Design and Analysis of Algorithms

In order to deal with huge data set alternative design with proper function is desirable. These functions may perform operations sorting, searching, updating DBMS frequently. Apart from time and space metrics, energy, pattern and size of input, exact match or approximations are also key issues to be considered. So there is versatile need to seek improvement for performance. Among state of art approaches binary search relies on divide and conquer approach explore key item at mid element of array after each iteration and accordingly moves interval to new sub range. In this paper an analysis of state of art bisection algorithm has been presented with certain parameters as effectively with some dynamic alteration in input. Further, analysis has been done with parallel processing. I. INTRODUCTION Binary search is a searching algorithm that gives result with order of complexity O(log n) in an average case and in worst case too [1][2].The best case complexity order is equivalent to O(1). It follows Divide and Conquer approach. The necessary and sufficient condition for this searching technique it the working array must be sorted [3]. The concept is to divide the array into two sub arrays. Initially do it with the help of the highest value (last element if ascending sorted order has been followed) and with least value i.e. first value of the array as per discussed sorting mechanism [3] Under binary search the target element is searched with comparing the middle element of the sorted array[4]. If it matches with the element which we are searching for, the index value of that matched element will be given out else it is noticed that whether this middle element is larger than the element which we are searching or smaller. If the middle element is larger than our value then the searching will be performed again in the left sub array of the main array, else we approach towards right sub array. These iterations will be continued until we get the key or either we met with the last or first element of the array [5]. This concludes that if we are searching any key element in a given array so the number of comparison will be either less than or equal to the half of the number of elements in array. Thus the comparisons taken will be less with respect to the linear search [6][7]. The thing which cumulatively affects the time taken to the complete execution is the processing speed and number of processing elements executing simultaneously [7]. The invention of single instructions multiple data approach beats vector and array processors on certain features specially dealing capacity of multiple instructions at a time. The need of time reduction is truly mandatory in contrast to modern data and performance scenario. Since the need of storage capacity is growing exponentially thus there is always a demand of performance improvement in time, space, order, iterations and recursion etc. In sequential processing the number of elements those can work together will be fixed and only one while it may be two or more than two are available for parallel processing. Parallel execution of any program can be achieved by assigning the work to more than one processor and/or by multithread based execution of program. Here in binary search there is a necessary condition it tells all the elements must be in a sorted array so the number of comparison will be equals to the number of elements available in the array at worst case. Here in this paper we are comparing the performance of binary search for threads one, four and eight threads based iterative and recursive version of searching. Remaining sections of papers are as follows, Section II discusses the related work done towards binary search and parallel processing. Section III contains the hypothesis were taken in part for implementation and results verifications. Section IV tells about the flow of execution and methodology followed, here in this section hybridization of binary searching and parallel threads processing can be seen. Section V contains results and discussion. Where various tables and charts according to the implementation were attached those help to derive conclusion. Sections VI concludes the study and analysis done in earlier sections and gives a direction of future work associated with this study.

Purpose –
The purpose of this paper is to propose that in order to tackle the question of bias in algorithms, a systemic, sociotechnical and 
holistic perspective is needed. With reference to the term  “algorithmic culture,” the inter-connectedness and mutual shaping of society and technology are postulated. A sociotechnical approach requires translational work between and across disciplines. This conceptual paper undertakes such translational work. It exemplifies how gender and diversity studies, by bringing in expertise on addressing bias and structural inequalities, provide a crucial source for analyzing and mitigating bias in algorithmic systems.

Design/methodology/approach –
After introducing the sociotechnical context, an overview is provided regarding the contemporary discourse around bias in algorithms, debates around algorithmic culture, knowledge production and bias identification as well as common solutions. The key concepts of gender studies (situated knowledges and strong objectivity) and concrete examples of gender bias then serve as a backdrop for revisiting contemporary debates. 

Findings –
The key concepts reframe the discourse on bias and concepts such as algorithmic fairness and transparency by contextualizing and situating them. The paper includes specific suggestions for researchers and practitioners on how to account for social inequalities in the design of algorithmic systems. 

Originality/value – 
A systemic, gender-informed approach for addressing the issue is provided, and a concrete, applicable methodology toward a situated understanding of algorithmic bias is laid out, providing an important contribution for an urgent multidisciplinary dialogue.

Let G (V, E) be a simple graph and k be a ®xed positive integer. A vertex w is said to be a kneighbourhood-cover of an edge (u, v) if d(u, w) k and d(v, w) k. A set C V is called a kneighbourhood-covering set if every edge in E is k-neighbourhood-covered by some vertices of C. This problem is NP-complete for general graphs even it remains NP-complete for chordal graphs. Using dynamic programming technique, an O(n) time algorithm is designed to solve minimum 2-neighbourhood-covering problem on interval graphs. A data structure called interval tree is used to solve this problem.

Today's helicopters are the result of collaborative work in mechanical engineering and aeronautics. A helicopter main rotor or rotor system is the combination of a rotary wing and a control system that generates the aerodynamic lift force that supports the weight of the helicopter, and the thrust that counteracts aerodynamic drag in forward flight. In the field of rotorcraft, the research in this project is currently focusing on active blade systems to adapt the aerodynamic properties of the blade to the local aerodynamic conditions. Fuel-efficiency, reduction of vibration and noise and increase of the helicopter maximum speed are the benefits expected from these new technologies. A helicopter's rotor is generally made of two or more rotor blades. Rotor blades are made out of various materials, including aluminum, composite structure, and steel or titanium, with abrasion shields along the leading edge. The blade pitch is typically controlled by a swash plate connected to the helicopter flight controls. An Active Twist Rotor (ATR) is developed for future implementation of the individual blade control for vibration and noise reduction in helicopters. The rotor blade is integrally twisted by direct strain actuation using active fiber composites (AFC). In this thesis, the model of rotor blade is designed and analyzed. 3D models are done in CATIA. Analysis is done in Ansys. The materials used for original model are steel and Aluminum alloy, The modified model is analyzed by specifying aluminum alloy using solid element and also the shell element. The optimization results have been obtained for design solutions, connected with the application of active materials.

Decision tree algorithms are among the most popular techniques for dealing with classification problems in different areas. Decision tree learning offers tools to discover relationships, patterns and knowledge from data in databases. As the volume of data in databases is growing up very quickly, the process of building decision trees on such databases becomes a quite complex task.  The problem with decision trees is to find the right splitting criterion in order to be more efficient and to get the highest accuracy. Different approaches for this problem have been proposed by researchers, using heuristic search algorithms. Heuristic search algorithms can help to find optimal solutions where the search space is simply too large to be explored comprehensively. This paper is an attempt to summarize the proposed approaches for decision tree learning with emphasis on optimization of constructed trees by using heuristic search algorithms. We will focus our study on four of the most popular heuristic search algorithms, such as hill climbing, simulated annealing, tabu search and genetic algorithms.