mohammad mosleh

One of the basic challenges in high-density integrated circuits is loss of power consumption, which is caused by presence of transistors in circuits and causes the temperature of the circuit to increase. The design of digital circuits in a reversible way can be used as one of efficient approaches to solve this challenge. In addition, the design of parity-preserving reversible circuits can be very effective in detecting faults in circuits. Dividers are used as one of the most widely used circuits in digital computing systems. Divider circuits include an adder, a multiplexer and two sequential register and parallel-in to parallel-out left shift register circuits. This paper is presented a new and efficient design of a parity-preserving reversible non-restoring divider. For this purpose, first, a parity-preserving reversible D-latch is proposed. second, a parity-preserving reversible n-bit register is presented using the proposed reversible D-latch. Third, a parity-preserving reversible (n+1) bit shift register using the proposed reversible D-latch and other reversible gates is proposed. Finally, a parity-preserving reversible n bit divider is developed based on the non-restoring algorithm. The results of comparisons show that the proposed circuit is superior in terms of evaluation criteria of reversible circuits such as quantum cost, number of constant inputs and number of garbage outputs compared to previous works.

Due to lower cost and greater flexibility, voice over internet protocol (VoIP) is widely used in telecommunications. A variety of VoIP terminals causes them to be vulnerable. A common way to secure VoIP includes intrusion detection based on machine learning. Due to the diversity of traffics and lack of class labels for training Intrusion detection systems (IDSs) in many situations, clustering approaches (unsupervised learning) have been focused on. But individual cluster systems can't cover the diversities of feature values well, and some traffic samples may be identified as outliers. As an ensemble approach, the proposed model for solving these problems focuses on using TwoStep clustering algorithm, and by improving it, tries to improve the clustering-based intrusion detection. Moreover, regarding the importance of the feature selection process, a combination of Simulated Annealing algorithm (SA) and Multi-Layer Perceptron (MLP) has been exploited for identifying superior features used for clustering VoIP packets, as Normal or involving DoS, R2L, U2R either Probe attacks. Based on evaluation results obtained on the dataset “Network Security Lab-Knwledge Discovery in Databases” (NSL-KDD) by MATLAB, the proposed feature selection reduced the training and testing times, averagely by 77% and 80%, respectively, by reducing the features to 10 and 8. Also, compared to previous works, the proposed IDS shows average improvements in Accuracy, Detection rate, and F-Measure at 3.34 %, 14.17 %, and 32.87 %, respectively.

With the advent of quantum computers, the need to protect quantum data as a fundamental issue has attracted the attention of researchers. In this article,, a quantum audio watermarking approach based on a combination of echo hiding and least significant bit (LSB) methods is proposed. In the embedding stage, the proposed approach first converts the host audio signal into QRDS quantum display form and then generates a quantum echo signal from it. Next, ,it inserts the watermark data qubits into the resulting quantum echo signal using the least significant bit method. Finally, the watermarked quantum signal is obtained from the result of the original quantum signal and the echo quantum signal. In the extraction phase, of the proposed approach, based on the difference between the samples of the original quantum signal and the received quantum signal, the watermark qubits are recovered. In addition, quantum reversible circuits for insertion and extraction processes have been designed and implemented. The results obtained from the evaluation and comparison of the proposed approach, in terms of transparency, robustness to attacks and also the embedding capacity, show that the proposed scheme compared to the quantum watermarking methods based on the LSB method, has a higher resistance . In addition, the proposed method has a very good transparency (SNR=66.26dB) at the embedding capacity of 512 kbps, which shows that the proposed method has been able to improve the compromise between robustness, transparency and capacity.

Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can solve the problem of the cold start of the system and improve the quality of the search. In this research, a new method is presented in order to improve recommender systems in the field of the web, which uses the DBSCAN clustering algorithm to cluster data, and this algorithm obtained an efficiency score of 99%. Then, using the Page rank algorithm, the user's favorite pages are weighted. Then, using the SVM method, we categorize the data and give the user a combined recommender system to generate predictions, and finally, this recommender system will provide the user with a list of pages that may be of interest to the user. The evaluation of the results of the research indicated that the use of this proposed method can achieve a score of 95% in the recall section and a score of 99% in the accuracy section, which proves that this recommender system can reach more than 90%. It detects the user's intended pages correctly and solves the weaknesses of other previous systems to a large extent.

Differential diagnosis of chronic lymphoproliferative disorders (CLDs) has remained challenging due to the highly variable morphology features and immunophenotyping. Currently, the development of multiple-marker panel analyses by flow cytometry has opened a broad way for diagnosis of CLDs.

We analyzed the peripheral blood and bone marrow samples of 131 patients with B-cell CLDs (including 91 chronic lymphocytic leukemia (CLL), 15 atypical CLL, 14 mantle cell lymphoma (MCL), and 11 CD5-/CD10-lymphoma patients) from April 2018 to April 2019, using a panel of specific markers by flow cytometry.

Our results indicated that the expression pattern of CD22, CD23, FMC-7, and CD5 allowed us to accurately and differentially diagnose the B-CLL, MCL, and CD5-/CD10- lymphoma, while it was not capable of differentiating MCL from atypical CLL. We, however, found that the expression patterns of CD38 and immunoglobulin light chain differed significantly between atypical B-CLL and MCL. CD38 and lambda light chain were remarkably expressed in MCL patients (92.8% and 85%, respectively) compared to the atypical CLL (1.1% and 0% respectively), with the p value less than 0.001 for both markers. In contrast to MCL patients, all the patients with atypical CLL, expressed kappa light chain. The immunohistochemistry method used for cyclin D1 confirmed that the flow cytometry detection of kappa and lambda light chains could provide a new approach with high sensitivity (91%) and moderate specificity (50%) to distinguish MCL patients from atypical B-CLL.

