Mohsen Rahmani

Arak University · Department of Computer Engineering

The personalized recommender systems provide favorite services based on user preferences and interests. Due to the user's interests changing over time; hence the recommender system must be tracking these changes automatically; to overcome the research gap and col start problem in the current study, we suggest a framework to create an adaptive user...

Many current multivariate filter feature selection approaches consider redundancy and relevance between features and class vectors simultaneously. However, these multivariate filter algorithms calculate the suitability of features by only the intrinsic characteristics of the data. In this paper, we suggest a new distributed framework to offset the...

Despite ensemble learning has recently been usefully applied in feature selection (FS) models, there are some issues such as the diversity and its effects on model performance that need to be more investigated. Diversity is a crucial property in the success of ensemble FS models, so that ignoring it and trying to improve only accuracy makes the mod...

p>This is an original reasearch work.</p

Applying ensemble feature selection (EFS) models in various problems has not been actively discussed, and there has been a lack of effort to make it applicable in the situations such as distributed environments. Due to restrictions of centralized algorithms such as their poor scalability in the high dimension data and also distributed nature of som...

Feature selection plays an important role as a preprocessing step for pattern recognition and machine learning. The goal of feature selection is to determine an optimal subset of relevant features out of a large number of features. The neighborhood discrimination index (NDI) is one of the newest and the most efficient measures to determine distingu...

Solving many-objective optimization problems has become one of the most popular research areas in recent years due to its ever-increasing applications in industries and other fields. In this paper, a novel two-phase hybrid feeder (TPHF) is proposed to provide elite solutions for selection mechanism of many-objective optimization algorithms (MaOAs)...

This paper proposes an improved Bayesian scheme by focusing on the region in which Bayesian may fail to correctly identify labels and improve classification performance by handling those errors. Bayesian method, as a probabilistic classifier, uses Bayes’ theorem to calculate the probability of an instance belonging to a class, where the class label...

Feature selection is exposed to the curse of dimensionality risk, and it is even more exacerbated with high-dimensional data such as microarrays. Moreover, the low-instance/high-feature (LIHF) property of microarray data needs considerable processing time to do some calculations and comparisons among features to choose the best subset of them, whic...

Due to the difficult educational condition caused by the COVID-19 pandemic, the design and development of mobile phone apps and technologies for teaching and learning are being more considered. Moreover, technology-based infrastructure for the use of distance education is developing rapidly. In this paper, an Android application called Triangular A...

The personalized recommender systems provide user-related services based on user preferences; these preferences are recorded in an individual profile. Therefore, the more complete and precise each user profile leads more successful the recommendation process. The people’s interests change over time though traditional researches do not follow these...

Tabriz Journal of Electrical Engineering

The role of email in communication is seriously threatened by a phenomenon called spam. So far, many methods have been proposed to deal with this phenomenon, one of the most important of which is to classify emails based on their content into two categories: spam and non-spam. Content-based classification mechanisms use the words as features, where...

Due to the increasing growth of data, many methods are proposed to extract useful data and remove noisy data. Instance selection is one of these methods which selects some instances of a data set and removes others. This paper proposes a new instance selection algorithm based on ReliefF, which is a feature selection algorithm. In the proposed algor...

Products and web pages are the main components of the e-commerce data knowledge and the relationship among them is an important issue to be highly considered in recommender systems. This study aims to focus on the similarity and complementarity relationships among the products that have wide applications in the recommender systems. In the previousl...

Recommender systems have emerged in the e-commerce domain and have been developed to actively recommend appropriate items to online users. The use of recently developed hybrid recommendation systems has helped overcome the main drawbacks of Content-Based Filtering (CBF) and Collaborative Filtering (CF). In hybrid recommendation systems that combine...

Recommender systems have emerged in the e-commerce domain and are developed to actively recommend the right items to online users. Traditional Collaborative Filtering (CF) recommender systems recommend the items to users based on their single-rating feedback which are used to match similar users. In multi-criteria CF recommender systems, however, m...

One of the commonly used techniques to verify software and hardware systems which have been specified through Graph Transformation System (GTS), especially safety critical ones, is model checking. However, the model checking of large and complex systems suffers from the state space explosion problem. Since Genetic Algorithm (GA) is a heuristic tech...

Two-microphone binary mask speech enhancement (2mBMSE) has been of particular interest in recent literature and has shown promising results. Current 2mBMSE systems rely on spatial cues of speech and noise sources. Although these cues are helpful for directional noise sources, they lose their efficiency in diffuse noise fields. We propose a new syst...

Control of seismic shake table in order to track the predefined earthquake profile is a key concern in design of seismic shake tables. This paper proposes a vision-based real time displacement measurement system using image processing techniques to control a laboratory-scale seismic shake table. The shake table is controlled via a fuzzy-supervisory...

Ideal binary mask speech enhancement is shown to increase the speech quality as well as speech intelligibility. But, this property depends highly on the accurate separation of speech and masker time-frequency units of the input spectrum, which is a difficult task in real situations. Ordinary binary mask methods are single-microphone methods and so,...

Voice Activity Detectors (VADs) are widely used in speech processing applications such as speech enhancement and coding. In speech enhancement systems, we use VAD in order to obtain more accurate estimation of noise. Voice activity detection methods usually work in time or frequency domains. In this paper, we propose two approaches for voice activi...

