The net profit of investors can rapidly increase if they correctly decide to take one of these three actions: buying, selling, or holding the stocks. The right action is related to massive stock market measurements. Therefore, defining the right action requires specific knowledge from investors. The economy scientists, following their research, have suggested several strategies and indicating factors that serve to find the best option for trading in a stock market. However, several investors’ capital decreased when they tried to trade the basis of the recommendation of these strategies. That means the stock market needs more satisfactory research, which can give more guarantee of success for investors. To address this challenge, we tried to apply one of the machine learning algorithms, which is called deep reinforcement learning (DRL) on the stock market. As a result, we developed an application that observes historical price movements and takes action on real-time prices. We tested our proposal algorithm with three—Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH)—crypto coins’ historical data. The experiment on Bitcoin via DRL application shows that the investor got 14.4% net profits within one month. Similarly, tests on Litecoin and Ethereum also finished with 74% and 41% profit, respectively.
Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques, and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: (i) the number of followers of the tweet poster, (ii) the number of comments on the tweet, (iii) the number of likes, and (iv) the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80% less time, 88.8% less CPU consumption, and 12.5% more accurate predictions compared with the classic approach. 16 17 processing and application in the field of predicting future 49 Bitcoin prices. Processing a large amount of Bitcoin-related 50 tweets normally consumes a high level of computer resources 51 (CPU, RAM, memory) and time [31], [32], [33], [34]. Most of 52 the previous works is focused on how to reduce the resource, 53 so maximizing the prediction result at the same time is not 54 considered. However, tweets written by an expert, public fig-55 ure, or celebrity will become viral, with many replies, likes, 56 and retweets. Tweets with few replies, likes, or retweets are 57 unlikely to become viral because they are likely to circulate 58 mainly among close friends. Consequently, viral tweets are 59 expected to have a greater influence on price changes than 60 other tweets. If we can separate tweets with the highest 61 impact on future price changes from less important tweets, 62 it gives the possibility to employ less computer resources 63 usage while still obtaining accurate forecasts. 64 Hence, different from the previous approaches, in this 65 study, we analyze how Bitcoin-related information on Twitter 66 affects the actual Bitcoin price by considering four main 67 attributes: (i) the number of followers of the poster, (ii) the 68 number of comments on a tweet, (iii) the number of likes, 69 and (iv) the number of retweets. For this, we gather all 70 Bitcoin-related tweets within a particular period and divide 71 them into four groups based on their attributes. Since we 72 use th...
Global Positioning Systems (GPS) are successfully used in many fields such as navigation, meteorology, military tasks, mapping, virtual fencing, and more. Smart collars are currently the most convenient device for determining animal location in virtual fencing systems, however; these systems are still suffering from environmental effects and propagation in direct visibility. These types of side effects may reduce the work of GPS receivers. The current article defines a method for improving animal location accuracy using a virtual fence smart collar worn around the animal’s neck by the aid of maximum probability of movement from one point to another. The proposed approach first checks the current position of the animal, and after receiving a GPS signal from satellites it calculates the distance between the two GPS signals. Secondly, the method checks the animal’s behavior for the receiving period of the two points. Finally, the approach calculates a probability of maximum animal movement for the two-point receiving period. If the animal can pass the distance in the time frame of the two signals, then the second signal is taken as the correct position; otherwise, the point is taken which the animal could pass. Real-time animal behavior is classified using Support Vector Machines (SVM). The proposed method was verified within seven days of experiments. Consequently, the proposed approach experiments were sufficiently successful. The recreated locations from our approach appeared very close to the real point. The mean average of passed distance by the marked line decreased to 16.2, 5, 0 m for running, walking, and resting conditions, respectively. On the other hand, the unfiltered geolocations of the GPS receiver, give results significantly further from the animal’s actual position such as 148.8, 182.7, 136.2 m for running, walking, and resting conditions.
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