Expression of CD5, CD20 (bright), CD22, FMC-7, CD38, and lambda light chain with no expression of CD23 can accurately detect MCL and differentiate it from atypical B-CLL.

Digital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for researchers and scholars. In this paper, we presented a new high-capacity, clear, and robust audio signaling scheme based on the DWT conversion synergy and golden ratio advantages using the TLBO algorithm. We used the TLBO algorithm to determine the effective frame length and embedded range, and the golden ratio to determine the appropriate embedded locations for each frame. First, the main audio signal was broken down into several sub-bands using a DWT in a specific frequency range. Since the human auditory system is not sensitive to changes in high-frequency bands, to increase the clarity and capacity of these sub-bands to embed bits we used the watermark signal. Moreover, to increase the resistance to common attacks, we framed the high-frequency bandwidth and then used the average of the frames as a key value. Our main idea was to embed an 8-bit signal simultaneously in the host signal. Experimental results showed that the proposed method is free from significant noticeable distortion (SNR about 29.68dB) and increases the resistance to common signal processing attacks such as high pass filter, echo, resampling, MPEG (MP3), etc.

Helicobacter pylori is a gram-negative spirochete that usually affects the gastric mucosa and has a prevalence of more than 50% of people worldwide. It has been revealed that Helicobacter pylori infection is not only associated with chronic gastritis and peptic ulcers, but also with some hematologic disorders such as immune thrombocytopenia, iron deficiency anemia and gastric mucosa-associated lymphoid tissue lymphoma. Immune thrombocytopenia is an acquired autoimmune disorder with low platelet count due to devastation of autoantibody-coated platelets.

In the present study, it was reported an Iranian patient with severe immune thrombocytopenia (platelet count 1×109/L) and Helicobacter pylori infection who achieved a long lasting platelet recovery after Helicobacter pylori eradication therapy. The patient was followed up for two years and no failure in platelet response was observed.

Accordingly, Helicobacter pylori treatment is not only useful in immune thrombocytopenia patients with mild and moderate thrombocytopenia but also may be beneficial in severe cases of immune thrombocytopenia.

The emergence of single-cell RNA-sequencing (scRNA-seq) technology has provided new information about the structure of cells, and provided data with very high resolution of the expression of different genes for each cell at a single time. One of the main uses of scRNA-seq is data clustering based on expressed genes, which sometimes leads to the detection of rare cell populations. However, the results of the proposed methods mainly depend on the shape of the cell populations and the dimensions of the data. Therefore, it is very important to develop a method that can identify cell populations regardless of these obstacles.

In the proposed method, which was a library method, at first, the number of clusters (cell populations) was estimated. Estimating the number of clusters is important because in the real world, basic information such as the number and type of cell populations is not available. Thereafter, using a graph-based Gaussian kernel, while reducing the dimensions of the problem, the cell populations were identified by means of the kmeans++ clustering.

The results of the implementation showed that the proposed method can achieve an acceptable improvement compared to other machine learning methods presented in this regard. For example, for the ARI criterion, values of 100, 93.47 and 84.69 were obtained for Kolod, Buettner, and Usoskin single-cell data sets, respectively.

The proposed method can cluster and thus identify cell populations with high accuracy and quality without having any basic information about the number and type of cell populations, regardless of the high dimensions of the problem.

Hematogones are normal B-cell precursor which can be seen in different physiological and pathological conditions. Due to variation in B-cell acute lymphoblastic leukemia (B-ALL) blasts immunophenotyping and interference of hematogones in minimal residual disease (MRD) assessment, precise discrimination of hematogones is very crucial.  The purpose of this study was to evaluate the expression pattern of surface markers in hematogones and compare them with lymphoblasts.

In this applied study, flow cytometric analysis was performed using Coulter FC-500 and MXP software in 4-color combination and 6 different tubes. In this study, 85 patients diagnosed with acute lymphoblastic leukemia were evaluated. Out of these patients, 45 were boys and 40 were girls. Patients aged from 1 to 15 years old. In addition, 27 bone marrow samples from other patients aged 4 to 18 years were included in this investigation. These samples had been obtained for other diagnostic purposes, such as immune thrombocytopenic purpura and juvenile idiopathic arthritis.

During flow cytometric analysis, hematogones showed expressions of CD19, CD20, CD22, CD10, CD45, CD81, CD123, CD9, CD34 (partial expression), and tdt (partial expression). In these patients, hematgones were negative for CD66c expression. Lymphoblastic cells were positive for CD19, CD20 (in some cases), CD22, CD10, CD45, CD81, CD123, CD58, CD9, CD66c, CD34 (in most cases), and TDT. CD81 mean fluorescence intensity (MFI) in hematogones was higher than that in lymphoblasts. (112.5 (30-251) vs. 17.5 (5-30); P<0.0001)

According to findings of this study, it seems that the use of CD81, CD58, CD123, CD66c, CD9, and CD81 MFI in combination with B-Cells associated markers can be very effective in differentiating hematogone from lymphoblast.

With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However, only a few methods are utilized for huge text classification problems. In this paper, we propose a new wrapper method based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM). We combine it with Learning Automata in order to make it more efficient. This helps to select better features using the reward and penalty system of automata. To evaluate the efficiency of the proposed method, we compare it with a method which selects features based on Genetic Algorithm over the Reuters-21578 dataset. The simulation results show that our proposed algorithm works more efficiently.