Local search is known as an effective technique for solving combinatorial optimization problems. However, there are few tools that provide high level facilities for users to implement their own local search algorithms. In this paper, we introduce ZLoc, a new C++ library for local search. ZLoc supports many high-level features usually found in model...

Today, model refinement has a key role in software engineering. It is very important to find an approach to add details to the abstract models. This stepwise refinement should continue till the final software is developed. Since this process in most of the cases is boring, long and error prone, it is crucial to find an automatic yet precise approac...

Nowadays UML has been accepted in both academia and industry as a de facto modelling language to develop software systems. As relational databases are widely used for developing software systems, thus, it is a challenge to derive relational tables from designed models. In this paper, we present a formal yet automatic approach to extract relational...

In this contribution, a novel dual-channel speech enhancement technique is introduced. The proposed approach uses the dissimilarity between the power of received signals in the two channels as a criterion for speech enhancement and noise reduction. We claim that in near field conditions, where the distances between microphones and sound source are...

Decision trees are one of the most effective and widely used induction methods that have received a great deal of attention over the past twenty years. When decision tree induction algorithms were used with uncertain rather than deterministic data, the result is a complete tree, which can classify most of the unseen samples correctly. This tree wou...

Some two-microphone noise reduction techniques that work in the frequency domain exploit coherence function between two noisy signals. They have shown good results when noise signals on two sensors are uncorrelated, but their performance decreases with correlated noises. Coherence based methods can be improved when the cross power spectral density...

Among various speech enhancement methods, two-microphone noise reduction systems are utilized for their low cost implementation and acceptable performance. Coherence-based methods are well known as efficient two-microphone noise reduction techniques. These techniques, however, do not work well when the received noise signals are correlated. Coheren...

In this paper, a new objective measure is proposed to assess the amount of the wide-band musical noise in an acoustic signal. The measure uses time and frequency characteristics of the musical noise to determine the musicalness of the residual noise. The proposed measure uses noise masking thresholds to measure how much audible the musical noise co...

This paper deals with the problem of speech enhancement in near field condition, when two microphones are available. Proposed technique relies on the difference in power of received signals at the two microphones. This difference is employed to estimate the clean speech signal power. The method has the capability of dealing with non-stationary nois...

Among various speech enhancement methods, dual-microphone methods are of a great importance for their low cost implementation and for exploiting spatial-filtering benefits of the microphone arrays. Coherence based methods are well-known as efficient two-microphone noise reduction techniques. These techniques do not work well, when received noise si...

In this paper, we propose a real-time architecture for a dual-microphone speech enhancement to be employed in a hands-free mobile communication. The proposed method is based on a new SNR estimation and a coherence based Voice Activity Detector (VAD). The enhancement system was implemented using MATLAB tools and DSP processors. The results show that...

A dual channel speech enhancement technique for hands-free applications is presented. The proposed method is based on the difference between the powers of two received signals in near-field condition. The subtraction of power spectral densities of noisy signals at two microphones is employed as a criterion for noise reduction. Obtained results show...

Among various enhancement methods, dual microphone methods are utilized for their low cost implementation and use of spatial noise reduction. In this paper, we present a double microphone noise reduction method for nearby microphones. Our method is based on a modified coherence based filter which reduces correlated noises using cross spectral subtr...

Array processing involves the use of multiple microphones to receive or transmit a signal carried by propagating waves. The classical method for source localization with microphone array is assumed to be Time Delay Estimation (TDE). Recently more attention is paid to another technique, interaural level difference (ILD) which estimates location of s...

The performance of two-microphone coherence based methods degrades if two captured noises are correlated. The Cross Power Spectrum Subtraction (CPSS) is an adapted coherence method for noise correlated environments. In this paper, we propose a new technique for estimation of speech cross power spectrum density and we exploit it in CPSS. The propose...

Speech enhancement systems performing in Fourier or wavelet domain usually generate musical noise and distortion. It is possible to reduce musical noise and speech distortion by shaping residual noise. In this paper, we propose to implement a noise shaping method for a wavelet based noise reduction system. For noise shaping, we propose a noise clas...

Linear discriminant analysis (LDA) is a feature selection method in speech recognition. LDA finds transformations that maximizes the between-class scatter and minimizes within-class scatter. This transformation can be obtained in a class-dependent or class independent manner. In this paper, we propose a method to use class-dependent LDA for speech...

In this paper we present a novel technique which localizes sound source with acceptable accuracy and low time complexity. In this technique, contrary to former localization techniques in 3-D space, only three microphones are sufficient. Instead of searching the entire space domain for the sound source, we first estimate an approximate point of the...

In some telephony headsets the microphone is not close enough to the speaker's mouth due to size limitation. Thus the microphone captures background noise in addition to useful speech. There is a need to enhance the received signal in order to improve the voice quality in these systems. Among various enhancement methods, dual microphone methods are...

The conventional view on the problem of robustness in speech recognition is that performance degradation in ASR systems is due to mismatch between training and test conditions. If problem of robustness in ASR systems is considered as a mismatch between the training and testing conditions the solution would be to find a way to reduce it. Common appr...