Human Activity recognition using embedded sensors has lately made renowned development and is drawing growing attention in numerous application domains including machinelearning, patternrecognition, context awarenes...
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ISBN:
(纸本)9781728107882
Human Activity recognition using embedded sensors has lately made renowned development and is drawing growing attention in numerous application domains including machinelearning, patternrecognition, context awareness, and human-centric sensing. Due to the lacking of a prominent analysis of this topic that can acquaint concomitant communities of the research avant-garde, there are still vital perspectives that, if pleaded, would create a vital turn in the way of interaction among people and mobile devices. In this paper, we have presented a comprehensive survey along with the prevailing state of various challenges of human activity recognition based on wearable, environmental, and smartphone sensors. Firstly, we have shown numerous factors to be considered for the data pre-processing part regarding noise filtering and segmentation methods. Besides, we have made a list of sensing devices, sensors, and applications that can be used for collecting activity data along with a discussion on sensor position and requirements. Moreover, we have made a comprehensive analysis of some benchmark datasets, which includes information about sensors, attributes, activity classes, etc. Finally, we have shown an analysis of activity recognition approaches on some of the benchmark datasets based on existing works.
This paper proposes a navigational method for mining by collecting evidences from diverse data sources. Since the representation method and even semantics of data elements differ widely from one data source to the oth...
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ISBN:
(纸本)9783642111631
This paper proposes a navigational method for mining by collecting evidences from diverse data sources. Since the representation method and even semantics of data elements differ widely from one data source to the other, consolidation of data under a single platform doesn't become cost effective. Instead, this paper has proposed a method of mining in steps where knowledge gathered in one step or from one data source is transferred to the next step or next data source exploiting a distributed environment. This incremental mining process ultimately helps in arriving at the desired result. The entire work has been done in the domain of systems biology. Indication has been given how this process can be followed in other application areas as well.
This paper proposes a process of Handwritten Character recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for f...
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ISBN:
(纸本)9781728107882
This paper proposes a process of Handwritten Character recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have various practical applications. The dataset used in this experiment is the BanglaLekha-Isolated dataset [1]. Using Convolutional Neural Network, this model achieves 91.81% accuracy on the alphabets (50 character classes) on the base dataset, and after expanding the number of images to 200,000 using data augmentation, the accuracy achieved on the test set is 95.25%. The model was hosted on a web server for the ease of testing and interaction with the model. Furthermore, a comparison with other machinelearning approaches is presented.
This paper reports on datamining of Cretan folk songs for distinctive patterns. A pattern is distinctive if it occurs with higher probability in a corpus as compared to an anticorpus. A small set of Cretan folk songs...
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ISBN:
(纸本)9781450301619
This paper reports on datamining of Cretan folk songs for distinctive patterns. A pattern is distinctive if it occurs with higher probability in a corpus as compared to an anticorpus. A small set of Cretan folk songs was collected, organized using a small knowledge base of classes, and mined using distinctive pattern discovery methods. Several highly distinctive and confident patterns emerge.
The emergence of patternrecognition, artificial intelligence and machinelearning. Bring new height to the development of smart agriculture. This paper studies an image classification algorithm, An intelligent pest m...
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ISBN:
(数字)9781728175621
ISBN:
(纸本)9781728175621
The emergence of patternrecognition, artificial intelligence and machinelearning. Bring new height to the development of smart agriculture. This paper studies an image classification algorithm, An intelligent pest monitoring system is built based on this algorithm. The system adopts UAV monitoring equipment carrying artificial intelligence module and intelligent recognition algorithm and ground monitoring equipment. Realize diseases and insect pests accurate prediction and intelligent diagnosis. To solve the current industry monitoring mode is single, low level of intelligence, monitoring type limitations and other issues. With the power of wisdom, drive China's agriculture to flourish.
The proceedings contain 273 papers. The topics discussed include: intelligent automation based gas valve control mechanism in biogas plant;analysis of guiding quality evaluation model based on regional ecological safe...
