dna sequence, containing all genetic traits is not a functional entity. instead, it transfers to protein sequences by transcription and translation processes. this protein sequence takes on a 3d structure later, which is a functional unit and can manage biological interactions using the information encoded in dna. every life process one can figure is undertaken by proteins with specific functio...

One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data minin...

We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial email, or “spam”, floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the eff...

The academic institutions are focusing more on improving the performance of students using various data mining techniques. Prediction models designed to predict at a very early stage so that preventive measures can be taken beforehand. Various parameters (academic as well non-academic) considered student different classifiers. Normally, given weightage in predicting student. This paper compares...

Ophthalmic diseases are a significant health concern globally, causing visual impairment and blindness in millions of people, particularly dispersed populations. Among these diseases, retinal fundus leading cause irreversible vision loss, early diagnosis treatment can prevent this outcome. Retinal scans have become an indispensable tool for doctors to diagnose multiple ocular simultaneously. In...

Data mining is concerned with applying techniques which automatically induce new patterns or knowledge from large data. Typically these patterns are expressed as a descriptive or predictive model. In this work we focus on predictive models which aim to predict a certain characteristic of the data in terms of other known characteristics. Usually one such model is built to perform the task of pre...

We present a Bayesian approach to learning flexible safety constraints and subsequently verifying whether plans satisfy these constraints. Our approach, called the Safety Constraint Learner/Checker (SCLC), is embedded within the Generalized Integrated Learning Architecture (GILA), which is an integrated, heterogeneous, multi-agent ensemble architecture designed for learning complex problem solv...

In ensemble teacher learning, ensemble teachers have only uncertain information about the true teacher, and this information is given by an ensemble consisting of an infinite number of ensemble teachers whose variety is sufficiently rich. In this learning, a student learns from an ensemble teacher that is iteratively selected randomly from a pool of many ensemble teachers. An interesting point ...