Neelu Jyothi Ahuja

Experimental analysis of traditional classification algorithms on bio medical dtatasets

Data classification in medical field is distinct from that in other fields, because the medical data are heterogeneous, skewed and complex in nature and medical data classification involves multi class classification. In this paper we present the experimental analysis of well-known traditional classification algorithms on bio-medical datasets in order to observe their performance. This experimental analysis will provide deeper insight in designing the efficient classification algorithm for bio medical data.

Genetic Algorithms-Fuzzy Based Trade-Off Adjustment Between Software Complexity and Deliverability

The paper highlights the issue of the Trade-off between Software Deliverability and Complexity. It uses the technique of Messy Algorithm to generate chromosomes containing Fuzzy rules derived under various combinations. The proposed system makes the combination of Complexity and Deliverability more transparent and interoperable.

Biodiversity and Conservation of Medicinally Important Plants by Cultivation Method of Shivalik Hills of Sahaspur Block, Dehradun, Uttarakhand

The present study has been conducted in the Misras Patti gramsabha of Sahaspur block, Dehradun, where we found that people are suffering from high human wildlife conflicts, less market knowledge and availability, unirrigated land area and changing their land use pattern beyond agriculture. It is found that adjoining forest of this gramsabha rich in biodiversity of medicinal and aromatic plants and some rare and endangered species are found such as Gloriosa superba, Tinospora cordifolia, Rauvolfia serpentine Development of agro-technology and cultivation of medicinal and aromatic plants can form one of the important components for the socio-economic development of native communities as an income-generating resource.

Improvement and Approval of Impediment Recognition and Activity for Power Window

The paper focuses to bring high-quality innovative products based on automotive power window control system. Currently, market is moving very rapidly and technology drives the use of simulation models and different validation techniques for model design and rapid realization process. A model-based design technique provides higher efficiencies in product development cycle that boost the technocrats to deliver outcome on time and satisfies initial design requirements and their validation. This paper applies the model-based design model and also develops prototype and appropriate code from a model-based development tool code generation automatically. The research paper emphasis on the modeling and validation of obstacle detection and appropriate action on automotive power window and implementation on electronic control unit based automotive power window. The main objective of ECU-based power window system is to make automatic or circuit-based approach to raise up glass door and to lower down the glass door with help of an appropriate driver and passenger switches or the use of appropriate sensors like carbon dioxide sensor, thermocouple, current sensor, and proximity sensor to replace the conventional hand-turned crank handle manual automotive window, and whenever obstacle is detected, power window should lower down by 10 cm. Automatic power window control system consists of power electronic circuit, DC motor, and control algorithm. The control algorithm senses soft obstructions and hard obstructions and accordingly controls speed of DC motor as well as orientation control of DC motor that moves the window glass frame downward of 10 cm when any obstruction is detected. In other way the power window ECU faculties hindrances and with help of calculations it is keeping up information which additionally control the engine development operation in three distinctive memory cushions that are routinely refreshed by the information beats that are identified with DC engine speed control. The whole framework is outlined as a clever control framework by applying number of conditions which results to the development of the power window.

Characterization of Human Knowledge for Intelligent Tutoring

Seismic data interpretation and subsurface mapping are key skills to analyze subsurface geology. They form the basis for the decision concerning hydrocarbons exploration and extraction. Interpreting a seismic graph, with perfection, needs expert knowledge. The knowledge of seismic data interpretation used in exploration industry is largely individualistic, with each human expert using his/her own set of mental database of interpretation rules developed over years of experience. For the lack of appropriate structure and formalization, this essential body of knowledge is unable to smoothly percolate to the next generation of seismologists, who are expected to deliver reasonable accuracy in their interpretations, almost immediate to their induction. Characterization of human knowledge is the process of structuring, formalizing and transforming the nature of the knowledge from tacit form to explicit form. Current work presents design and development of intelligent system to characterize this knowledge and deliver it using tutoring strategy exclusively devised as per the learner adjudged learning preference. This prototype additionally also measures learner’s performance and facilitates learning gain. The system has been tested with 16 participants, and the resultant performance is recorded.

Development of Knowledge Capsules for Custom-Tailored Dissemination of Knowledge of Seismic Data Interpretation

Knowledge management system is a repository of factual information. Seismic data interpretation is a field of exploration geophysics, which deals with interpretation of seismic images, to infer subsurface geology and provide information regarding hydrocarbon accumulation. This knowledge of interpretation is rare, expensive and largely individualistic. Lack of formal interpretation rules, causes seismic experts to use their own expertise gained over years of experience, leading to uncertainty. In current work a knowledge management framework is proposed, which initiates with the knowledge engineer gathering tacit knowledge from seismic experts, followed by a knowledge manager, synchronizing, sequencing, formalizing and organizing it in explicit form to develop a knowledge capsule, to facilitate its sharing through tutoring. Knowledge capsules have been refined to effectively suit different levels and knowledge grasping preferences of novice seismologists.