MapReduce-Based Dynamic Partition Join with Shannon Entropy for Data Skewness

Join operations of data sets play a crucial role in obtaining the relations of massive data in real life. Joining two data sets with MapReduce requires a proper design of the Map and Reduce stages for different scenarios. The factors affecting MapReduce join efficiency include the density of the data sets and data transmission over clusters like Hadoop. This study aims to improve the efficiency of MapReduce join algorithms on Hadoop by leveraging Shannon entropy to measure the information changes of data sets being joined in different MapReduce stages. To reduce the uncertainty of data sets in joins through the network, a novel MapReduce join algorithm with dynamic partition strategies called dynamic partition join (DPJ) is proposed. Leveraging the changes of entropy in the partitions of data sets during the Map and Reduce stages revises the logical partitions by changing the original input of the reduce tasks in the MapReduce jobs. Experimental results indicate that the entropy-based measures can measure entropy changes of join operations. Moreover, the DPJ variant methods achieved lower entropy compared with the existing joins, thereby increasing the feasibility of MapReduce join operations for different scenarios on Hadoop.

Information Theory and Channel Coding

Chapter 9 presents the fundamentals of information theory and coding, which are required for understanding of the information measure, entropy and limits in signal transmission including the definition and derivative of the communication channel capacity. The coding theorem is separtelly presented. The chapter contains a part that defines the entropy of continuous and discrete Gaussian and uniform stochastic processes. The results of this unique analysis is essential to understand the notion of the continuous and discrete white Gaussian noise process. The block and convolutional codes, including hard decision Viterbi algorihthm are presented. The theory of iterative and turbo coding is presented in a form of a Project in the supplementary material, where several topics are defined and the related solutions are offered.

An Efficient Trajectory representative generation moving data prediction using different Clustering algorithm

This paper highlighted Moving and Trajectory Object Cluster (MOTRACLUS) algorithm and analysis the sub-trajectories and real-trajectories algorithm for moving data and suggest a new approach of moving elements. This paper evaluates the Hurricane data measure and mass less data measure entropy of trajectories objects of moving data of Chhattisgarh location. The paper covered Prediction Generation with their distance cluster minimum description length (MDL) algorithm and others corresponding distance cluster (CLSTR) algorithm. This paper highlighted the k-nearest algorithm with least cluster section (LCSS) model and dimensional Euclidean of MDL algorithm. Our algorithm consists of two parts that is partitioning and grouping phase. This paper develops and enhances a cluster of trajectory object and calculates the actual distance of moving object. This algorithm works on the CLSTR algorithm and calculates Trajectory movement of object. In this we evaluate the entropy of moving object by consideration the heuristic parameter.

An Improved Method for Combine (LSB and MSB) Based on Color Image RGB

Image steganography is the art of hiding data into an image by using the secret key. This paper presents two techniques that combine the most significant bit (MSB) as well as the least significant bit (LSB) based on a color image (24bit for RGB). The presented study proposes a novel method to combine (LSB and MSB) bits based on check MSB values and replace bits from LSB with a secret message. The result of this proposed method that made not affect quality stego -image based on the resulting histogram that shows a match between the cover image and stego- image and more secure because not hidden in all image. The factors were used Mean Square Error (MSE), Compute Payload, in addition to Peak Signal to Noise Ratio (PSNR). The PSNR’s rate is high and MSE is less. The result of this paper when applying on the different image gives high PSNR of 87.141 and less MSE of 0.00012 when inserting message 80 bits and reduction value PSNR of 72.023 and MSE of 0.0040 when inserting message 1200 bits and measure entropy is the same value for cover image and stego –image then this method is more security for the attacker.

QUANTUM INFORMATION, ENTANGLEMENT AND ENTROPY

In this review I had given an introduction to axiomatic Quantum Mechanics, Quantum Information and its measure entropy. Also, I had given an introduction to Entanglement and its measure as the minimum of relative quantum entropy of a density matrix of a Hilbert space with respect to all separable density matrices of the same Hilbert space. I also discussed geodesic in the space of density matrices, their orthogonality and Pythagorean theorem of density matrix.

Text documents clustering using modified multi-verse optimizer

In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.

Analysis of Polarimetric Mini-SAR and Mini-RF Datasets for Surface Characterization and Crater Delineation on Moon

The hybrid polarimetric architecture of Mini-SAR and Mini-RF onboard Indian Chandrayaan-1 and LRO missions were the first to acquire shadowed polar images of the Lunar surface. This study aimed to characterize the surface properties of Lunar polar and non-polar regions containing Haworth, Nobile, Gioja, an unnamed crater, Arago, and Moltke craters and delineate the crater boundaries using a newly emerged approach. The Terrain Mapping Camera (TMC) data of Chandrayaan-1 was found useful for the detection and extraction of precise boundaries of the craters using the ArcGIS Crater tool. The Stokes child parameters estimated from radar backscatter like the degree of polarization (m), the relative phase (δ), Poincare ellipticity (χ) along with the Circular Polarization Ratio (CPR), and decomposition techniques, were used to study the surface attributes of craters. The Eigenvectors and Eigenvalues used to measure entropy and mean alpha showed distinct types of scattering, thus its comparison with m-δ, m-χ gave a profound conclusion to the lunar surface. The dominance of surface scattering confirmed the roughness of rugged material. The results showed the CPR associated with the presence of water ice as well as a dihedral reflection inside the polar craters.

The Entropy and Reversibility of Cellular Automata on Cayley Tree

In this paper, we study linear cellular automata (CAs) on Cayley tree of order [Formula: see text] over the field [Formula: see text] (the set of prime numbers modulo [Formula: see text]). After revealing the rule matrix corresponding to cellular automata on Cayley tree with the null boundary condition, we analyze the reversibility problem of these cellular automata for some given values of [Formula: see text] and the levels [Formula: see text] of Cayley tree. The necessary and sufficient conditions for determining whether a CA is reversible or not are demonstrated. Furthermore, we compute the measure-theoretical entropy of the cellular automata which we define on Cayley tree. We show that for CAs on Cayley tree, the measure entropy with respect to uniform Bernoulli measure is infinity.

New Bio Inspired Techniques in the Filtering of Spam

The internet era promotes electronic commerce and facilitates access to many services. In today's digital society the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This paper deals on the unveiling of fresh bio-inspired techniques (artificial social cockroaches (ASC), artificial haemostasis system (AHS) and artificial heart lungs system (AHLS)) and their application for SPAM detection. For the authors' experimentation, they have used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy and error). They have optimising the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine learning algorithms (decision tree C4.5 and K-means).