Basis Of Execution Time Research Articles

MRBSChain a novel scalable medical records binance smart chain framework enabling a paradigm shift in medical records management

Medical records management had always been a challenging in healthcare sector. Traditionally, medical records are handled either manually or electronically that are under the stewardship of hospitals/healthcare institutions. A patient centric approach is the new paradigm where patient is an inherent part of the healthcare ecosystem controlling the access and sharing of his/her personal medical care information. Medical care information requires robust security and privacy. Also there are other issues like confidentiality, interoperability, scalability, cost efficiency and timeliness that need to be addressed. To achieve these objectives, this paper proposes a novel-scalable patient centric yet privacy preserving framework for efficient and secure electronic medical records management. In addition, proposed system generates a unified trusted record and authentication role mapping for enforcing secure access control for medical records using complex encryption algorithms. This paper identifies 13 key performance factors for performance comparison of proposed framework with traditional models. Ethereum and Binance Smart Chain acted as a benchmark platform for performance evaluation of MRBSChain on the basis of three metrics (transaction cost, average block time and deployment cost).At last, a comparative analysis of MRBSChain with other state of art blockchain systems on the basis of execution time is presented in the paper.

FogBus3: A scalable and reliable framework for integrated IoT and fog computing scenario

AbstractFog computing is an advantageous concept that works in consonance with cloud computing to enhance user experience. It presents innumerable benefits such as low latency, bandwidth saving, and location awareness. But, just being opportunistic towards the platform without checking it for security is not at all justifiable. As more and more sensitive data flows through the fog layer, it becomes mandatory to examine it for security. The fog nodes being closer to the user end are highly vulnerable to being malicious. There exists a possibility that a faulty fog node tries to tamper the data generated from the Internet of things (IoT) device layer and restrains the other functioning nodes to operate upon a common perceived value. In this regard, the authors have proposed the FogBus3 framework that guarantees reliable results from the fog level even in the presence of faulty fog nodes. The proposed framework adds a job categorization mechanism and a reliability ensuring IoT‐Fog Agreement Algorithm (IFAA) to the fog layer. The proposed framework has been thoroughly examined with the help of a working example and simulated through MATLAB R2021a. Further, the scenario has been tested before and after implementation of the proposed algorithm on the basis of execution time. Experimental results show that FogBus3's IFAA algorithm improves the execution time of the said setup by 41%. FogBus3 has also been evaluated for its scalability and is believed to ensure reliability in the said scenario.

Foreground algorithms for detection and extraction of an object in multimedia

Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called “background image” and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It’s quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required.

Hybridization of Chaos and Flower Pollination Algorithm over K-Means for data clustering

Classical clustering algorithms like K-means often converge to local optima and have slow convergence rates for larger datasets. To overcome such situations in clustering, swarm based algorithms have been proposed. Swarm based approaches attempt to achieve the optimal solution for such problems in reasonable time. Many swarm based algorithms such as Flower Pollination Algorithm (FPA), Cuckoo Search Algorithm (CSA), Black Hole Algorithm (BHA), Bat Algorithm (BA) Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Artificial Bee Colony (ABC) etc have been successfully applied to many non-linear optimization problems. In this paper, an algorithm is proposed which hybridizes Chaos Optimization and Flower Pollination over K-means to improve the efficiency of minimizing the cluster integrity. The proposed algorithm referred as Chaotic FPA (CFPA) is compared with FPA, CSA, BHA, BA, FFA, and PSO over K-Means for data clustering problem. Experiments are conducted on sixteen benchmark datasets. Algorithms are compared on four different performance parameters — cluster integrity, execution time, number of iterations to converge (NIC) and stability. Results obtained are analyzed statistically using Non-parametric Friedman test. If Friedman test rejects the Null hypothesis then pair wise comparison is done using Nemenyi test. Experimental Result demonstrates the following: (a) CFPA and BHA have better performance on the basis of cluster integrity as compared to other algorithms; (b) Prove the superiority of CFPA and CSA over others on the basis of execution time; (c) CFPA and FPA converges earlier than other algorithms to evaluate optimal cluster integrity; (d) CFPA and BHA produce more stable results than other algorithms.

Task Scheduling Algorithm Based on Bacterial Foraging Optimization (BFO) in Cloud Computing

Cloud computing is the architecture in which cloudlets are executed by the virtual machines. The most applicable virtual machines are selected on the basis of execution time and failure rate. Due to virtual machine overloading, the execution time and energy consumption is increased at steady rate. In this paper, BFO technique is applied in which weight of each virtual machine is calculated and the virtual machine which has the maximum weight is selected on which cloudlet will be migrated. The performance of proposed algorithm is tested by implementing it in CloudSim and analyzing it in terms of execution time, energy consumption.

Study of Selected Shifting based String Matching Algorithms

This paper presents detailed comparisons and analysis of shift-based exact string matching algorithms. The paper proposes comparison among these algorithms on the basis of execution time taken by the algorithms to completely match a given pattern on a given text. The algorithms have been analyzed on the following parameters: length of pattern, length of text, and number of characters in the text. This study will help in selecting the appropriate algorithm to be used in solving a particular real-life problem.

