4th International Conference on Software Engineering and
Applications (SOEA 2020)

November 28~29, 2020, Dubai, UAE

Accepted Papers


Anomaly Detection in Time Series based Deep Learning

Rim Romdhane1, Zeineb Ghrib1 and Rakia Jaziri2, 1Deavoteam Research and Innovation, France, 2Paris 8 University, France

ABSTRACT

Anomaly detection is the process of identifying unexpected items or events in data sets, which differ from the norm. Anomaly detection is an active research field which attracts the attention of many business and research actors. Typically, anomalous data can be connected to some kind of problem or rare event such as bank fraud, medical problems, cloud monitoring or network intrusions detection, etc. Dealing with effective anomaly detection for complex and high-dimensional time series data remains a challenging task. In this work, We propose an approach based on an LSTM Autoencoder trained on normal records to learn efficient normal sequence representations combined with a supervised classifier to detect abnormal data. Experimental results show that the encoding step based on LSTM pretrained encoder alows to get efficient representation of data that we can accurately detect abnormal records. In fact, the encoded representation reduces significantly the correlations between normal and abnormal records and allows us to have an efficient latent data representation that separates consistently the two classes. The proposed approach was compared with state-of-the art approaches [19],[16],[20],[27] and outperform them by reducing significantly the classification error.

KEYWORDS

Anomaly detection, LSTM Autoencoder, Supervised Classification, Latent Representation.


Local Branching Strategy-based Method for the Knapsack Problem with Setup

Samah Boukhari1, Isma Dahmani2 and Mhand Hifi3, 1LaROMaD, USTHB, BP 32 El Alia, 16111 Alger, Algérie, 2AMCD-RO, USTHB, BP 32, El Alia, 16111 Bab Ezzouar, Alger, Algerie, 3EPROAD EA4669, UPJV, 7 rue du Moulin Neuf, 80000 Amiens, France

ABSTRACT

In this paper, we propose to solve the knapsack problem with setups by combining mixed linear relaxation and local branching. The mixed linear relaxation can be viewed as driving problem, where it is solved by using a special black-box solver while the local branching tries to enhance the solutions provided by adding a series of invalid / valid constraints. The performance of the proposed method is evaluated on benchmark instances of the literature and new large-scale instances. Its provided results are compared to those reached by the Cplex solver and the best methods available in the literature. New results have been reached.

KEYWORDS

Knapsack, Setups, Local Branching, Relaxation.


Glaucoma Screening using Simple Fusion Features

Panaree Chaipayom1, Assc Prof.Somying Thainimit2, Dr.Duangrat Gansawat3 and Prof.Hirohiko Kaneko4, 1Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand, 2Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand, 3National Electronics and Computer Technology Center, Pathum Thani, Thailand and 4Department of Information and Communications Engineering, Tokyo Institute of Technology, Yokohama, Japan

ABSTRACT

Glaucoma is the second most common cause of blindness. It is caused by high intraocular pressure within the eye, result in an injury to the optic nerve. Fundus images are widely used to detect diagnosis of glaucoma. Therefore, image processing technology is used in various systems that aid in screening as glaucoma screening system can be used to screen for glaucoma. The use of glaucoma screening system can help solve cost issues and the workload of healthcare professionals. This study proposes a method for identifying glaucoma from fundus images by using a fusion three feature to find glaucoma’s significant by using wavelet decomposition and texture such as Discrete Wavelet Transform (DWT), Principal Components Analysis (PCA), and Local Binary Patterns (LBP). Support vector machine (SVM) is used to classify high accuracy at 95% by using tenfold cross-validation with HRF Database and using fusion three feature as DWT, PCA, and LBP.

KEYWORDS

Glaucoma, Fundus Image, Data Mining, Feature Extraction, Feature Ranking, Classification.


Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images

Yakoop Razzaz Hamoud Qasim, Habeb Abdulkhaleq Mohammed Hassan AL-Sameai, AbdulelahAbdulkhaleq Mohammed Hassan, Department of Mechatronics and Robotics Engineering, Taiz University, Yemen

ABSTRACT

In this paper we present a Convolutional Neural Networks consisting of NASNet and MobileNet in parallel (concatenation) to classify three classes COVID-19, normal and pneumonia depending on a dataset of 1083 x-ray images divided into 361 images for each class. VGG16 and RESNet152-v2 modelswere also prepared and trained on the same dataset to compare performance of the proposed model with the performance of these two models. After training the networks and verifying their performance, an overall accuracy 96.91% for the proposed model, 92.59% for VGG16 model and 94.14% for RESNet. We obtained accuracy, sensitivity, specificity and precision 99.69%, 99.07%, 100% and 100% respectively for the proposed model related to the COVID-19 class. These results were better than the results of other models. The conclusion, neural networks are built from models in parallel are effective when the data available for training are small and the different classes features are similar.

KEYWORDS

Deep Learning, Concatenation Technique, Convolutional Neural Networks, COVID-19, Transfer Learning.


