scholarly journals Systematic Literature Review pada Analisis Prediktif dengan IoT: Tren Riset, Metode, dan Arsitektur

2020 ◽  
Vol 3 (1) ◽  
pp. 43-53
Author(s):  
Fahrur Rozi
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Smart Home ◽  
Research Question ◽  
Support Vector ◽  
Research Fields ◽  
Vector Machines ◽  
Artificial Neural Network Ann

Nowadays IoT researches on intelligent service systems is becoming a trend. IoT produces a variety of data from sensors or smart phones. Data generated from IoT can be more useful and can be followed up if data analysis is carried out. Predictive analytic with IoT is part of data analysis that aims to predict something solution. This analysis utilization produces innovative applications in various fields with diverse predictive analytic methods or techniques. This study uses Systematic Literature Review (SLR) to understand about research trends, methods and architecture used in predictive analytic with IoT. So the first step is to determine the research question (RQ) and then search is carried out on several literature published in popular journal databases namely IEEE Xplore, Scopus and ACM from 2015 - 2019. As a result of a review of thirty (30) selected articles, there are several research fields which are trends, namely Transportation, Agriculture, Health, Industry, Smart Home, and Environment. The most studied fields are agriculture. Predictive analytic with IoT use varied method according to the conditions of data used. There are five most widely used methods, namely Bayesian Network (BN), Artificial Neural Network (ANN), Recurrent Neural Networks (RNN), Neural Network (NN), and Support Vector Machines (SVM). Some studies also propose architectures that use predictive analytic with IoT.

A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 351
Author(s):  
Lorenzo Colantonio ◽  
Lucas Equeter ◽  
Pierre Dehombreux ◽  
François Ducobu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Research Question ◽  
Support Vector ◽  
Self Organizing Map ◽  
Holistic View ◽  
Benchmark Database ◽  
Depth Analysis

In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.

scholarly journals Intelligent Fish Freshness Assessment

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Hamid Gholam Hosseini ◽  
Dehan Luo ◽  
Guanggui Xu ◽  
Hongxiu Liu ◽  
Deena Benjamin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machines ◽  
Fish Species ◽  
Support Vector ◽  
Intelligent Processing ◽  
Fish Species Identification ◽  
Vector Machines ◽  
Industry Applications ◽  
Artificial Neural Network Ann

Fish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

scholarly journals Machine Learning Methods to Classify Mushrooms for Edibility-A Review

2020 ◽  
Vol 06 (09) ◽  
pp. 54-58
Author(s):  
Rakesh Kumar Y and Dr. V. Chandrasekhar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Literature Review ◽  
Edible Mushroom ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Learning Techniques ◽  
Artificial Neural Network Ann

There are thousands of species of Mushrooms in the world; they are edible and non-edible being poisonous. It is difficult for non-expertise person to Identify poisonous and edible mushroom of all the species manually. So a computer aided system with software or algorithm is required to classify poisonous and nonpoisonous mushrooms. In this paper a literature review is presented on classification of poisonous and nonpoisonous mushrooms. Most of the research works to classify the type of mushroom have applied, machine learning techniques like Naïve Bayes, K-Neural Network, Support vector Machine(SVM), Artificial Neural Network(ANN), Decision Tree techniques. In this literature review, a summary and comparisons of all different techniques of mushroom classification in terms of its performance parameters, merits and demerits faced during the classification of mushrooms using machine learning techniques.

