Farid Ghareh Mohammadi

Bio: Farid Ghareh Mohammadi is an academic researcher from University of Georgia. The author has contributed to research in topics: Evolutionary algorithm & Steganalysis. The author has an hindex of 9, co-authored 25 publications receiving 138 citations. Previous affiliations of Farid Ghareh Mohammadi include Tarbiat Modares University & Florida State University College of Arts and Sciences.

Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

Abstract: A large number of engineering, science, and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. Therefore, data plays a pivotal role in technologies by introducing several challenges: how to present, what to present, why to present. Researchers explored various approaches to implement a comprehensive solution to express their results in every particular domain, such that the solution enhances the performance and minimizes cost, especially time complexity. In this chapter, we focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data, especially large sets of extracted features for a particular purpose. This motivates researchers to think about optimization and apply nature-inspired algorithms, such as meta-heuristic and evolutionary algorithms (EAs) to solve large-scale optimization problems. Building on the strategies of these algorithms, researchers solve large-scale engineering and computational problems with innovative solutions. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. To that end, researchers try to run their algorithms more than usually suggested, around 20 or 30 times, then they compute the mean of result and report only the average of 20/30 runs’ result. This high number of runs becomes necessary because EAs, based on their randomness initialization, converge the best result, which would not be correct if only relying on one specific run. Certainly, researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms.

DeepMSRF: A Novel Deep Multimodal Speaker Recognition Framework with Feature Selection

Abstract: For recognizing speakers in video streams, significant research studies have been made to obtain a rich machine learning model by extracting high-level speaker’s features such as facial expression, emotion, and gender. However, generating such a model is not feasible by using only single modality feature extractors that exploit either audio signals or image frames, extracted from video streams. In this paper, we address this problem from a different perspective and propose an unprecedented multimodality data fusion framework called DeepMSRF, Deep Multimodal Speaker Recognition with Feature selection. We execute DeepMSRF by feeding features of the two modalities, namely speakers’ audios and face images. DeepMSRF uses a two-stream VGGNET to train on both modalities to reach a comprehensive model capable of accurately recognizing the speaker’s identity. We apply DeepMSRF on a subset of VoxCeleb2 dataset with its metadata merged with VGGFace2 dataset. The goal of DeepMSRF is to identify the gender of the speaker first, and further to recognize his or her name for any given video stream. The experimental results illustrate that DeepMSRF outperforms single modality speaker recognition methods with at least 3% accuracy.

Ontology Summarization: Graph-Based Methods and Beyond

Abstract: Ontologies have been widely used in numerous and varied applications, e.g. to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontol...

A new metaheuristic feature subset selection approach for image steganalysis

Abstract: Processing a huge amount of information takes extensive execution time and computational sources most of the time with low classification accuracy. As a result, it is needed to employ a phase of pre-analysis processing, which can influence the performance of execution time and computational sources and classification accuracy. One of the most important phases of pre- processing is Feature selection, which can improve the classification accuracy of steganalysis. The experiments are accomplished by using a large and important data set of 686 features vectores named SPAM. One of the promising application domains for such a feature selection process is steganalysis. In this paper, we propose a new metaheuristic approach for image steganalysis method for detecting stego images from the cover images in JPEG images using a feature selection technique based on an improved artificial bee colony. Within the ABC structure the k-Nearest Neighbor (kNN) method is employed for fitness evaluation. ABC and kNN have been adjusted together to make an operative dimension reduction method Experimental results demonstrate the effectiveness and accuracy of the proposed technique compared to recent ABC-based feature selection methods and other existing techniques.

An Introduction to Advanced Machine Learning: Meta-Learning Algorithms, Applications, and Promises

Abstract: In Chaps. 3 and 4, we have explored the theoretical aspects of feature extraction optimization processes for solving large-scale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in Mohammadi et al. (Evolutionary computation, optimization and learning algorithms for data science, 2019. arXiv preprint arXiv: 1908.08006; Applications of nature-i nspired algorithms for dimension reduction: enabling efficient data analytics, 2019. arXiv preprint arXiv: 1908.08563) guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learning employs a strict model or embedded engine to train and predict which still fails to learn unseen classes and sufficiently use online data. In this chapter, we introduce these challenges elaborately. We further investigate meta-learning (MTL) algorithm, and their application and promises to solve the emerging problems by answering how autonomous agents can learn to learn?

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

A Brief Review of Domain Adaptation

Abstract: Classical machine learning assumes that the training and test sets come from the same distributions. Therefore, a model learned from the labeled training data is expected to perform well on the test data. However, this assumption may not always hold in real-world applications where the training and the test data fall from different distributions, due to many factors, e.g., collecting the training and test sets from different sources or having an outdated training set due to the change of data over time. In this case, there would be a discrepancy across domain distributions, and naively applying the trained model on the new dataset may cause degradation in the performance. Domain adaptation is a subfield within machine learning that aims to cope with these types of problems by aligning the disparity between domains such that the trained model can be generalized into the domain of interest. This paper focuses on unsupervised domain adaptation, where the labels are only available in the source domain. It addresses the categorization of domain adaptation from different viewpoints. Besides, it presents some successful shallow and deep domain adaptation approaches that aim to deal with domain adaptation problems.

A survey on data‐efficient algorithms in big data era

Abstract: The leading approaches in Machine Learning are notoriously data-hungry Unfortunately, many application domains do not have access to big data because acquiring data involves a process that is expensive or time-consuming This has triggered a serious debate in both the industrial and academic communities calling for more data-efficient models that harness the power of artificial learners while achieving good results with less training data and in particular less human supervision In light of this debate, this work investigates the issue of algorithms’ data hungriness First, it surveys the issue from different perspectives Then, it presents a comprehensive review of existing data-efficient methods and systematizes them into four categories Specifically, the survey covers solution strategies that handle data-efficiency by (i) using non-supervised algorithms that are, by nature, more data-efficient, by (ii) creating artificially more data, by (iii) transferring knowledge from rich-data domains into poor-data domains, or by (iv) altering data-hungry algorithms to reduce their dependency upon the amount of samples, in a way they can perform well in small samples regime Each strategy is extensively reviewed and discussed In addition, the emphasis is put on how the four strategies interplay with each other in order to motivate exploration of more robust and data-efficient algorithms Finally, the survey delineates the limitations, discusses research challenges, and suggests future opportunities to advance the research on data-efficiency in machine learning