Agriculture planning plays a significant role in economic growth and food security of agro-based country. Se- lection of crop(s) is an important issue for agriculture planning. It depends on various parameters such as production rate, market price and government policies. Many researchers studied prediction of yield rate of crop, prediction of weather, soil classification and crop classification for agriculture planning using statistics methods or machine learning techniques. If there is more than one option to plant a crop at a time using limited land resource, then selection of crop is a puzzle. This paper proposed a method named Crop Selection Method (CSM) to solve crop selection problem, and maximize net yield rate of crop over season and subsequently achieves maximum economic growth of the country. The proposed method may improve net yield rate of crops.

As a creative discipline, Requirements Engineering (RE), lends importance to understanding the associated human factors. Crowd RE, the approach of acquiring requirements from members of the public—the so-called crowd—emphasizes human factors further. We investigate how human personality and creative potential influence a requirement acquisition task. These factors are of specific importance to Crowd RE because (1) crowd workers are generally not trained in RE, and (2) a key motivation in engaging them is to benefit from their creativity. We propose a sequential Crowd RE process, where workers in one stage review requirements from the previous stage and produce additional requirements. To reduce potential information overload in this process, we propose strategies for selecting requirements from one stage to expose to workers in later stages. We conducted a study on Amazon Mechanical Turk tasking 300 workers with creating requirements via the above sequential process (in the domain of smart home applications for concreteness) and tasking an additional 300 workers to rate the creativity (novelty and usefulness) of those requirements. Our findings offer insights on how to carry out Crowd RE effectively. First, we find that a crowd worker's (1) creative potential, and personality traits of openness and conscientiousness have significant positive influence on the novelty of the worker's ideas, and (2) personality traits of agreeableness and conscientiousness have significant positive influence, but extraversion has significant negative influence on the usefulness of the worker's ideas. Second, we find that exposing a worker to ideas from previous workers cognitively stimulates the worker to produce creative ideas. Third, we identify effective strategies based on personality traits and creative potential for selecting a few requirements from a pool of previous requirements to stimulate a worker.

Defensive deception techniques have emerged as a promising proactive defense mechanism to mislead an attacker and thereby achieve attack failure. However, most game-theoretic defensive deception approaches have assumed that players maintain consistent views under uncertainty. They do not consider players’ possible, subjective beliefs formed due to asymmetric information given to them. In this work, we formulate a hypergame between an attacker and a defender where they can interpret the same game differently and accordingly choose their best strategy based on their respective beliefs. This gives a chance for defensive deception strategies to manipulate an attacker’s belief, which is the key to the attacker’s decision-making. We consider advanced persistent threat (APT) attacks, which perform multiple attacks in the stages of the cyber kill chain (CKC) where both the attacker and the defender aim to select optimal strategies based on their beliefs. Through extensive simulation experiments, we demonstrated how effectively the defender can leverage defensive deception techniques while dealing with multi-staged APT attacks in a hypergame in which the imperfect information is reflected based on perceived uncertainty, cost, and expected utilities of both the attacker and defender, the system lifetime (i.e., mean time to security failure), and improved false-positive rates of intrusion detection.