scholarly journals Depth and Width Changeable Network-Based Deep Kernel Learning-Based Hyperspectral Sensor Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Liu ◽  
Tingting Wang ◽  
Yulong Qiao
Keyword(s):  
Data Analysis ◽  
Network Architecture ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Kernel Learning ◽  
Discriminative Ability ◽  
Neural Network Learning ◽  
And Performance ◽  
Hyperspectral Sensor

Sensor data analysis is used in many application areas, for example, Artificial Intelligence of Things (AIoT), with the rapid developing of the deep neural network learning that promotes its application area. In this work, we propose the Depth and Width Changeable Deep Kernel Learning-based hyperspectral sensing data analysis algorithm. Compared with the traditional kernel learning-based hyperspectral data classification, the proposed method has its advantages on the hyperspectral data classification. With the deep kernel learning, the feature is mapped through many times mapping and has the more discriminative ability. So, the deep kernel learning has the better performance compared with the multiple kernels learning. And it has the ability to adjust the network architecture for hyperspectral data space, with the optimization equation of the span bound. The experiments are implemented to testified the feasibility and performance of the algorithms on the hyperspectral data analysis, with the classification accuracy of hyperspectral data. The comprehensive analysis of the experiments shows that the proposed algorithm is feasible to hyperspectral sensor data analysis and its promising classification method in many areas data analysis.

scholarly journals Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jing Liu ◽  
Yulong Qiao
Keyword(s):  
Machine Learning ◽  
Quasiconformal Mapping ◽  
Kernel Function ◽  
Hyperspectral Image ◽  
Data Classification ◽  
Data Retrieval ◽  
Hyperspectral Data ◽  
The Internet ◽  
Discriminative Ability ◽  
Kernel Machine

Intelligent internet data mining is an important application of AIoT (Artificial Intelligence of Things), and it is necessary to construct large training samples with the data from the internet, including images, videos, and other information. Among them, a hyperspectral database is also necessary for image processing and machine learning. The internet environment provides abundant hyperspectral data resources, but the hyperspectral data have no class labels and no so high value for applications. So, it is important to label the class information for these hyperspectral data through machine learning-based classification. In this paper, we present a quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification algorithm for internet-based hyperspectral data retrieval. The contributions include three points: the quasiconformal mapping-based multiple kernel learning network framework is proposed for hyperspectral data classification, the Mahalanobis distance kernel function is as the network nodes with the higher discriminative ability than Euclidean distance-based kernel function learning, and the objective function of measuring the class discriminative ability is proposed to seek the optimal parameters of the quasiconformal mapping projection. Experiments show that the proposed scheme is effective for hyperspectral image classification and retrieval.

scholarly journals A Bidirectional Deep-Learning-Based Spectral Attention Mechanism for Hyperspectral Data Classification

2022 ◽  
Vol 14 (1) ◽  
pp. 217
Author(s):  
Bishwas Praveen ◽  
Vineetha Menon
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Short Term Memory ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Attention Mechanism ◽  
Spectral Information ◽  
Analysis Model ◽  
Analysis Techniques ◽  
Spectral Bands

Hyperspectral remote sensing presents a unique big data research paradigm through its rich information captured across hundreds of spectral bands, which embodies vital spatial and temporal information about the underlying land cover. Deep-learning-based hyperspectral data analysis methodologies have made significant advancements over the past few years. Despite their success, most deep learning frameworks for hyperspectral data classification tend to suffer in terms of computational and classification efficacy as the data size increases. This is largely due to their equal emphasis criteria on the rich spectral information present in the data, albeit all of the spectral information not being essential for hyperspectral data analysis. On the contrary, this redundant information present in the spectral bands can deter the performance of hyperspectral data analysis techniques. Therefore, in this work, we propose a novel bidirectional spectral attention mechanism, which is computationally efficient and capable of adaptive spectral information diversification through selective emphasis on spectral bands that comprise more information and suppress the ones with lesser information. The concept of 3D-convolutions in tandem with bidirectional long short-term memory (LSTM) is used in the proposed architecture as spectral attention mechanism. A feedforward neural network (FNN)-based supervised classification is then performed to validate the performance of our proposed approach. Experimental results reveal that the proposed hyperspectral data analysis model with spectral attention mechanism outperforms other spatial- and spectral-information-extraction-based hyperspectral data analysis techniques compared.

