Earticle

The Intensive Care Unit (ICU) bedside monitors present the medical staff with large amounts of continuous data which can create a number of challenges. If the data is transmitted as part of a telemedicine system then the large volume of data can put pressure on bandwidth and affect the quality of service of the network. Another challenge is that the large volume of data has to be interpreted by medical staff to make a patient state assessment. In this paper we propose a time series analysis technique called data wavelets to derive trends in the data – this acts as a form of data compression for telemedicine and improves the quality of service of a network and also facilitates clinical decision support in the form of qualitative reasoning for patient state assessment. Our approach has been successfully applied to cardiovascular data from a neonatal ICU.

The process of data mining comprises of seven major steps: (1) data integration, (2) data transformation, (3) data cleaning, (4) data selection, (5) pattern extraction or knowledge mining, (6) pattern evaluation, and (7) knowledge presentation. Steps 1 to 4 are pre-data mining, whereas steps 6 and 7 may be viewed as post-data mining. Therefore, the seven major steps can be grouped into pre-data mining, mining, and post-data mining. We focus our study on the post-data mining processing. Most data mining systems finish their processing at the knowledge presentation step. Our work further the regular post-data mining processing to the step of automatic knowledge deployment. This paper illustrates the knowledge deployment step in which its input is the induced knowledge, in the formalism of classification rules. These rules are evaluated and filtered on the basis of coverage measurement, that is from all the training cases, how many cases are covered by the rule. High coverage rules are transformed into decision rules to be used by the inference engine of the expert system. This post-data mining processing leads to a new design of the next generation rule-based expert system in a medical domain. It is a new idea in that in addition to the set of predefined rules in the knowledge base, the system includes rules that are automatically induced from the database instances. We design the inductive expert system such that the inductive process has been done through the tree-based knowledge discovery technique. Probabilistic decision rules are then transformed from the induced decision tree. The induced, as well as predefined, rules together form a knowledge base for the inductive expert system. Another feature of our system is the inference engine that can be created automatically. The system is intended to support decision making in biomedical informatics. The accuracy of recommendation given by the expert system is evaluated and compared to other three classification systems: decision-tree induction, rule induction, and neural network. The experimental results confirm the high accuracy of our inductive expert system and the automatically created knowledge base.

The present paper deals with the detailed study of a degraded miscellaneous forest of Manikpur village forest committee of Mandla district of Madhya Pradesh, India by examining various socio-economic needs of people, documentation of plant diversity of the area in both qualitative and quantitative terms, regeneration behavior of various timber and non timber species, phyto-sociological structure of ground vegetation including herbs, shrubs and grasses, biomass production, fertility status of soil, identification of different land use zones for obtaining optimum productivity through their effective utilization etc. Innovative site specific eco-silvicultural options encompassing social, economic, cultural, spiritual, ecological and institutional aspects of management, were prescribed to ensure multi-product flow of forest resources, optimum utilization of various land use zones through collaborative efforts of Ministry of Environment and Forests (MOEF) and Ministry of Agriculture, ecological balance or homeostasis of ecosystem through maintaining ecological processes like nutrient cycling and energy flow, and ultimately the prosperity of forest dependent communities.

Recent advancement in the field of life science data mining has inspired researchers and healthcare professionals to apply this novel technology to obtain descriptive patterns and predictive models from biomedical and healthcare databases. The discovery of hidden biomedical patterns from large clinical database can uncover potential knowledge to support prognosis and diagnosis decision makings. However, clinical application of data mining algorithms has a severe problem of low predictive accuracy rate that hampers their wide usage in the clinical environment. We thus focus our study on the improvement of predictive accuracy of the models created from the data mining algorithms. Our main research interest concerns the problem of learning a classification model from a multiclass data set with low prevalence rate of some minority classes. With such data characteristics, directly applying classification data mining techniques such as decision tree induction, regression analysis, neural networks, or support vector machines yields a suboptimal model in terms of predictive accuracy rate. To remedy the imbalanced class distribution among data instances, we apply random over-sampling and synthetic minority over-sampling (SMOTE) techniques to increase the predictive performance of the learned model. In our preliminary study, we consider specific kinds of primary tumors occurring at the frequency rate less than one percent as rare and minority classes. From the experimental results, the SMOTE technique gave a high specificity model, whereas the random over-sampling produced a high sensitivity classifier. The precision performance of a classification model obtained from the random over-sampling technique is on average much better than the model learned from the original imbalanced data set. We then extend our study by designing the heuristic based method to cope with the abundance of irrelevant feature that causes the decrease in learning time and sometimes lower the accuracy rate. The over-sampling technique and the heuristic-based feature selection are coupled as a data preparation method to deal with imbalanced data sets with many irrelevant features. The experimental results on arrhythmia and communities-and-crime data sets show significant improvement on the predicting accuracy, specificity, and sensitivity of the induced models.

