The route recommendation system helps the driver find the best route between origin and destination. A recommendation system often suggests its decision without considering some criteria. This paper proposes a multicriteria decision-making method, namely Fuzzy—Analytic Hierarchy Process—Express (F-AHP-Express) for recommending a personal travel route from several alternative routes. It is calculated based on the driving preferences of a driver and road conditions for each road segment. We compare the F-AHP-Express to others; Fuzzy—Analytic Hierarchy Process (F-AHP) and Fuzzy—Analytic Hierarchy Process—Technique for Others Preference by Similarity to Ideal Solution (F-AHP-TOPSIS), for its recommendation results, time processing, agility, and complexity. Our experiments show that F-AHP-Express could deliver similar recommendation results compared to other methods, and it is additionally the fastest method. F-AHP-Express is 45% and 23% faster than F-AHP and F-AHP-TOPSIS, respectively. F-AHP-Express not only has the fastest time processing among the others but also has the least judgments in agility testing. It needs 37.5% and 16.67% fewer judgments from F-AHP and F-AHP-TOPSIS, respectively. Moreover, AHP-Express has a complexity of O(n), meanwhile, the others have O(n²) for their complexity. Thus, the results show that F-AHP-Express is the best method for recommending a personal route.

The traffic composition in developing countries comprises of variety of vehicles which include cars, buses, trucks, and motorcycles. Motorcycles dominate the road with 77.5% compared to other types. Meanwhile, route recommendation such as navigation and Advanced Driver Assistance Systems (ADAS) is limited to particular vehicles only. In this research, we propose a framework for a contextual route recommendation system that is compatible with traffic conditions and vehicle type, along with other relevant attributes (traffic prediction, weather, temperature, humidity, heterogeneity, current speed, and road length). The framework consists of two phases. First, it predicts the traffic conditions by using Knowledge-Growing Bayes Classifier on which the dataset is obtained from crawling the public CCTV feeds and TomTom digital map application for each observed road. The performances of the traffic prediction are around 60.78–73.69%, 63.64–77.39%, and 60.78–73.69%, for accuracy, precision, and recall respectively. Second, to accommodate the route recommendation, we simulate and utilize a new measure, called road capacity value, along with the Dijkstra algorithm. By adopting the compatibility, the simulation results could show alternative paths with the lowest RCV (road capacity value).

Home is a place for human beings to live and also to socialize with the society. In this rapid technology development, a home security system is a must to prevent crimes. One of the innovation is home automation which enables controlling the doors automatically. RGB-D camera can detect human body movements which later can be used to recognize the activity through the Support Vector Machine (SVM) algorithm. The result of activity prediction can be used as home automation input for the home security system. In this paper, a security system analysis has been carried out which is beneficial for the users in maintaining the security system through combining the RGB-D Camera and Skeleton Tracking which then classified by using SVM algorithm. The result shows that the optimal data resulted through 1 meter distance with 100% accuracy, while testing variable C of SVM resulting in C = 2 as the best score for C variable with 92% accuracy.

Congestion often hinders human mobility. This problem occurs due to the constant increase in vehicles every year. Reliable predictions of traffic conditions would allow drivers to choose their routes to avoid traffic jams while providing the police with traffic management strategies. Therefore, this paper tests the ability of various machine learning methods to predict traffic conditions. The study assesses Neural Networks, Bayes Classifier, Decision Trees, SVM, Deep Neural Network, and Deep Learning. Of these methods, the Decision Tree, Deep Neural Network, and Bayes Classifier show the highest performance in predicting traffic conditions using static data testing. However, in dynamic testing to assess the growth of knowledge, the performance of the Knowledge-Growing Decision Tree tends to decrease as the training data grows. Its performance decreased 3.89 points (88.24% to 84.35%) in accuracy, and 7.55 points (76.25% to 68.70%) for each precision, recall, and F1 Score. Conversely, the Knowledge-Growing Deep Neural Network and Bayes Classifier had a better performance than Decision Tree. The performances of Knowledge-Growing Deep Neural Network increased slightly by 0.35 points (93.38% to 93.73%) for accuracy and 0.69 points (86.77% to 87.64%) in other measurements. Although its performance increased, the processing time takes very long, namely 139452.76 seconds and 318832.80 seconds for sub-scheme (a) and (b), respectively. Meanwhile, the Knowledge-Growing Bayes Classifier offers a greater performance increase of 2.3 points (80.52% to 82.82%) for the accuracy and 4.6 points (65.63% to 61.03%) for the other performance measurements. In addition, it also scored better for processing time, as predictions only take 3 seconds using sub-scheme (a), and 7 seconds when using sub-scheme (b). Therefore, the paper proposes the Knowledge-Growing Bayes Classifier to predict rapidly changing traffic conditions. This method outperform the others. These can be attributed to its ability to 1) adjust to ever-changing the traffic conditions; 2) predict the result as soon as the data are acquired; and 3) make decentralized predictions.

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