Context-aware Clustering Research Articles

An adaptive methodology based on predictive deep learning and context aware clustering for electricity power usage mining and optimization at different granularity levels

Smart metering in electricity power grid is an optimistic trend at global level. All smart devices and appliances based on Internet of Things (IoT) are now playing very significant role in household. These days’ electric power usage mining and optimization is possible down to meter level only. However, it is very challenging and significant to go down to different granularity levels such as appliances, various sensors and activities etc. The shifting of the electric power usage to low price electricity is also significant and possible by mining and optimizing electric power usage behaviour at low level. All smart appliances and activities are needs to be customized to when you use them. This paper proposes an adaptive methodology based on predictive deep learning and context aware clustering to discover new ways for mining and optimization of electric power usage at different granularity levels and make optimal decisions for shifting electric power usage to low cost. Here we have considered households and business meters approximately 2000 with unique id of each meter. The data of three months is used for user preference of starting appliance. The predictive accuracy of proposed methodology for usage mining and optimization is improved by average 4 %. Different input data features are used to form clusters of meters with similar power consumption behaviour for household occupancy. The clustering accuracy for household occupancy is improved from 0.68 to 0.91. The impact of accurate household occupancy detection and appliance usage mining and optimization is in reduction of electric power cost. The consumer can see how electric power efficiency and time-of-use shift makes a difference using experimental setup.

Improvement of the recommendation system based on the multi-armed bandit algorithm

In order to effectively solve common problems of the recommendation system, such as the cold start problem and dynamic data modeling problem, the multi-armed bandit (MAB) algorithm, the collaborative filtering (CF) algorithm, and the user information feedback are applied by researchers to update the recommendation model online and in time. In other words, the cold start problem of the recommendation system is transformed into an issue of exploration and utilization. The MAB algorithm is used, user features are introduced as content, and the synergy between users is further considered. In this paper, the author studies the improvement of the recommendation system based on the multi-armed bandit algorithm. The Liner Upper Confidence Bound (LinUCB), Collaborative Filtering Bandits (COFIBA), and Context-Aware clustering of Bandits (CAB) algorithms are analyzed. It is found that the MAB algorithm can get a good maximum total revenue regardless of the content value after going through the cold start stage. In the case of a particularly large amount of content, the CAB algorithm achieves the greatest effect.

Context-Aware Clustering and the Optimized Whale Optimization Algorithm: An Effective Predictive Model for the Smart Grid

For customers to participate in key peak pricing, period-of-use fees, and individualized responsiveness to demand programmes taken from multi-dimensional data flows, energy use projection and analysis must be done well. However, it is a difficult study topic to ascertain the knowledge of use of electricity as recorded in the electricity records' Multi-Dimensional Data Streams (MDDS). Context-Aware Clustering (CAC) and the Optimized Whale Optimization Algorithm were suggested by researchers as a fresh power usage knowledge finding model from the multi-dimensional data streams (MDDS) to resolve issue (OWOA). The proposed CAC-OWOA framework first performs the data cleaning to handle the noisy and null elements. The predictive features are extracted from the novel context-aware group formation algorithm using the statistical context parameters from the pre-processed MDDS electricity logs. To perform the energy consumption prediction, researchers have proposed the novel Artificial Neural Network (ANN) predictive algorithm using the bio-inspired optimization algorithm called OWOA. The OWOA is the modified algorithm of the existing WOA to overcome the problems of slow convergence speed and easily falling into the local optimal solutions. The ANN training method is used in conjunction with the suggested bio-inspired OWOA algorithm to lower error rates and boost overall prediction accuracy. The efficiency of the CAC-OWOA framework is evaluated using the publicly available smart grid electricity consumption logs. The experimental results demonstrate the effectiveness of the CAC-OWOA framework in terms of forecasting accuracy, precision, recall, and duration when compared to underlying approaches.

CAC-WOA: context aware clustering with whale optimization algorithm for knowledge discovery from multidimensional space in electricity application

Energy consumption forecasting is a hot field of research; despite the number of developed models, projecting electric consumption in residential buildings remains problematic owing to the significant unpredictability of occupant energy use behavior. Discovering the electricity consumption knowledge from the multi-dimensional data streams (MDDS) of electricity logs is a challenging research problem. We propose a novel electricity knowledge discovery model proposed from the MDDS using clustering and machine learning. Context-aware clustering with whale optimization algorithm (CAC-WOA) is proposed to discover the predictive features from the electricity MDDS and perform the predictions using WOA. The CAC-WOA consists of two phases context-aware group formation and a WOA-based machine learning predictive model. In the CAC algorithm, group formation using electricity contextual information to estimate the robust predictive features are proposed. Using such predictive features, the predictive model using the WOA-based artificial neural network (ANN) is built. The modified ANN technique using the WOA algorithm is used to reduce the error rates and improve the prediction accuracy. The experimental outcomes using publicly available electricity consumption datasets prove the efficiency of the CAC-WOA model. Overall prediction accuracy is improved by 3.27% and prediction time is reduced by 11.31% using CAC-WOA compared state-of-the-art solutions.

HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning.

Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity among clients is one of the major concerns. However, due to the security issue and the complexity of deep learning models, it is challenging to investigate data heterogeneity across different clients. To address this issue, based on a requirement analysis we developed a visual analytics tool, HetVis, for participating clients to explore data heterogeneity. We identify data heterogeneity through comparing prediction behaviors of the global federated model and the stand-alone model trained with local data. Then, a context-aware clustering of the inconsistent records is done, to provide a summary of data heterogeneity. Combining with the proposed comparison techniques, we develop a novel set of visualizations to identify heterogeneity issues in HFL. We designed three case studies to introduce how HetVis can assist client analysts in understanding different types of heterogeneity issues. Expert reviews and a comparative study demonstrate the effectiveness of HetVis.

