<span>The proper mapping in case of allocation of available tasks among particles is a challenging job to accomplish. It requires proper procedural approach and effectual algorithm or strategy. The deterministic polynomial time for task allocation problem is relative. The existence of proper and exact approach for allocation problem is void. However, for the survival of the grid and executing the assigned tasks, the reserved tasks need to be allocated equally among the particles of the grid space. At the same time, the applied model for task allocation must not consume unnecessary time and memory. We applied Particle Swarm Optimization (PSO) for allocating the task. Additionally, the particles will be divided into three clusters based on their energy level. Each cluster will have its own cluster header. Cluster headers will be used to search the task into space. In a single cluster, particles member will be of same energy level status such as full energy, half energy, and no energy level. As a result, the system will use the limited time for searching task for the remaining tasks in it if a particular task requires allocating half task to a particle.</span>
Abstract. T-way combinatorial testing aims to generate a smaller test suite size. The purpose of t-way combinatorial testing is to overcome exhaustive testing. Although many existing strategies have been developed for t-way combinatorial testing, study in this area is encouraging as it falls under NP-hard optimization problem. This paper focuses on the analysis of existing algorithms or tools for the past seven years. Taxonomy of combinatorial testing is proposed to ease the analysis. 20 algorithms or tools were analysed based on strategy approach, search technique, supported interaction and year published. 2015 was the most active year in which researchers developed t-way algorithms or tools. OTAT strategy and metaheuristic search technique are the most encouraging research areas for t-way combinatorial testing. There is a slight difference in the type of interaction support. However, uniform strength is the most utilized form of interaction from 2010 to the first quarter of 2017.
Drink and drive issue have become solemnly that needs immediate attention. This is due to drivers’ ignorance towards road rules and regulations and their selfish attitude that caused loss of innocent lives. Although previously there is a drunk detecting mechanism using breathalyzer but it isn’t suitable for current fast-paced lifestyle. Therefore, to overcome these issues, this system is proposed. This system is fixed on vehicle’s steering to measure alcohol concentration reading using MQ-3 sensor from the driver’s exhaled breath. If the driver found to be drunk beyond the threshold level of 400 ppm, then ignition lock is activated and the car engine does not start till alcohol concentration falls to a safe level. Or, if the driver consumes an alcoholic drink while driving, upon exceeding permissible limit, the car slows down till it stops. Then, the location of the vehicle is tracked and sent as Google Map integrated link via text message to authorized unit. Simultaneously, the car buzzer goes off while the car slows down so that surrounding road users are aware of the driver’s condition and drives at a distance. The proposed detection system is highly potential to be implemented for reducing the drunk and drive accidents.
Detection text from handwriting in historical documents provides high-level features for the challenging problem of handwriting recognition. Such handwriting often contains noise, faint or incomplete strokes, strokes with gaps, and competing lines when embedded in a table or form, making it unsuitable for local line following algorithms or associated binarization schemes. In this paper, a proposed method based on the optimum threshold value and namely as the Optimum Mean method was presented. Besides, Wolf method unsuccessful in order to detect the thin text in the non-uniform input image. However, the proposed method was suggested to overcome the Wolf method problem by suggesting a maximum threshold value using optimum mean. Based on the calculation, the proposed method obtained a higher F-measure (74.53), PSNR (14.77) and lowest NRM (0.11) compared to the Wolf method. In conclusion, the proposed method successful and effective to solve the wolf problem by producing a high-quality output image.
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