The reduction of polynomial degrees using moving average filter and derivative approach to decrease the computational load in polynomial classifiers

Carbon monoxide is a type of pollutant that is harmful to human health and the environment. On the other hand, carbon monoxide also has benefits for industrial matter. Since the benefits and disadvantages of carbon monoxide, the measurement of carbon monoxide concentration is required. The measurement of carbon monoxide level is not easy moreover with low-cost sensors. The usage of 4 sensors namely TGS2611, TGS2612, TGS2610 and TGS2602 has been used along with feature extractor. The polynomial classifier is required to interpret the feature vector into the amount of substance concentration. The common classifier methods suffer fatal limitations. The polynomial classifiers method offers lower complexity in solution and lower computational effort. Since the involvement of a huge number of data points in the modelling process leads to high degree in the polynomial model. The occurrence of Runge's phenomenon is highly possible in this condition. This phenomenon affects the accuracy level of the generated model. The degree reduction algorithm is required to prevent the occurrence of Runge’s phenomenon. The combination of MAF (Mean Average Filter) and derivative approach as degree reductor algorithm has succeeded in reducing the polynomial model degree. The greater the number degree in the model means the greater the computational load. The model degree reductor algorithm has been succeeded to reduce computational load by 96.6%.Karbon monoksida merupakan salah satu jenis polutan yang berbahaya bagi kesehatan manusia dan lingkungan. Di sisi lain, karbon monoksida juga memiliki manfaat untuk keperluan industri. Karena kelebihan dan kekurangan karbon monoksida, maka diperlukan pengukuran konsentrasi karbon monoksida. Pengukuran kadar karbon monoksida tidak mudah apalagi dengan sensor yang murah. Penggunaan 4 sensor yaitu TGS2611, TGS2612, TGS2610 dan TGS2602 telah digunakan bersama dengan feature extractor. Pengklasifikasi polinomial diperlukan untuk menginterpretasikan vektor fitur ke dalam jumlah konsentrasi zat. Metode pengklasifikasi umum mengalami keterbatasan fatal. Metode pengklasifikasi polinomial menawarkan kompleksitas yang lebih rendah dalam solusi dan upaya komputasi yang lebih rendah. Karena keterlibatan sejumlah besar titik data dalam proses pemodelan mengarah ke derajat yang tinggi dalam model polinomial. Fenomena Runge sangat mungkin terjadi pada kondisi ini. Fenomena ini mempengaruhi tingkat akurasi model yang dihasilkan. Algoritma reduksi derajat diperlukan untuk mencegah terjadinya fenomena Runge. Kombinasi MAF (Mean Average Filter) dan pendekatan turunan sebagai algoritma pereduksi derajat telah berhasil mereduksi derajat model polinomial. Semakin besar angka derajat dalam model berarti semakin besar beban komputasinya. Algoritma pereduksi derajat model telah berhasil mengurangi beban komputasi sebesar 96,6%

Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

Musical genre classification is put into context byexplaining about the structures in music and how it is analyzedand perceived by humans. The increase of the music databaseson the personal collection and the Internet has brought a greatdemand for music information retrieval, and especiallyautomatic musical genre classification. In this research wefocused on combining information from the audio signal thandifferent sources. This paper presents a comprehensivemachine learning approach to the problem of automaticmusical genre classification using the audio signal. Theproposed approach uses two feature vectors, Support vectormachine classifier with polynomial kernel function andmachine learning algorithms. More specifically, two featuresets for representing frequency domain, temporal domain,cepstral domain and modulation frequency domain audiofeatures are proposed. Using our proposed features SVM act asstrong base learner in AdaBoost, so its performance of theSVM classifier cannot improve using boosting method. Thefinal genre classification is obtained from the set of individualresults according to a weighting combination late fusionmethod and it outperformed the trained fusion method. Musicgenre classification accuracy of 78% and 81% is reported onthe GTZAN dataset over the ten musical genres and theISMIR2004 genre dataset over the six musical genres,respectively. We observed higher classification accuracies withthe ensembles, than with the individual classifiers andimprovements of the performances on the GTZAN andISMIR2004 genre datasets are three percent on average. Thisensemble approach show that it is possible to improve theclassification accuracy by using different types of domainbased audio features

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks

Basic research planning in mathematical pattern recognition and image analysis

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis

A Comprehensive Literature Review on Convolutional Neural Networks

The fields of computer vision and image processing from their initial days have been dealing with the problems of visual recognition. Convolutional Neural Networks (CNNs) in machine learning are deep architectures built as feed-forward neural networks or perceptrons, which are inspired by the research done in the fields of visual analysis by the visual cortex of mammals like cats. This work gives a detailed analysis of CNNs for the computer vision tasks, natural language processing, fundamental sciences and engineering problems along with other miscellaneous tasks. The general CNN structure along with its mathematical intuition and working, a brief critical commentary on the advantages and disadvantages, which leads researchers to search for alternatives to CNN’s are also mentioned. The paper also serves as an appreciation of the brain-child of past researchers for the existence of such a fecund architecture for handling multidimensional data and approaches to improve their performance further