ANALISIS NILAI KOHESI DAN KOHERENSIDALAM TERJEMAHAN AL-QUR’AN SURAH AL AL ZALZALAH

2016 ◽  
Vol 1 (2) ◽  
pp. 23
Author(s):  
MUNIRAH MUNIRAH ◽  
HUSAIN SYARIFUDDIN
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Data Classification ◽  
Research Data ◽  
Relative Pronoun ◽  
Second Person ◽  
Record Keeping ◽  
Descriptive Research ◽  
Qualitative Descriptive

This study aimed to describe the value of cohesion and coherence contained in the translation of the Qur'an surah Al Zalzalah. This study was a qualitative descriptive research, research data collection techniques using three techniques namely, inventory, rading and understanding, and record keeping. The data analysis used the coding of data, classification data, and the determination of the data. The results showed that the cohesion markers used in the translation of surah Al Zalzalah discourse are: 1) reference, 2) pronouns, ie pronouns second person, and third, the relative pronoun, the pronoun pointer, pen pronouns and pronouns owner, 3 ) conjunctions, namely temporal conjunctions, coordinating conjunctions, subordinating conjunctions, and conjunctions koorelatif, and 4) a causal ellipsis. It mean that there was a coherence in the translation of surah Al Zalzalah discourse are: the addition or addition, pronouns, repetition or repetition, match words or synonyms, in whole or in part, a comparison or ratio of conclusions or results. Keywords: Cohesion, Coherence, sura Al Zalzalah AbstrakPenelitian ini bertujuan untuk mendeskripsikan nilai kohesi dan koherensi yang terdapat dalam terjemahan Al-Qur’an surah Al Zalzalah. Jenis penelitian ini termasuk jenis penelitian deskriptif kualitatif, Teknik pengumpulan data penelitian menggunakan tiga teknik yakni, inventarisasi, baca simak, dan pencatatan. Teknik analisis data menggunakan pengodean data, pengklasifikasian data, dan penentuan data. Hasil penelitian menunjukkan bahwa pemarkah kohesi yang digunakan dalam wacana terjemahan surah Al Zalzalah adalah: 1) referensi, 2) pronomina, yaitu kata ganti orang kedua, dan ketiga, kata ganti penghubung, kata ganti penunjuk, kata ganti penanya dan kata ganti empunya, 3) konjungsi, yaitu konjungsi temporal, konjungsi koordinatif, konjungsi subordinatif, dan konjungsi koorelatif, dan 4) elipsis kausal. Sarana koherensi yang terdapat di dalam wacana terjemahan surah Al Zalzalah adalah: penambahan atau adisi, pronomina, pengulangan atau repetisi, padan kata atau sinonim, keseluruhan atau bagian, komparasi atau perbandingan simpulan atau hasil.Kata Kunci: Kohesi, Koherensi, surah Al Zalzalah

scholarly journals Flavor Profiling by Consumers Segmented According to Product Involvement and Food Neophobia

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 598
Author(s):  
Yun-Mi Lee ◽  
Seo-Jin Chung ◽  
John Prescott ◽  
Kwang-Ok Kim
Keyword(s):  
Sensory Evaluation ◽  
Black Tea ◽  
Sensory Attributes ◽  
Individual Characteristics ◽  
Product Involvement ◽  
Discriminative Ability ◽  
Food Neophobia ◽  
Personal Involvement ◽  
Intensity Rating ◽  
And Performance

The relationship between food-related individual characteristics and performance in sensory evaluation was investigated. The study focused on differences in discriminative ability and perceptual sensitivity according to levels of product involvement or food neophobia during the intensity rating of sensory attributes in consumer profiling. Consumers (N = 247) rated the intensity of attributes for seven flavored black tea drinks and completed the Food Neophobia Scale and the Personal Involvement Inventory measuring product involvement with the flavored black tea drink. In the higher product involvement (IH) group and the lower food neophobia (NL) group, the number of sensory attributes representing the sample effect and of subsets discriminating the samples were greater, and more total variance of the samples was explained. The higher the product involvement or the lower the food neophobia, the greater the differentiation in characterizing samples with more attributes in the intensity ratings. Interestingly, the high food neophobia (NH) group showed less active performance compared to the NL group during the sensory evaluation overall, but the NH group was more concerned about unfamiliar attributes and samples. The results implied that the positive attitude resulting from high product involvement and low food neophobia may induce more active behavior and better performance during the sensory evaluation.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

scholarly journals Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Tarek Frahi ◽  
Francisco Chinesta ◽  
Antonio Falcó ◽  
Alberto Badias ◽  
Elias Cueto ◽  
...  
Keyword(s):  
Data Analysis ◽  
The State ◽  
Sensor Data ◽  
Topological Data Analysis ◽  
Motion Sensors ◽  
Driver Assistance Systems ◽  
The Road ◽  
Topological Features ◽  
Recent Developments ◽  
Topological Data

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

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