With the acquisition of remote sensing data, the improvement of image resolution, and the further development of remote sensing application and computer technology, remote sensing has become an important means of land...
详细信息
With the acquisition of remote sensing data, the improvement of image resolution, and the further development of remote sensing application and computer technology, remote sensing has become an important means of land avalanche investigation. Using GIS and spatial database technology, this paper designs and develops the land use dynamic remote sensing monitoring database system. At the same time, the author studies the key technology of the system, and constructs a massive spatial database of land geography. The construction of the platform is mainly divided into two aspects, one is the audit and distribution system on the web side and the other is the mobile side to investigate APP. Web-side system mainly realizes image viewing and verification of investigation results. Through this platform, image data can be efficiently distributed to users in different areas through the network, providing users with the functions of spatial data browsing, query and analysis, and fully sharing data. The effect of datamining and remote sensing technology in land resource management is obvious, which promotes the development of land resource management in the direction of intellectualization and refinement. The data of the research results in this paper will be the basis of land management. By mastering the precise utilization of land resources, we can further improve the precision level of land management and promote the sustainable development of economy and society.
Optimizing energy consumption in modern mobile handled devices plays a crucial role as lowering the power consumption impacts battery life and system reliability. Recent mobile platforms have an increasing number of s...
详细信息
Optimizing energy consumption in modern mobile handled devices plays a crucial role as lowering the power consumption impacts battery life and system reliability. Recent mobile platforms have an increasing number of sensors and processing components. Added to the popularity of power-hungry applications, battery life in mobile devices is an important issue. However, the utilization pattern of large amount of data from the various sensors can be beneficial to detect the changing device context, the user needs and the running application requirements in terms of resources. When these information are used properly, an efficient control of power knobs can be implemented to reduce the energy consumption. This paper presents a framework for ENergy Optimization for MObile platform using User needS (ENOsMOUS). This framework is able to identify user contexts and to understand user habits, preferences and needs to improve the operating system power scheme. Machine Learning (ML) algorithms have been used to obtain an efficient trade-off between power consumption reduction opportunities and user satisfaction requirements. ENOrMOUS is a generic solution that manages the power knobs. When applied to the CPU frequency, the sound level, the screen brightness and the Wi-Fi, ENOrMOUS can lower the power consumption by up to 35% compared the out-of-the-box operating system power manager schemes with a negligible overhead.
Educational Big data(EBD)is coming due to the emerging of large amount of learning data in the education field,and promotes a research hotspot called Educational datamining(EDM).This paper focuses on current research...
详细信息
Educational Big data(EBD)is coming due to the emerging of large amount of learning data in the education field,and promotes a research hotspot called Educational datamining(EDM).This paper focuses on current researches from the datamining perspective,summarizes the traditional applications scenes,classifies the data mining algorithms into descriptive and predictive ones,and designs a framework for educational big *** proposed approach gives a clear vision and points out challenges and directions for further research in EBD.
This paper describes an awareness campaign on the correct use of social networks. Based on the results of some surveys, it describes the reality of the situation and proposes methods and strategies to modify the use o...
详细信息
ISBN:
(纸本)9783319947822;9783319947815
This paper describes an awareness campaign on the correct use of social networks. Based on the results of some surveys, it describes the reality of the situation and proposes methods and strategies to modify the use of social networks in adolescents. Extraction, transformation and cleaning of the data obtained from the surveys was carried out to generate a data repository. Once the data bank was obtained, an analysis was carried out, by applying data mining algorithms, such as those of association, classification and clustering, in order to obtain patterns of behavior. We compare these results to data received from subjects prior to the training in order to determine the efficacy of the training.
Purpose Adverse events (AEs) caused by polypharmacy have recently become a clinical problem, and it is important to monitor the safety profile of drug-drug interactions (DDIs). mining signals using the spontaneous rep...
详细信息
Purpose Adverse events (AEs) caused by polypharmacy have recently become a clinical problem, and it is important to monitor the safety profile of drug-drug interactions (DDIs). mining signals using the spontaneous reporting systems is a very effective method for single drug-induced AE monitoring as well as early detection of DDIs. The objective of this study was to compare signal detection algorithms for DDIs based on frequency statistical models. Methods Five frequency statistical models: the omega shrinkage measure, additive (risk difference), multiplicative (risk ratio), combination risk ratio, and chi-square statistics models were compared using the Japanese Adverse Drug Event Report (JADER) database as the spontaneous reporting system in Japan. The drugs targeted for the survey are all registered and classified as "suspect drugs" in JADER, and the AEs targeted for this study were the same as those in a previous study on Stevens-Johnson syndrome (SJS). Results Of 3924 pairs that reported SJS, the number of signals detected by the omega shrinkage measure, additive, multiplicative, combination risk ratio, and chi-square statistics models was 712, 3298, 2252, 739, and 1289 pairs, respectively. Among the five models, the omega shrinkage measure model showed the most conservative signal detection tendency. Conclusion Specifically, caution should be exercised when the number of reports is low because results differ depending on the statistical models. This study will contribute to the selection of appropriate statistical models to detect signals of potential DDIs.
In higher education, students face challenges when choosing elective courses in their study programmes. Most higher education institutions employ advisors to assist with this task. Recommender systems have their origi...