Earth resources: A continuing bibliography with indexes (issue 51)

This bibliography lists 382 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Domain Knowledge Infusion in Machine Learning for Digital Signal Processing Applications : An in-depth case study on table tennis stroke recognition

Diese Arbeit untersucht die Infusion von Domänenwissen als eine Möglichkeit zur Optimierung von Anwendungen des maschinellen Lernens in der Signalverarbeitung. Als Anwendungsbeispiel wird die Erkennung von Tischtennisschlägen anhand von Signalen detailliert analysiert. Die Signale stammen von Sensoren, die in einer am Handgelenk getragenen Smartwatch integriert sind. Domänenwissen wird auf verschiedenen Abstraktionsebenen verwendet, um die Schlagerkennung und -klassifikation zu verbessern. Diese reichen von der Wahl und Fusion tischtennisrelevanter Sensoren, über Low-Level-Signalkorrekturen, bis hin zu Zustandsautomaten, die basierend auf dem Wissen über gültige Schlagsequenzen eine Selbstkorrektur von Fehlklassifikationen ermöglichen. Die Evaluation des LSTMbasierten Prototyps zeigt, dass er erfolgreich zwischen Spiel/kein Spiel, Schlag/kein Schlag, und acht Schlagarten (Vorhand/Rückhand Konter, Topspin, Block, Unterschnitt) unterscheiden kann, sowie Metriken zukünftiger Schläge zur Analyse des Spielstils basierend auf vergangenen Schlägen vorhersagen kann. Das System wurde basierend auf 3770 Schlägen von zwei langjährigen Tischtennisspielern entwickelt und validiert. Die Daten wurden in einer kontrollierten Umgebung unter Zuhilfenahme eines Tischtennisroboters gesammelt, der Bälle präzise servieren kann

Pattern recognition using genetic programming for classification of diabetes and modulation data

The field of science whose goal is to assign each input object to one of the given set of categories is called pattern recognition. A standard pattern recognition system can be divided into two main components, feature extraction and pattern classification. During the process of feature extraction, the information relevant to the problem is extracted from raw data, prepared as features and passed to a classifier for assignment of a label. Generally, the extracted feature vector has fairly large number of dimensions, from the order of hundreds to thousands, increasing the computational complexity significantly. Feature generation is introduced to handle this problem which filters out the unwanted features. The functionality of feature generation has become very important in modern pattern recognition systems as it not only reduces the dimensions of the data but also increases the classification accuracy. A genetic programming (GP) based framework has been utilised in this thesis for feature generation. GP is a process based on the biological evolution of features in which combination of original features are evolved. The stronger features propagate in this evolution while weaker features are discarded. The process of evolution is optimised in a way to improve the discriminatory power of features in every new generation. The final features generated have more discriminatory power than the original features, making the job of classifier easier. One of the main problems in GP is a tendency towards suboptimal-convergence. In this thesis, the response of features for each input instance which gives insight into strengths and weaknesses of features is used to avoid suboptimal-convergence. The strengths and weaknesses are utilised to find the right partners during crossover operation which not only helps to avoid suboptimal-convergence but also makes the evolution more effective. In order to thoroughly examine the capabilities of GP for feature generation and to cover different scenarios, different combinations of GP are designed. Each combination of GP differs in the way, the capability of the features to solve the problem (the fitness function) is evaluated. In this research Fisher criterion, Support Vector Machine and Artificial Neural Network have been used to evaluate the fitness function for binary classification problems while K-nearest neighbour classifier has been used for fitness evaluation of multi-class classification problems. Two Real world classification problems (diabetes detection and modulation classification) are used to evaluate the performance of GP for feature generation. These two problems belong to two different categories; diabetes detection is a binary classification problem while modulation classification is a multi-class classification problem. The application of GP for both the problems helps to evaluate the performance of GP for both categories. A series of experiments are conducted to evaluate and compare the results obtained using GP. The results demonstrate the superiority of GP generated features compared to features generated by conventional methods

Development of automatic image analysis methods for high-throughput and high-content screening

This thesis focuses on the development of image analysis methods for ultra-high content analysis of high-throughput screens where cellular phenotype responses to various genetic or chemical perturbations that are under investigation. Our primary goal is to deliver efficient and robust image analysis platforms which can 1) automatically detect cellular structures of interest from florescence microscope images and 2) quantify dynamics and organization of multi-cellular systems with phenotypic features. To recover heterogeneity of cellular behavior, we aim to develop single-cell-based image analysis methods so that cell subpopulations can be distinguished and investigated. Furthermore, we intend to develop methods to extract an ultra-high level of phenotypic details from images. This would enable system-level studies of phenotype characterization.Netherlands Toxicogenomics Centre; OcellOUBL - phd migration 201