Buchanania lanzan Spreng (common name - Char, Chironji) belonging to family Anacardiaceae. This plant was first described by Francis Hamilton in 1798. It is a non wood tree species found in deciduous forests throughout the greater part of India and generally attaining a height up to 18m and girth 1.5m. In Madhya Pradesh, it is a common associate of teak, sal and mixed forests. It is used for environmental conservation and in ‘agroforestry system’. It is used as a fuel, fodder specially buffaloes alternative host Kusmi lac insect, and its oil for cosmetic items and soaps. Its oil is also used by tribal as edible oil. Seeds / kernel of Buchanania lanzan are nutritional, palatable and used as a substitute of almonds in confectionery. They yield a fatty oil known as Chironji oil and substitute for olive and almond oils in both confectionery and indigenous medicine used for glandular swellings of the neck (CSIR, 1986). Fruits are laxative and used to relieve thirst burning of body and fever. Kernels of fruits are used as ointment in skin diseases (Das and Agrawal, 1991). Tree of Buchanania lanzan flowers from January to March and fruits ripen in the month of April-June (Troup, 1986). Fruits become red after ripening. The fruit collection take place from April to June. Early harvesting results into low fruit/ seed quality and poor germination potential. In most parts of M. P., fruits of Buchanania lanzan are harvested before ripening. With the result, it fetches low price in the market because of small seed size and low seed quality. In natural forests, its regeneration is vary scanty due to unscientific and pre-mature harvesting of its seeds and site degradation on account of growing biotic pressure. Keeping above in views, there is a need to find out the best harvesting period of Chironji fruit/seed with special reference to seed size, seed weight, biochemistry and germination potential of seed. The present communication deals with morphological, physiological and biochemical study of Buchanania lanzan seed harvested at 7 days interval during its various developing stages from April to May. The fruits were collected from forests of Kundam range in Jabalpur forest division for the present study. The best results in terms of seed size, seed weight, germination percent, oil content etc, were obtained in the fruits harvested in the 2nd week of May.

Modeling natural behaviors of swarming nanorobots is being extensively studied during the last decade. Employing these natural behaviors to nanorobots is considered highly demanded in the medical applications ranging from treating sever diseases to anti-aging treatments. Recent medical applications considers a scenario where a swarm of nanorobots is launched from a starting point into the human body to perform group tasks; these tasks could be detecting cell concentration of a specific chemicals emitted by cells and acting upon findings, or searching particular places in the human body for drug delivery or other certain actions. These applications consider scenarios that emphasize local self-coordination. Yet, these scenarios lack the global view to coordinate globally over long distances to accomplish interactively assigned group task. Considering the scenario of launching a swarm of nanorobot in blood vessel for the purpose of removing cholesterol plaques, a communication model is proposed. The model identifies communication based coordination between nanorobots in the swarm. The proposed model includes both decentralized and centralized communications for possessing both the local information within the swarm and global information for interactive task assignment, task cancellation, re-assigning new task by physicians monitoring task accomplishment. The task is simply searching lipoprotein Cholesterol threshold concentration in a specified location in blood vessel. The experimentation results has shown to be efficient as it overcomes the local minima problem for swarm navigation and problem of using coverage based particle swarm optimization.