Finding Music Formal Concepts Consistent with Acoustic Similarity

Yoshiaki OKUBO, Faculty of Information Science and Technology, Hokkaido University N-14 W-9, Sapporo 060-0814, Japan

ABSTRACT

In this paper, we present a method of finding conceptual clusters of music objects based on Formal Concept Analysis. A formal concept (FC) is defined as a pair of extent and intent which are sets of objects and terminological attributes commonly associated with the objects, respectively. Thus, an FC can be regarded as a conceptual cluster of similar objects for which its similarity can clearly be stated in terms of the intent. We especially discuss FCs in case of music objects, called music FCs. Since a music FC is based solely on terminological information, we often find extracted FCs would not always be satisfiable from acoustic point of view. In order to improve their quality, we additionally require our FCs to be consistent with acoustic similarity. We design an efficient algorithm for extracting desirable music FCs. Our experimental results for The MagnaTagATune Dataset shows usefulness of the proposed method.

KEYWORDS

formal concept analysis, music formal concepts, music objects, terminological similarity, acoustic similarity.


Multi-sensor Calibration Method based on Master-slave Coupling for 3D Reconstruction

Minhtuan Ha1,2, Dieuthuy Pham1,2, Yucheng Li1 and Changyan Xiao1, 1College of Electrical and Information Engineering, Hunan University, Changsha 410208, China, 2Faculty of Electrical Engineering, Saodo University, Haiduong 170000, Vietnam

ABSTRACT

Multi-sensor systems are known as an effective solution to the problems in 3D reconstruction such as a small field-of-view and self-occlusion. However, the dispersed distribution of views of the imaging system is still challenging a global calibration for the whole system. In this paper, a complete set of high-precision calibration scheme is presented. Firstly, the problem of control point extraction of projector is solved by using the method of mark reconstruction. Then, a multi-sensor calibration method based on master-slave coupling is proposed, and a global calibration objective function is established for the system by using the idea of the bundle adjustment method. The calibration parameters of the system are obtained through optimization, which can avoid complex point cloud registration operation. The experimental results show that the average center of gravity error of the control points of the proposed method is 5.729um which is much better than the conventional methods.

KEYWORDS

Multi-sensor calibration, Master-slave coupling, 3D reconstruction & Point cloud optimization.


Smart Farming

Oliver L. Iliev1, Ahmad Zakeri2, Bojan Despodov1, Navya Venkateshaiah2, Simona Ivkovska1, Kyaw Min Naing2, Aleksandar Stojkovski1, 1Institute of Applied Sciences, American University of Europe - FON, Av. Kiro Gligorov, bb. 1000 Skopje, Republic of Macedonia, 2School of Engineering, University of Wolverhamton, , Wulfruna St. WV1 1LY, Wolverhampton, United Kingdom

ABSTRACT

Contemporary Agriculture is facing numerous challenges including: continually increasing demand for quality food, shortages of labour and arable land, irrigation water reduction, increased soil contamination, loss of yields due to the plant diseases and pests. In such circumstances, to maintain the efficiency of the agricultural industry the sector needs to resort to the latest networking and artificial Intelligence (AI) techniques in order to optimize resources and sustainably produce quality and ecologically healthy food. An integrated tool based on Internet of Things (IoT) and Artificial Neural Networks (ANN) technologies will enable the industry to collect, process, transmit data and make autonomous decisions and actions based on incorporated informal knowledge obtained through using Fuzzy logic without need of human interaction. The capabilities offered by IoT including basic communications infrastructure (used to connect smart devices - from sensors, vehicles, Unmanned Aerial Vehicles (UAVs), to user-friendly mobile devices - using the Internet) and a range of services, such as local or remote information retrieval, intelligent information analysis, pattern recognition and processes of autonomous decision making based on AI and agriculture automation. There is no doubt that such integrated technology will revolutionize the agricultural industry which is probably one of the most inefficient sectors today. In this paper we present our current project status and further developments.

KEYWORDS

Artificial Intelligence, Artificial Neural networks, Fuzzy Logic, Internet of Things, Unmanned Aerial Vehicles.


Left to Right-right Most Parsing Algorithm with Lookahead

Jamil Ahmed, AvantureBytes, Canada

ABSTRACT

Left to Right-Right Most (LR) parsing algorithm is a widely used algorithm of syntax analysis. It is contingent on a parsing table whereas the parsing tables are extracted from the grammar. The parsing table specifies the actions to be taken during parsing. It requires that the parsing table should have no action conflicts for the same input symbol. This requirement imposes a condition on the class of grammars over which the LR algorithms work. However, there are grammars for which the parsing tables hold action conflicts. In such cases, the algorithm needs a capability of scanning (looking-ahead) next input symbols ahead of the current input symbol. In this paper, a ‘Left to Right’-‘Right Most’ parsing algorithm with lookahead capability is introduced. The “look-ahead” capability in the LR parsing algorithm is the major contribution of this paper. The practicality of the proposed algorithm is substantiated by the parser implementation of the Context Free Grammar (CFG) of an already proposed programming language “State Controlled Object Oriented Programming” (SCOOP). SCOOP’s Context Free Grammar has 125 productions and 192 item sets. This algorithm parses SCOOP while the grammar requires to ‘look ahead’ the input symbols due to action conflicts in its parsing table. Proposed LR parsing algorithm with lookahead capability can be viewed as an optimization of ‘Simple Left to Right’-‘Right Most’ (SLR) parsing algorithm.

KEYWORDS

Left to Right-Right Most (LR) Parsing, Syntax Analysis, Bottom-Up parsing algorithm.