详细信息
In higher education, students face challenges when choosing elective courses in their study programmes. Most higher education institutions employ advisors to assist with this task. Recommender systems have their origins in commerce and are used in other sectors such as education. Recommender systems offer an alternative to the use of human advisors. This paper aims to examine the scope of recommender systems that assist students in choosing elective courses. To achieve this, a systematic literature review (SLR) on recommender systems corpus for choosing elective courses published from 2010-2019 was conducted. Of the 16 981 research articles initially identified, only 24 addressed recommender systems for choosing elective courses and were included in the final analysis. These articles show that several recommender systems approaches and data mining algorithms are used to achieve the task of recommending elective courses. This study identified gaps in current research on the use of recommender systems for choosing elective courses. Further work in several unexplored areas could be examined to enhance the effectiveness of recommender systems for elective courses. This study contributes to the body of literature on recommender systems, in particular those applied for assisting students in choosing elective courses within higher education.
Optimizing energy consumption in modern mobile handheld devices plays a crucial role as lowering power consumption impacts system's autonomy and system reliability. Recent mobile platforms have an increasing numbe...
详细信息
ISBN:
(纸本)9781450359337
Optimizing energy consumption in modern mobile handheld devices plays a crucial role as lowering power consumption impacts system's autonomy and system reliability. Recent mobile platforms have an increasing number of sensors and processing components. Added to the increasing popularity of power-hungry applications, battery life in mobile devices is an important issue. However, we think that the utilization of the large amount of data from the various sensors can be beneficial to detect the changing device context, the user needs or the running application requirements in terms of resources. When these information are used properly, an efficient control of power knobs can be implemented thus reducing the energy consumption. This paper presents URBOC, for User Request Based Optimization Component. This component is an extension of our previous framework [7] ENOrMOUS. This framework was able to identify and classify the user contexts in order to understand user habits, preferences and needs which allow to improve the operating system power scheme. In this paper, we extend the use of ENOrMOUS by allowing the user to send requests in order to extend the battery life up to a specific time. Machine Learning (ML) and data mining algorithms have been used to obtain an efficient trade-off between power consumption reduction opportunities and user requests satisfaction. The proposed solution increases battery life by up to seven hours depending on user requests vs. the out-of-the-box operating system power manager with a negligible overhead.
At present, more and more patients suffering from knee OA (Ostarthritis) are treated with complementary and alternative medicine, such as herbal drugs, herbal patches, acupuncture and manipulation etc, as an effective...
详细信息
ISBN:
(纸本)9781467327466;9781467327459
At present, more and more patients suffering from knee OA (Ostarthritis) are treated with complementary and alternative medicine, such as herbal drugs, herbal patches, acupuncture and manipulation etc, as an effective therapy. However, traditional statistical methods data gathered from randomized controlled trials (RCT) which were considered as the golden standard for therapy effectiveness failed to confirm those therapies efficacy. Whether we can accurately predict these therapeutic effects on the basis of a prospective, five-center, parallel-group, randomized controlled trial by means of other innovative ways is the question. According to this question, our team adopted several commonly used data mining algorithms to study it, such as KNN (k-Nearest Neighbor algorithm), j48 (decision tree), ANN (Artificial Neural Network). By means of modeling analysis of the patients' Traditional Chinese Medicine (TCM) symptoms questionnaire, Western Ontario and McMaster Universities Index of OA (WOMAC) total score and SF-36 assessment to predict the therapeutic effect which a patient can achieve after adopting one of those TCM therapies. Then we comprehensively analysed the effect and characteristic of every therapy schedule.
The science of bioinformatics has been accelerating at a fast pace, introducing more features and handling bigger volumes. However, these swift changes have, at the same time, posed challenges to datamining applicati...
详细信息
The science of bioinformatics has been accelerating at a fast pace, introducing more features and handling bigger volumes. However, these swift changes have, at the same time, posed challenges to datamining applications, in particular efficient association rule mining. Many data mining algorithms for high-dimensional datasets have been put forward, but the sheer numbers of these algorithms with varying features and application scenarios have complicated making suitable choices. Therefore, we present a general survey of multiple association rule miningalgorithms applicable to high-dimensional datasets. The main characteristics and relative merits of these algorithms are explained, as well, pointing out areas for improvement and optimization strategies that might be better adapted to high-dimensional datasets, according to previous studies. Generally speaking, association rule miningalgorithms that merge diverse optimization methods with advanced computer techniques can better balance scalability and interpretability.
The pervasive and increasing deployment of smart meters allows collecting a huge amount of fine-grained energy data in different urban scenarios. The analysis of such data is challenging and opening up a variety of in...
详细信息
The pervasive and increasing deployment of smart meters allows collecting a huge amount of fine-grained energy data in different urban scenarios. The analysis of such data is challenging and opening up a variety of interesting and new research issues across energy and computer science research areas. The key role of computer scientists is providing energy researchers and practitioners with cutting-edge and scalable analytics engines to effectively support their daily research activities, hence fostering and leveraging data-driven approaches. This paper presents SPEC, a scalable and distributed engine to predict building-specific power consumption. SPEC addresses the full analytic stack and exploits a data stream approach over sliding time windows to train a prediction model tailored to each building. The model allows us to predict the upcoming power consumption at a time instant in the near future. SPEC integrates different machine learning approaches, specifically ridge regression, artificial neural networks, and random forest regression, to predict fine-grained values of power consumption, and a classification model, the random forest classifier, to forecast a coarse consumption level. SPEC exploits state-of-the-art distributed computing frameworks to address the big data challenges in harvesting energy data: the current implementation runs on Apache Spark, the most widespread high-performance data-processing platform, and can natively scale to huge datasets. As a case study, SPEC has been tested on real data of an heating distribution network and power consumption data collected in a major Italian city. Experimental results demonstrate the effectiveness of SPEC to forecast both fine-grained values and coarse levels of power consumption of buildings.
暂无评论