Research on the College Students’ Physique Test Based on Artificial Neural Network and Support Vector Machines Approach

2014 ◽  
Vol 945-949 ◽  
pp. 3558-3561
Author(s):  
Han Sheng Liu
Keyword(s):  
Neural Network ◽  
College Students ◽  
Artificial Neural Network ◽  
Support Vector Machines ◽  
Support Vector ◽  
Test Management ◽  
Vector Machines ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Health Test

In recent years, China’s college students’ physique presents trend of declining. In the colleges and universities health test management, it produces the problems of only paying high attention to the test and evaluation but neglects the link of feedback and improvement. Based on artificial neural network (ANN) and support vector machines (SVM) approach, this paper makes use of the principle component analysis to conduct discussion on China's college students’ physique health test management mechanism design so as to further perfect current procedure of China's college students’ physique health test work, improve the quality of management work, improve the college students’ physique and truly reach the target for the college students’ overall development.

scholarly journals PEMETAAN RISET TENTANG DETEKSI TOPIK PADA TWITTER DENGAN TEKNIK SYSTEMATIC LITERATURE REVIEW

2021 ◽  
Vol 5 (1) ◽  
pp. 55-62
Author(s):  
Dwi Suchisty ◽  
Widodo ◽  
Bambang Prasetya Adhi
Keyword(s):  
Neural Network ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Recurrent Neural Network ◽  
Digital Library ◽  
Deep Neural Network ◽  
Research Question ◽  
Search String

Sebuah dokumen atau tulisan pastinya mengandung suatu informasi penting di dalamnya. Peringkasan dokumen membuat penemuan informasi-informasi tersebut menjadi lebih mudah karena mempersingkat kalimat dengan cara menghilangkan kata atau kalimat yang tidak penting. Peringkasan dokumen saat ini sudah banyak dilakukan dengan cara yang otomatis menggunakan metode-metode yang dikembangkan dari model neural netowork. Penelitian ini bertujuan untuk mengetahui sejauh mana perkembangan metode neural network dalam meringkas dokumen dilakukan dengan cara menganalisis literatur atau penelitian menggunakan teknik systematic literature review. Pengumpulan literatur dilakukan dengan cara melakukan pencarian pada beberapa digital library dengan memasukkan search string yang telah dibuat berdasarkan research question dengan batas publikasi antara tahun 2014-2018. Hasil dari penelitian ini menunjukkan bahwa dari 1266 literatur yang diperoleh 39 diantaranya layak untuk dianalisa. Berdasarkan dari 39 literatur tersebut diketahui bahwa metode neural network yang digunakan untuk meringkas dokumen adalah sebanyak 28 metode. Metode yang paling sering digunakan adalah metode Recurrent Neural Network (RNN) dan metode terbaik yang ditemukan untuk melakukan peringkasan adalah Deep Neural Network (DNN) dengan persentase ketepatan mencapai 62%.

scholarly journals A systematic literature review of automatic Alzheimer’s disease detection from speech and language

2020 ◽  
Vol 27 (11) ◽  
pp. 1784-1797 ◽  
Author(s):  
Ulla Petti ◽  
Simon Baker ◽  
Anna Korhonen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machines ◽  
Literature Review ◽  
Decision Trees ◽  
Systematic Literature Review ◽  
Neural Nets ◽  
Support Vector ◽  
Speech And Language ◽  
Vector Machines

Abstract Objective In recent years numerous studies have achieved promising results in Alzheimer’s Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. Materials and Methods We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? Results and Discussion We identified 33 eligible studies and 5 main findings: participants’ demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. Conclusion The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.

scholarly journals Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Qingdong Wu ◽  
Bo Yan ◽  
Chao Zhang ◽  
Lu Wang ◽  
Guobao Ning ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surrounding Rock ◽  
Support Vector ◽  
Tunnel Construction ◽  
Displacement Prediction ◽  
Vector Machines ◽  
Artificial Neural ◽  
Comparative Results ◽  
Artificial Neural Network Ann

Displacement prediction of tunnel surrounding rock plays an important role in safety monitoring and quality control tunnel construction. In this paper, two methodologies, support vector machines (SVM) and artificial neural network (ANN), are introduced to predict tunnel surrounding rock displacement. Then the two modes are texted with the data ofFangtianchongtunnel, respectively. The comparative results show that solutions gained by SVM seem to be more robust with a smaller standard error compared to ANN. Generally, the comparison between artificial neural network (ANN) and SVM shows that SVM has a higher accuracy prediction than ANN. Results also show that SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.

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