BUILDING A BI-OBJECTIVE QUADRATIC PROGRAMMING MODEL FOR THE SUPPORT VECTOR MACHINE
Mohammed Zakaria Moustafa1, Mohammed Rizk Mohammed2, Hatem Awad Khater3
1,2,4Alexandria University, Alexandria, Egypt
2HORAS University, Damietta, Egypt
A support vector machine (SVM) learns the decision surface from two different classes of the input points, in many applications there are misclassifications in some of the input points. In this paper a biobjective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The experimental results, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. vector, random forests, or naive Bayes classifiers, although the latter methods perform acceptably.
Support vector machine (SVMs), Classification, Multi-objective problems, weighting method, Quadratic programming.
For More Details :
https://aircconline.com/csit/papers/vol10/csit100208.pdf
Volume Link :
http://airccse.org/csit/V10N02.html
A BRIEF SURVEY OF DATA PRICING FOR MACHINE LEARNING
Zuoqi Tang1, Zheqi Lv2, Chao Wu3,4*
1,2,3,4*Zhejiang University, China
Big data and machine learning are poised to revolutionize the field of artificial intelligence and represent a step towards building an intelligent society. Big data is considered to be the key to unlocking the next great waves of growth in productivity, the value of data is realized through machine learning.
In this survey, we begin with an introduction to the general field of data pricing and distributed machine learning then progress to the main streams of data pricing and mechanism design methods. Our survey will cover several current areas of research within the field of data pricing, including the incentive mechanism design for federated learning, reinforcement learning, auction, crowdsourcing, and blockchain, especially, focus on reward function for machine learning and payment scheme. In parallel, we highlight the pricing scheme in data transactions, focusing on data evaluation via distributed machine learning. To conclude, we discuss some research challenges and future directions of data pricing for machine learning.
Data pricing, Big data, Machine learning, Data transaction
For More Details :
https://aircconline.com/csit/papers/vol10/csit100209.pdf
Volume Link :
http://airccse.org/csit/V10N02.html
RESEARCH ON FARMLAND PEST IMAGE RECOGNITION BASED ON TARGET DETECTION ALGORITHM
Shi Wenxiu and Li Nianqiang
School of Information Science and Engineering University of Jinan, Jinan, China
In order to achieve the automatic identification of farmland pests and improve recognition accuracy, this paper proposes a method of farmland pest identification based on target detection algorithm .First of all, a labeled farm pest database is established; then uses Faster R-CNN algorithm, the model uses the improved Inception network for testing; finally, the proposed target detection model is trained and tested on the farm pest database, with the average precision up to 90.54%.
Object detection algorithm, Faster R-CNN, Inception network
For More Details :
https://aircconline.com/csit/papers/vol10/csit100210.pdf
Volume Link :
http://airccse.org/csit/V10N02.html
OVERSAMPLING LOG MESSAGES USING A SEQUENCE GENERATIVE ADVERSARIAL NETWORK FOR ANOMALY DETECTION AND CLASSIFICATION
Amir Farzad and T. Aaron Gulliver
University of Victoria, Canada
Dealing with imbalanced data is one of the main challenges in machine/deep learning algorithms for classification. This issue is more important with log message data as it is typically very imbalanced and negative logs are rare. In this paper, a model is proposed to generate text log messages using a SeqGAN network. Then features are extracted using an Autoencoder and anomaly detection is done using a GRU network. The proposed model is evaluated with two imbalanced log data sets, namely BGL and Openstack. Results are presented which show that oversampling and balancing data increases the accuracy of anomaly detection and classification.
Deep Learning, Oversampling, Log messages, Anomaly detection, Classification
For More Details :
https://aircconline.com/csit/papers/vol10/csit100515.pdf
Volume Link :
http://airccse.org/csit/V10N05.html
INDUSTRIAL DUCT FAN MAINTENANCE PREDICTIVE APPROACH BASED ON RANDOM FOREST
Mashael Maashi, Nujood Alwhibi, Fatima Alamr, Rehab Alzahrani, Alanoud Alhamid and Nourah Altawallah
King Saud University, Riyadh, Kingdom of Saudi Arabia
When manufacturers equipment encounters an unexpected failure, or undergo unnecessary maintenance pre-scheduled plan, which happens for a total of millions of hours worldwide annually, this is time-consuming and costly. Predictive maintenance can help with the use of modern sensing technology and sophisticated data analytics to predict the maintenance required for machinery and devices. The demands of modern maintenance solutions have never been greater. The constant pressure to demonstrate enhanced cost-effectiveness return on investment and improve the competitiveness of the organization is always combined with the pressure of improving equipment productivity and keep machines running at the maximum output. In this paper, we propose maintenance prediction approach based on a machine learning technique namely random forest algorithm. The main focus is on the industrial duct fans as it is one of the most common equipment in most manufacturing industries. The experimental results show the accuracy, reliability of proposed Predictive Maintenance approach.
