chain approach. In sharp contrast, this paper invokes an extension of a renewal-reward approach. Our extension has the following unparalleled advantages: good scalability, ease of understanding, fast computation speed, high accuracy, models joint differentiation of all four parameters, captures the impact of an arbitrary buffer size, and predicts a wide range of performance indicators including the buffer overflow probability and the MAC access delay distribution. Our nonsaturated EDCA model is a nontrivial augmentation of our previously proposed nonsaturated DCF model. Our results indicate that if we accurately model the nonsaturated collision probability, the same formulas used for the saturated performance descriptors can produce accurate results for nonsaturated operation, and therefore it is unnecessary to construct specific formulas for nonsaturated performance descriptors, as done in previous work. To illustrate the utility of our model, we also develop an admission control policy based on the proposed EDCA model for a CWmin-differentiation system. Simulations validate that this policy enables the system to run slightly below a critical point, beyond which the system performance deteriorates drastically.

Beyond Generating Transit Performance Measures

In recent years, the use of performance measures for transit planning and operations has gained a great deal of attention, particularly as transit agencies are required to provide service under increasing demand and with diminishing resources. The widespread application of the technologies of intelligent transportation systems to transit encourages automating the generation of comprehensive performance measures. In Portland, Oregon, the local transit provider, Tri-County Metropolitan Transportation District of Oregon (TriMet), has been on the leading edge of the transit industry since it implemented its bus dispatch system (BDS) in 1997. The BDS comprises automatic vehicle location on all buses, a radio communications system, automatic passenger counters on most vehicles, and a central dispatch center. Most significant, TriMet developed a system to archive all its stop-level data, which are then available for conversion to performance indicators. In the past decade, TriMet has used this system extensively to generate performance indicators through monthly, quarterly, and annual reporting. TriMet generates a wide range of performance indicators, yet an opportunity remains to explore metrics beyond general transit performance measures (TPMs). On the basis of an analysis of 1 year of archived BDS data for all routes and stops, the power of using visualization tools to understand the abundance of BDS data is demonstrated. In addition, several statistical models are generated to demonstrate the power of statistical analysis in conveying valuable and new TPMs beyond what is currently generated at TriMet or in the transit industry in general. It is envisioned that systematic use of these new methods and TPMs can help TriMet and other transit agencies improve the quality and reliability of their service.

Review of TRESIS as a Policy Advisory Tool for Evaluating Land Use and Transport Interaction

The purpose of this paper is to review TRESIS (Transportation and Environment Strategy Impact Simulator) model system developed by Hensher and Ton (2002) as a policy advisory tool to evaluate the impact of policy instruments on a wide range of performance indicators. In this paper, we identify the key issues and associated criteria related to the use of urban land use and transport models for supporting policy analysis task. We then propose a policy review framework named SEEBEST as a means to identify and classify a wide range of key policy instruments and performance indicators supported by an urban model. The main focus is on the review of TRESIS, based on SEEBEST. A summary of main findings and further research questions are included in the conclusion.

An economist's perspective on irrigation performance assessment: With examples from large scale irrigation in Morocco

This paper accepts that assessing the performance of irrigated agriculture is difficult because being multi-dimensional it requires a multi-disciplinary approach. Public irrigation schemes may have a number of often competing objectives and are assessed by interest groups with differing values and perspectives. A wide range of performance indicators are thus required. Some of these and the complex inter-relationships that may exist in the system are illustrated.

Social Service Studies and the Explanation of Policy Outcomes

At least two theoretical implications of studies of social services are not given their full weight in much political science writing. The first is that the influence on policy outcomes of factors external to the organiztion have their influence at a variety of levels and on a multeity of groups of actors. For instance, the operating environment is argued to affect provision through its effects on the characteristics of potential consumers and therefore on the demand they make of the organization, the professionals and others at the client interface, local teams, departments, and authorities. In particular, the literature has stressed the impact of demands made by consumers on the structures of outputs. In doing so, it has begun to integrate theories about the causes of variations in demand into the theory about causes of variations in provisions. This is reflected in the modelling literature which draws on the social service studies. The models focus at a level of explanation at which it is possible to look at a wide range of performance indicators and to draw on a great deal of detailed evidence, and face more squarely issues of causal priority in the determination of outcomes.