Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem

Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.Comment: 6 pages, published in CISS 201

Clustering Method for categorical and Numeric Data sets

Many issues concerned with clustering process are due to large datasets involves. In clustering computation become expensive when there are large data sets involved and work efficiently when there is limited number of cluster with relatively small data set. This paper will present a new technique for clustering for large datasets. That will work efficiently equally with large data set as well as with small data sets. The main idea behind this method is to divide the whole process in two steps. The first step uses a cheap approximate distance measure that divide the data into overlapped subsets we call it stubs. Then in second step clustering is performed for measuring exact distances only between points that occur in common stubs. The stub based clustering approach reduces computation time over a traditional clustering and also increases its efficiency

The Next-Generation of Processes in Supply Chain: Impact on the Health Care Industry

In recent years, there has been a growing focus on the next-generation of supply chain processes in the health care sector, with a great emphasis on the use of data analytics, automation, radio-frequency identification (RFID) and blockchain technology. Data analytics can be used to identify patterns and optimize decision-making in the operational process flow. Automation can be used to streamline processes, reduce errors and improve efficiency. RFID can be used to track the movement of products through all channels of distribution, providing real-time visibility and traceability. Blockchain can be used to create a secure and immutable record of transactions, providing transparency and trust throughout the supply chain. This article will discuss the adoption of these technologies and techniques, and how they have the potential to revolutionize the health care supply chain, making it more efficient, transparent and secure. It will describe the potential of its positive impact on patient care, by ensuring that patients have access to the right products at the right time, and by reducing the risk of counterfeit and contaminated products. Health care organizations need to invest in these technologies, develop newer business models and focus on transforming their operating processes in order to remain competitive in this rapidly changing landscape. It will also provide an overview of the opportunities associated with the adoption of these technologies. Alongside, it will also discuss the implications of these trends for health care organizations and distribution networks.&nbsp

Measuring the Effectiveness of Generic Malware Models

Malware detection based on machine learning techniques is often treated as a problem specific to a particular malware family. In such cases, detection involves training and testing models for each malware family. This approach can generally achieve high accuracy, but it requires many classification steps, resulting in a slow, inefficient, and impractical process. In contrast, classifying samples as malware or be- nign based on a single model would be far more efficient. However, such an approach is extremely challenging—extracting common features from a variety of malware fam- ilies might result in a model that is too generic to be useful. In this research, we perform controlled experiments to determine the tradeoff between accuracy and the number of malware families modeled

Age changes in some linear measurements and secular trend in height in adult Indian women

Decline in linear body dimensions with age has been reported in studies throughout the world. There is sufficient evidence that a portion of the changes in stature in a cross-sectional study may be due to secular trend rather than ageing. In the present study, age changes in linear measurements were studied and an attempt made to partition the age associated decline in stature from that of secular trend in height in younger generation. Data comprised of 126 Maratha women patients living in Government Mental Hospital in Pune, India. Their age ranged from 30 to 70 years. Using stature and sitting height, sub-ischial height was derived for each subject which was used as an indirect measurement to approximate the secular trend in height gain in younger generation. Iliac height- a direct measurement was used to quantify the increase in height in younger cohorts. Analysis showed that almost 71 percent (2.55cms) of statural difference between youngest and oldest age group could be attributed to ageing effect, and remaining 29 percent (1.05cms) to secular trend in those born later in time. The timing of reduction in height appears to be in the fifth decade accelerating in subsequent decades