An Approach to Implement Map Reduce with NoSQL Databases

MapReduce is a well-known programming model and an implementation method for executing, processing and generating massive data sets. MapReduce algorithm consists of a map function that processes a key/value pair to produce a set of intermediate key/value pairs, and a reduce function which combine all these values related with the same intermediate key. MapReduce executes in parallel itself without implementing any parallel programming model and it is the most efficient way to process unstructured data. In this research MapReduce algorithm is implemented on a cluster based machine using Hadoop distributed file system (HDFS) in order to perform a Pattern matching algorithm for different volumes of datasets. The quantitative performance analysis of MapReduce algorithm is done for the different volumes of data on the basis of execution time and number of patterns searched. So far relational databases are used for storing the data for the applications but now there is need to store huge amount of data to store and manage which cannot stored by relational databases. NoSQL technology over comes this problem. This research paper provides a brief introduction to NoSQL database working and comparative study between MongodB and CouchDB, Which are mostly used for big data application. The operations are performed to explore the results as distinguish between both NoSql databases. This paper shows the performance of Mongodb and CouchDB. Results proves that CouchDB is more powerful than Mongodb to load and process on big data and processing very fast as compare to Mongodb. This paper describes the functionality of Mongodb and CouchDB over the large dataset.

Performance Evaluation of RC6, Blowfish, DES, IDEA, CAST-128 Block Ciphers

Rapid growth of internet applications fueled the need for securing information and computers. Encryption algorithms play vital role to secure information. This paper provides comparison of most common encryption algorithms namely: DES, Blowfish, CAST-128, RC6, IDEA. Performance evaluation is carried out on the basis of execution time and throughput. These algorithms has different key and block size. DES, IDEA, Blowfish, CAST-128 has block size of 64 bits. DES has key Size of 64 bits while IDEA, Blowfish, CAST128 has key size of 128 bits. RC6 has Key size and Block size of 128 bits. Simulation results are provided to demonstrate the effectiveness of each algorithm. General Terms Cryptography, Symmetric encryption, DES, Blowfish, CAST128, RC6, IDEA

Performance Analysis of RC5, Blowfish and DES Block Cipher Algorithms

In this paper, Performance analysis of RC5, Blowfish and DES block cipher algorithms have been done on the basis of execution time and resource utilization. CPU utilization and memory utilization both are considered for determining resource utilization. These three algorithms are parameterized algorithm and encrypt two w-bits at a time. Allowable choices for w are 16 bits, 32 bits, and 64 bits. Blowfish and DES have same structure for encryption and decryption while RC5 have different. RC5 has 12 and Blowfish & DES have 16 rounds. These three algorithms have a variable block size and a variable key size in their structure. Performances of RC5 & Blowfish algorithms have been evaluated on key size of 128-bits, 192-bit and 256-bit while key size is fixed 64-bit for DES in this paper. General Terms Cryptography, Block cipher, Symmetric encryption, RC5, Blowfish, DES

Performance Analysis of RC6, Twofish and Rijndael Block Cipher Algorithms

In this paper, Performance analysis of RC6, Twofish and Rijndael block cipher algorithms have been done on the basis of execution time and resource utilization. CPU utilization and memory utilization both are considered for determining resource utilization. These algorithms are parameterized algorithm and were designed to meet the requirements of the Advanced Encryption Standard (AES) competition and selected among five finalists of that competition. These three algorithms have a variable block size and a variable key size in their structure and encrypt four w-bits at a time. Allowable choices for w are 16 bits, 32 bits, and 64 bits. Twofish and Rijndael have same structure for encryption and decryption while RC6 have different. RC6, Twofish and Rijndael have 20, 16 and 10 rounds respectively. Performances of these three algorithms have been evaluated on key size of 128-bits, 192-bit and 256-bit in this paper. General Terms Cryptography, Block cipher, Symmetric encryption, RC6, Twofish, Rijndael

New parallel Hough transform for circles

The Hough transform is a well known medium-level image recognition technique for the detection of curves. The conventional Hough technique [1, 2] requires a three-dimensional accumulator array (AA) for the detection of circles. Because shape parameterisation and data structure of the accumulator array significantly affect the memory space and computation loading requirements of any hardware or software realisation, they should be carefully selected for any fast and efficient algorithm. A new scheme which uses only a pair of two-dimensional accumulator arrays to reduce the storage and computation time by an order of magnitude or more is proposed. This new scheme is capable of discriminating multiple (including concentric) circles in a complex real life image with a recognition rate of 95–100%. Various parallel realisations of this Hough scheme for circles on a general purpose MIMD (a reconfigurable transputer network) machine are discussed and a comparison of their performances with the conventional approach on the basis of execution time and recognition rate is presented.

Transputer based implementations of the hough transform for computer vision

Implementations of a popular pattern extraction technique for Computer Vision, the Hough Transform, are described for a network of Transputers. The various techniques are described and their performance compared on the basis of execution time.