Soil Moisture Monitoring of the Plant Root Zone by Using Phenology as Context in Remote Sensing

In this study, the phenological behavior and energy balance of plants are used as a sensory mechanism for root-zone soil moisture monitoring using both in-situ and satellite remote sensing data. The commonly used in-situ measurements are not feasible for mapping soil moisture at large-scale agricultural areas. Local direct root-zone soil moisture measurements cannot be reliably interpolated owing to the high spatial variability of soil structure and the vegetative content. Remote sensing methods are negatively affected by vegetation coverage and density regarding penetration and backscattering characteristics. In order to overcome these limitations, we propose a root-zone soil moisture estimation method utilizing a context-aware data clustering process, which can be applied prior to any statistical analysis, for empirical evaluation of data. In this aspect, the crops’ phenological stages and soil–air temperature differences are defined as the two contexts for data clustering. Parameters such as canopy–air temperature difference, land surface temperature, and solar radiation with respect to plant energy and water processes are used for the analysis. The proposed model is utilized using piecewise linear regression of data obtained from 16 rainfed wheat parcels distributed across Turkey, under different climatic and topographic conditions. It is shown that the proposed context-aware data clustering process enables the nonlinear plant behavior to be analyzed linearly. The correlation value of the whole season increased from 21% to a range between 78% and 95% for different clusters. The outliers became relevant and the parameters became significant after the proposed context-aware data clustering.

An efficient context-aware agglomerative fuzzy clustering framework for plagiarism detection

Plagiarism refers to the act of copying content without acknowledging the original source. Though there are several existing commercial tools for plagiarism detection, still plagiarism is tricky and challenging due to the rise in volume of online publications. Existing plagiarism detection methods use paraphrasing, sentence and key-word matching, but such techniques has not been very effective. In this work, a framework for fuzzy based plagiarism detection is proposed using a context-aware agglomerative clustering approach with an improved time complexity. The work aims in retrieving key concepts at word, sentence and paragraph level by integrating semantic features in a novel optimisation function to detect plagiarism effectively. The notion of fuzzy clustering has been applied to improve the robustness and consistency of results for clustering multi-disciplinary papers. The experimental analysis is supported by comparison with other contemporary techniques which indicate the superiority of proposed approach for plagiarism detection.

A Context-Aware Member Clustering Algorithm Based on Ant Colony and Genetic Optimization for P2P Mobile Social Network

t is one of important topics in social network research how to form member clustering according to potential social relations by automatic context-aware the user's behavior feature. This paper presents a context-aware mobile P2P social network framework, member clustering model and algorithm. The user's location information, environmental characteristics etc. are introduced to the clustering algorithm, which intelligently cluster to potential P2P social network. The experimental results show that the proposed approach and the algorithm have a higher response speed, load balance and adaptive ability.

애드-혹 네트워크에서의 에너지 보존적인 계층 클러스터링에 관한 연구

애드-혹 네트워크는 기간망에 의존하지 않고, 노드들끼리 자율적으로 데이터 이동 경로를 형성하는 네트워크이다. 일반적으로 애드-혹 네트워크는 다중-홉 통신을 기반으로 하며 네트워크의 규모가 커질수록 경로 설정을 위한 제어 패킷의 양이 급격히 증가하게 되고 노드의 수명이 단축 된다. 이러한 노드들의 특성으로 인해서 네트워크 크기에 따른 보다 에너지 효율성 적용이 가능한 경로 설정 기법에 대한 연구가 요구되고 있다. 본 논문에서는 특히 계층적인 클러스터 구조 기반의 애드-혹 네트워크 환경에서 노드가 동질적 공간-포아송의 조건에서 노드의 분포 밀도와 속성정보를 고려한 클러스터의 계층 깊이 결정 알고리즘인 CACHE-R(Context-Aware Clustering Hierarchy, Efficient and Resilient)를 제안한다. 제안하고자 하는 CACHE-R기법은 최적화된 계층의 깊이와 속성정보를 적용함으로써 에너지 보존적인 경로를 설정하여, 전체 네트워크의 에너지 소비량을 감소시키고 네트워크 수명을 향상되는 특성을 보여주었다. An ad-hoc wireless network provides self-organizing data networking while they are routing of packets among themselves. Typically multi-hop and control packets overhead affects the change of route of transmission. There are numerous routing protocols have been developed for ad hoc wireless networks as the size of the network scale. Hence the scalable routing protocol would be needed for energy efficient various network routing environment conditions. The number of depth or layer of hierarchical clustering nodes are analyzed the different clustering structure with topology in this paper. To estimate the energy efficient number of cluster layer and energy dissipation are studied based on distributed homogeneous spatial Poisson process with context-awareness nodes condition. The simulation results show that CACHE-R could be conserved the energy of node under the setting the optimal layer given parameters.

Context-aware cluster-based hierarchical protocol for Wireless Sensor Networks

Wireless Sensor Network (WSN) has been a key research topic for its multi dimensional applications. Its applications require accurate information collecting as well as, longer service life. In WSN, employing an efficient routing protocol plays a significant role in attaining such service requirements. In this paper, an energy efficient routing protocol, Context-Aware Clustering Hierarchy (CACH) has been proposed where cluster formation is entirely based on the context of the environment. Moreover, an efficient technique has been utilised to avoid similar data traffic across the network and cluster head role has been equally distributed among the nodes. The performance in the simulation shows substantial amount of energy optimisation.