Sharif Digital Repository

Measurement of efficiency is a significant issue in organizations which is used to improvement efficiency and performance .one of the serious offaires in any country, is banking, inefficiency in which leads to problems and obstacles in developing capital market. In this research, Data Envelopment Analysis models are used to estimate the extent of performance in decision making units (DMU’s) to improve inefficient ones. We change the inputs ( outputs) to a special unit at both the constancy or improvement of efficiency using the DEA models, we estimate the values of output ( input ) levels. Howeve, we assume the data of (the values of input ( output ) levels ) can be either crisp or interval.... 

It has become increasingly important to be able to adapt or construct a software development process based on the specific characteristics of the development project at hand; this has resulted in the emergence of a new branch of study called Situational Method Engineering (SME). Compared with Software Engineering, Situational Method Engineering has not suitably matured, in that many of its deficiencies have not been properly addressed yet; SME approaches are especially deficient in support for modeling, portability, and automation. Model-Driven Development (MDD) has been effectively used for enhancing portability and automation in Software Engineering, and it is also considered as a... 

In this research, an optical processing method for DNA classification is presented in order to overcome the problems in the previous methods. With improving in the operational capacity of the sequencing process, which has increased the number of genomes, comparing sequences with a complete database of genomes is a serious challenge to computational methods. Most current classification programs suffer from either slow classification speeds, large memory requirements, or both. To achieve high speed and accuracy at the same time, we suggest using optical processing methods. The performance of electronic processing-based computing, especially in the case of large data processing, is usually... 

It is well known that data centres consume high amounts of energy, which has become a major concern in the field of cloud computing. Therefore, energy consumption could be reduced by using intelligent mechanisms work to adapt the set of network components to the total traffic volume. SDN is an efficient way to do so because it has many benefits over traditional approaches, such as centralised management, low capex, flexibility, scalability and virtualisation of network functions. In our work will we use the heuristic energy-aware routing (HEAR) model, which is composed of the proposed heuristic algorithm and the energy-aware routing algorithm. This work identifies the unused links and... 

Due to the increasing volume of data in various fields, existing electronic processors face a major challenge. While processing power has increased, solving complex problems in a timely manner remains a major challenge for today's processors. Neuromorphic engineering offers a potential solution by looking to processors found in nature, such as the human brain. This field of research involves investigating natural processors and designing new ones based on these models. To address issues related to manufacturing and integrating transistors, increasing processor costs, and the limitations of Moore's law, it is possible to use analog signals, such as sound or light, instead of electrical... 

The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on feature extraction from the peptide and MHC sequences separately and ignore their valuable binding information. This paper develops a capsule neural network-based method to efficiently capture and model the peptide-MHC complex features to predict the peptide- MHC class I binding. Various evaluations over multiple datasets using popular performance metrics... 

As sequencing technologies have advanced in the field of single cells, it has become possible to investigate complex and rare cell populations and discover regulatory relationships between genes. The detection of rare cells has been greatly facilitated by this technology. However, due to the large volume of data and the complex and uncertain distribution of data, as well as the high rate of technical zeros, the analysis of single cell data clusters remains a computational and statistical challenge. Dimensionality reduction is a significant component of big data analysis. Machine learning methods provide the possibility of better analysis by reducing the non-linear dimensions of data. A graph... 

Motif finding problem in biology is a search for repeated patterns to reveal information about gene expression, one of the most complex subsystems in genomics. ChIP-seq technology abled researchers to investigate location of protein-DNA interactions but analyzing downstream results of such experiments to find actual regulatory signals in genome is challenging. For many years, applications of motif finding had models based on limiting assumption as an exchange for lower computational complexity. Results: AKAGI program is build upon upgraded methods and new general models to investigate statistical and experimental evidences for accurately finding significant patterns among biological... 

Predicting the binding affinity of drug molecules and proteins is one of the most important stages of drug discovery, development and screening, for which numerous laboratory, simulation and computational solutions have been provided. Laboratory and simulation methods require molecular structures, are time and financial expense, and computational methods do not provide accurate predictions. Therefore, the use of deep neural networks in extracting features from data with a simpler and more accessible structure of protein sequences and molecules solves these challenges with lower cost and higher accuracy. In this article, the use of a new molecular sequence named selfies, which has solved the... 

