Every second, millions of data are being generated due to the use of emerging technologies. It is very challenging to store and handle such a large amount of data. Data deduplication is a solution for this problem. It is a new technique that eliminates duplicate data and stores only a single copy of data, reducing storage utilization and the cost of maintaining redundant data. Content-defined chunking (CDC) has been playing an important role in data deduplication systems due to its ability to detect high redundancy. In this paper, we focused on deduplication system optimization by tuning relevant factors in CDC to identify chunk cut-points and introduce an efficient fingerprint using a new hash function. We proposed a novel bytes frequency-based chunking (BFBC) algorithm and a new low-cost hashing function. To evaluate the efficiency of the proposed system, extensive experiments were done using two different datasets. In all experiments, the proposed system persistently outperformed the common CDC algorithms, achieving a better storage gain ratio and enhancing both chunking and hashing throughput. Practically, our experiments show that BFBC is 10 times faster than basic sliding window (BSW) and approximately three times faster than two thresholds two divisors (TTTD). The proposed triple hash function algorithm is five times faster than SHA1 and MD5 and achieves a better deduplication elimination ratio (DER) than other CDC algorithms. The symmetry of our work is based on the balance between the proposed system performance parameters and its reflection on the system efficiency compared to other deduplication systems.

Due to the quick increase in digital data, especially in mobile usage and social media, data deduplication has become a vital and cost-effective approach for removing redundant data segments, reducing the pressure imposed by enormous volumes of data that must be kept. As part of the data deduplication process, fingerprints are employed to represent and identify identical data blocks. However, when the amount of data increases, the number of fingerprints grows as well, and due to the restricted memory size, the speed of data deduplication suffers dramatically. Various deduplication solutions show a bottleneck in the form of matching lookups and chunk fingerprint calculations, for which we pay in the form of storage and processors needed for storing hashes. Utilizing a fast hash algorithm to improve the fingerprint lookup performance is an appealing challenge. Thus, this study is focused on enhancing the deduplication system by suggesting a novel and effective mathematical bounded linear hashing algorithm that decreases the hashing time by more than two times compared to MD5 and SHA-1 and reduces the size of the hash index table by 50%. Due to the enormous number of chunk hash values, looking up and comparing hash values takes longer for large datasets; this work offers a hierarchal fingerprint lookup strategy to minimize the hash judgement comparison time by up to 78%. Our suggested system reduces the high latency imposed by deduplication procedures, primarily the hashing and matching phases. The symmetry of our work is based on the balance between the proposed hashing algorithm performance and its reflection on the system efficiency, as well as evaluating the approximate symmetries of the hashing and lookup phases compared to the other deduplication systems.

The shape of red blood cells (RBCs) contributes to clinical diagnoses of blood diseases. The field of medical imaging has become more important because of the increasing need for automated and efficient diagnoses within a short period of time. Imaging technique plays an important role in RBC research for hematology. Classification is an important component of the retrieval system which allows one to distinguish between normal RBCs and abnormal RBCs which indicate anemia. In this paper, image processing techniques that use the optimization segmentation and mean filter play an important role in obtaining the geometric, texture and color features related to RBC images by using a photo imaging microscope. The support vector machine, which is an advanced kernel-based technique, is used to classify RBC data as either normal or abnormal, the proposed classifier algorithm achieved very good accuracy rates with validation measure of sensitivity, specificity and Kappa to be 100%, 0.998% and 0.9944 respectively.

The research presented in this paper was aimed to develop a classification system of breast tumors tissues using histopathological images. The paper focuses on using the advantages of fractal geometry texture analysis. The developed approach consists of two main steps: (i) the extraction of the fractal dimension spectra for the regions of interest, and (ii) using a classifier that automatically identifies the kind of the tested breast tumor tissue. The performance of the classifier was enhanced by using k-means clustering algorithm; this algorithm was utilized to define sets of centroids, such that each set various states of a specific kind of breast tumors tissue. The classification powers of several fractal dimension spectra descriptors have been investigated. The proposed classification methods were applied on 24 different histopathological breast tumors images. The reached higher recognition accuracy was (98.8%).