Mohammad Saniee Abadeh

Hamidreza Keshavarz, Mohammad Saniee Abadeh, Reza Rawassizadeh

A fundamental challenge for running machine learning algorithms on battery-powered devices is the time and energy limitations, as these devices have constraints on resources. There are resource-efficient classifier algorithms that can run on these devices, but their accuracy is often sacrificed for resource efficiency. Here, we propose an ultra-low power classifier, SEFR, with linear time complexity, both in the training and the testing phases. SEFR is comparable to state-of-the-art classifiers in terms of classification accuracy, but it is 63 times faster and 70 times more energy efficient than the average of state-of-the-art and baseline classifiers on binary class datasets. The energy and memory consumption of SEFR is very insignificant, and it can even perform both train and test phases on microcontrollers. To our knowledge, this is the first multipurpose classification algorithm specifically designed to perform both training and testing on ultra-low power devices.

Mohsen Pourvali, Mohammad Saniee Abadeh

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. The lexical cohesion structure of the text can be exploited to determine the importance of a sentence/phrase. Lexical chains are useful tools to analyze the lexical cohesion structure in a text .In this paper we consider the effect of the use of lexical cohesion features in Summarization, And presenting a algorithm base on the knowledge base. Ours algorithm at first find the correct sense of any word, Then constructs the lexical chains, remove Lexical chains that less score than other, detects topics roughly from lexical chains, segments the text with respect to the topics and selects the most important sentences. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to sate-of-the-art summarization approaches.