Predicting the interaction and binding affinity of drug-target represents a crucial yet complex phase in the time-consuming and costly process of drug discovery and development. Advances in deep learning have significantly enhanced the ability to model and extract intricate relationships and patterns from diverse biological and pharmaceutical data. However, existing methodologies encounter several fundamental challenges, including the modeling of protein and drug representations, understanding molecular interactions, and overcoming data access limitations. Addressing these challenges has necessitated the use of various heterogeneous architectures, methods, and data structures. Despite these... 

Protein, as the functional unit of the cell, plays a vital role in its biological function. With the advent of advanced sequencing techniques in recent years and the consequent exponential growth of the number of protein sequences extracted from diverse biological samples, their analysis, comparison, and classification have faced a considerable challenge. Existing methods for comparing proteins divide into two categories: methods based on alignment and alignment-free. Although alignment-based methods are among the most accurate methods, they face inherent limitations such as poor analysis of protein groups with low sequence similarity, time complexity, computational complexity, and memory... 

The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing customized network structures. However, a few works focus on more effective factors, such as input encoding method or implementation technology, to address accuracy and efficiency issues in this area. Therefore, in this work, we propose an image-based encoding method, called as WalkIm, whose adoption, even in a simple neural network, provides competitive accuracy and superior efficiency, compared to the existing classification methods (e.g. VGDC,... 

Alignment of genetic sequences is a fundamental part of genetic and bio-science. Alignment of DNA and protein sequences has an effective role in accelerating and simplifying problems in Bioinformatics like predicting protein interactions. Smith-Waterman algorithm is a precise algorithm for performing local alignment, suffering from computation complexity. There are some implementations on CPU, GPU, and FPGA platforms in order to reduce the run time of this algorithm. FPGA implementation is considered because of low power consumption and high degree of parallelism. With using pipeline and hardware redundancy techniques, various architectures have been proposed and implemented. In the best... 

Drug-target binding affinity prediction is one of the most important and vital part of drug discovery. The computational methods to predict binfing affinity is a standing challenge in drug discovery. State-of-the-art models are usually based on supervised machine learning with known label information. It is expensive and time-consuming to collect labeled data. This thesis proposes a semi-supervised model based on convolutional GAN (Generative adversarial networks). The model consists of two Gans and Two CNN blocks for feature extraction and fully connected layers for prediction. Gan can learn protein and drug features from unlabeled data. We evaluate the performance of our method using four... 

With the remarkable advancements in genome sequencing technology, we are witnessing a significant increase in the volume and diversity of metagenomic data. This growth has introduced new challenges in the analysis of metagenomic data, among which precise classification of these data into taxonomic groups is one of the most important. Assigning a label to a new metagenomic data at higher taxonomic levels has become a concern in metagenomic data classification. The main challenges in this area include classification accuracy, processing speed, and memory and resource consumption. These challenges have caused existing methods to fall short in fully meeting the increasing demands of this field.... 

For more than five decades, the task of accurately determining the three-dimensional arrangement of a protein based merely on its sequence of amino acids has remained a prominent and unsolved area of research. Three-dimensional structure prediction methods are essential due to the time-consuming nature, expensive equipment requirements, and high expenses associated with establishing the structure of each protein using traditional laboratory procedures. When trying to figure out the connection between sequences of known structures and sequences of unknown structures, deep learning algorithms are a faster and less expensive alternative to experiments. Although there have been recent... 

Global on chip communication becomes a critical power bottleneck in high performance many core architectures. The importance of power dissipation in networks-on-chip along with power reduction capability of on-chip nanophotonic interconnects has made optical network on chip a novel technology. Major advantages like high bandwidth, light speed latency and low power consumption, provide a promising solution for future of communications in many core architectures. However, the basic elements that are embedded in optical networks on chip are extremely temperature sensitive. This would lead to change in the physical characteristics of nanophotonic elements which may cause failure in network on... 

Exponential growth of traffic and bandwidth demands in current data center networks, requires low-latency high-throughput interconnection networks, considering power consumption. Furthermore, increasing multicast intensive applications, alongside conventional unicast applications, arises power efficient communication in today’s data center networks as the main design challenge. Addressing these demands, optical networks suggest several benefits as well as circumventing most disadvantages of electrical networks. In this thesis, we propose an all-optical scalable architecture, for communicating intra-data centers. This architecture utilizes passive optical devices and enables optical circuit...