ISBN:
(纸本)9781728170893
The proceedings contain 273 papers. The topics discussed include: intelligent automation based gas valve control mechanism in biogas plant;analysis of guiding quality evaluation model based on regional ecological safety performance evaluation and information mining;review on materials and methods for supercapacitors;performance analysis of current-fed DAB converter for DC microgrid with active power control;computer data processing mode in the era of big data: from patternrecognition to intelligent sensing;road conditions and obstacles indication and autonomous braking system;a study of big data analytics using apache spark with python and Scala;implementing a DC UPS with battery’s state of charge estimation based on coulomb-counting method;cascaded GSM detector-jammer design;diabetes prediction by using big data tool and machinelearning approaches;and an efficient design of fault tolerant reversible multiplexer using QCA technology.
The proceedings contain 43 papers. The topics discussed include: application research of model-free reinforcement learning under the condition of conditional transfer function with coupling factors;expected regret min...
ISBN:
(纸本)9781450375511
The proceedings contain 43 papers. The topics discussed include: application research of model-free reinforcement learning under the condition of conditional transfer function with coupling factors;expected regret minimization for Bayesian optimization with student’s-t processes;a mining frequent itemsets algorithm in stream data based on sliding time decay window;experimental and theoretical scrutiny of the geometric derivation of the fundamental matrix;dual-precision deep neural network;annotating documents using active learning methods for a maintenance analysis application;offline handwritten Chinese character recognition based on improved Googlenet;a network combining local features and attention mechanisms for vehicle re-identification;and a spatial attention-enhanced multi-timescale graph convolutional network for skeleton-based action recognition.
In this paper, we introduce a system that integrates activity recognition, which collects various activities of the elderly people of a nursing care facility in Japan. This system incorporates various activity levels,...
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ISBN:
(纸本)9781728107882
In this paper, we introduce a system that integrates activity recognition, which collects various activities of the elderly people of a nursing care facility in Japan. This system incorporates various activity levels, sensor data through smartphones, and records by nurses for a period of 4 months. There are 28 activity labels, recorded by a number of staffs/nurses. The system architecture has mobile application to encode data and a networked system in the 6-floored facility, covering a number of residents. The system is designed in such a manner that even a non-expert nurse or user can manage this system. After introducing the entire system and data collection procedure, we recognized the acitivities by exploiting some statistical features and Extremely Randomized Tree. We noticed that there are discrepancies between start and end recording time-stamps. Therefore, we explored a time correction technique. The dataset is an extensive one and it can be explored for various activity recognition in smart-homes or elder care facilities. The dataset includes the sensor data from staffs' smartphones, activity labels, and care details input using the system.
Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kern...
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ISBN:
(纸本)9783642030697
Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.
Chronic kidney disease (CKD), is also known as chronic nephritic sickness. It defines constrains which affects your kidneys and reduces your potential to stay healthy. There will be various complication concerns like ...
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ISBN:
(纸本)9781538678084
Chronic kidney disease (CKD), is also known as chronic nephritic sickness. It defines constrains which affects your kidneys and reduces your potential to stay healthy. There will be various complication concerns like increased levels in your blood, anemia (low blood count), weak bones, and nerve injury. Detection and treatment should be done prior so it will typically keep chronic uropathy from obtaining a worse condition. data processing is the term used for information discovery from big databases. The task of knowledge mining is to generate regular patterns from historical data and emphasize future conclusions, follows from the convergence of many recent trends: the decreased value of huge knowledge storage devices and therefore the tremendous ease of aggregation knowledge over networks;the development of robust and economical machinelearning algorithms to method this data;and therefore the decrease value of machine power, enabling use of computationally intensive strategies for knowledge analysis. machinelearning is an important task as it benefits many applications such as analyzing life science outcomes, sleuthing fraud, sleuthing faux users etc. varied knowledge mining classification approaches and machinelearning algorithms are applied for prediction of chronic diseases. Therefore, this paper examines the performance of Naive Bayes, K-Nearest Neighbour (KNN) and Random Forest classifier on the basis of its accuracy, preciseness and execution time for CKD prediction. Finally, the outcome after conducted research is that the performance of Random Forest classifier is finest than Naive Bayes and KNN
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