Predictive Maintenance, Maintenance, Random Forest, Duct Fan, Machine Learning & Artificial Intelligence
For More Details :
https://aircconline.com/csit/papers/vol10/csit100516.pdf
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http://airccse.org/csit/V10N05.html
CLASSIFICATION OF COMPUTER HARDWARE AND PERFORMANCE PREDICTION USING STATISTICAL LEARNING AND NEURAL NETWORKS
Courtney Foots1, Palash Pal2, Rituparna Datta1and Aviv Segev1
1University of South Alabama, Mobile, USA
2University Institute of Technology, Burdwan University, India
We propose a set of methods to classify vendors based on estimated central processing unit (CPU) performance and predict CPU performance based on hardware components. For vendor classification, we use the highest and lowest estimated performance and frequency of occurrences of each vendor in the dataset to create classification zones. These zones can be used to list vendors who manufacture hardware that satisfy given performance requirements. We use multi-layered neural networks for performance prediction, which accounts for nonlinearity in performance data. Several neural network architectures are analysed in comparison to linear, quadratic, and cubic regression. Experiments show that neural networks can be used to obtain low prediction error and high correlation between predicted and published performance values, while cubic regression can produce higher correlation than neural networks when more data is used for training than testing. The proposed methods can be used to identify suitable hardware replacements.
Computer Hardware, Performance Prediction and Classification, Neural Networks, Statistical Learning, Regression
For More Details :
https://aircconline.com/csit/papers/vol10/csit100517.pdf
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THE PARALLEL HTM SPATIAL POOLER WITH ACTOR MODEL
Damir Dobric1, Andreas Pech2, Bogdan Ghita1aand Thomas Wennekers1
1University of Plymouth, Faculty of Science and Engineering, United Kingdom
2Frankfurt University of Applied Sciences, Germany
The Hierarchical Temporal Memory Cortical Learning Algorithm (HTM CLA) is an algorithm inspired by the biological functioning of the neo-cortex, which combines spatial pattern recognition and temporal sequence learning. It organizes neurons in layers of column-like units built from many neurons such that the units are connected into structures called regions (areas). Layers can be hierarchically organized and can further be connected into more complex networks, which would allow to implement higher cognitive capabilities like invariant representations. However, a complex topology and a potentially high number of neurons would require more computing power than a single machine even with multiple cores or a GPU could provide. This paper aims to improve the HTM CLA by enabling it to run on multiple nodes in a highly distributed system of processors; to achieve this we use the Actor Programming Model. The proposed concept also makes use of existing cloud and server less technology and it enables easy setup and operation of cortical algorithms in a distributed environment. The proposed model is based on a mathematical theory and computation model, which targets massive concurrency. Using this model drives different reasoning about concurrent execution and should enable flexible distribution of cortical computation logic across multiple physical nodes.
This work is the first one about the parallel HTM Spatial Pooler on multiple nodes with named computational model. With the increasing popularity of cloud computing and serverless architectures, this work is the first step towards proposing interconnected independent HTM CLA units in an elastic cognitive network. Thereby it can provide an alternative to deep neuronal networks, with theoretically unlimited scale in a distributed cloud environment. This paper specifically targets the redesign of a single Spatial Pooler unit.
Hierarchical Temporal Memory, Cortical Learning Algorithm, HTM CLA, Actor Programming Model, AI, Parallel, Spatial Pooler.
For More Details :
https://aircconline.com/csit/papers/vol10/csit100606.pdf
Volume Link :
http://airccse.org/csit/V10N06.html
BACK-PROPAGATION NEURAL NETWORK BASED METHOD FOR PREDICTING THE INTERVAL NATURAL FREQUENCIES OF STRUCTURES WITH UNCERTAIN- UTBOUNDED PARAMETERS
Pengbo Wang1*, Wenting Jiang2and Qinghe Shi3
1Beihang University, Beijing, China
2Chinese Academy of Sciences, Beijing, China
3Jiangsu University of Technology, China
Uncertain-but-bounded parameters have a significant impact on the natural frequencies of structures, and it is necessary to study their inherent relationship. However, their relationship is generally nonlinear and thus very complicated. Taking advantage of the strong non-linear mapping ability and high computational efficiency of BP neural networks, namely the error back-propagation neural networks, a BP neural network-based method is proposed to predict the interval natural frequencies of structures with uncertain-but-bounded parameters. To demonstrate the proposed method’s feasibility, a numerical example is tested. The lower and upper frequency bounds obtained using the proposed approach are compared with those obtained using the interval-based perturbation method, which is a commonly used method for problems with uncertainties. A Monte Carlo simulation is also conducted because it is always referred to as a reference solution for problems related to uncertainties. It can be observed that as the varying ranges of uncertain parameters become larger, the accuracy of the perturbation method deteriorates remarkably, but the proposed method still maintains a high level of accuracy. This study not only puts forward a novel approach for predicting the interval natural frequencies but also exhibits the broad application prospect of BP neural networks for solving problems with uncertainties.
Back-propagation neural network, Natural frequency, Interval parameter, Perturbation method, Monte Carlo simulation.
For More Details :
https://aircconline.com/csit/papers/vol10/csit100702.pdf
Volume Link :
http://airccse.org/csit/V10N07.html
MULTI-LABEL CLASSIFIER PERFORMANCE EVALUATION WITH CONFUSION MATRIX
Damir Krstinić, Maja Braović, Ljiljana Šerić and Dunja Božić-Štulić
University of Split, Croatia
Confusion matrix is a useful and comprehensive presentation of the classifier performance. It is commonly used in the evaluation of multi-class, single-label classification models, where each data instance can belong to just one class at any given point in time. However, the real world is rarely unambiguous and hard classification of data instance to a single class, i.e. defining its properties with single distinctive feature, is not always possible. For example, an image can contain multiple objects and regions which makes multi-class classification inappropriate to describe its content. Proposed solutions to this set of problems are based on multi-label classification model where each data instance is assigned one or more labels describing its features. While most of the evaluation measures used to evaluate single-label classifier can be adapted to a multi-label classification model, presentation and evaluation of the obtained results using standard confusion matrices cannot be expanded to this case.
In this paper we propose a novel method for the computation of a confusion matrix for multi-label classification. The proposed algorithm overcomes the limitations of the existing approaches in modeling relations between the classifier output and the Ground Truth (i.e. hand-labeled) classification, and due to its versatility can be used in many different research fields.
Classification, multi label classifier, performance evaluation, confusion matrix
For More Details :
https://aircconline.com/csit/papers/vol10/csit100801.pdff
Volume Link :
http://airccse.org/csit/V10N08.html
DEVELOPING E-LEARNING SYSTEM TO SUPPORT MATH IN THAI EDUCATION CURRICULUM (PRIMARY LEVEL)
Piya Techateerawat Engineering Faculty
Thammasat University, Thailand
E-learning is a common tool to support the education in variety of scenarios. As the education content can be prepared by the group of specialists, but the skilled teachers are limited in remote area. Also, the contents in most curriculum are planned to distribute to limited skilled people. The gap of education can be full-filled with E- learning system. However, the conventional E-learning is high cost system and not appropriated for rural area. Also, opensource system is complicated to implement and configure in dedicated curriculum. This research is proposed the customized design of E-learning system for primary Thai education curriculum. As Thai education curriculum was updated in 2017, school and teacher need to update the plan and methodology accordingly. Our research is based on the actual Thai school in Ratchaburi province by using the Android framework. The system is designed and implemented from actual requirements from teachers and students in grade 3. As a result, the result show student involvement and continue using of system in both school and extra hours. The feedback from actual usage also is evaluated.
E-learning, Android, Thai Curriculum
For More Details :
https://aircconline.com/csit/papers/vol10/csit100802.pdf
Volume Link :
http://airccse.org/csit/V10N08.html
AUTOMATED CLASSIFICATION OF BANANA LEAF DISEASES USING AN OPTIMIZED CAPSULE NETWORK MODEL
Bolanle F. Oladejo and Oladejo Olajide Ademola
University of Ibadan, Ibadan, Nigeria
Plant disease detection and classification have undergone successful researches using Convolutional Neural Network (CNN); however, due to the intrinsic inability of max pooling layer in CNN, it fails to capture the pose, view and orientation of images. It also requires large training data and fails to learn the spatial relationship of the features in an object. Thus, Capsule Network (CapsNet) is a novel deep learn- ing model proposed to overcome the shortcomings of CNN. We developed an optimized Capsule Network model for classification problem using banana leaf diseases as a case study. The two dataset classes in- clude Bacterial Wilt and Black Sigatoka, with healthy leaves. The developed model adequately classified the banana bacterial wilt, black sigatoka and healthy leaves with a test accuracy of 95%. Its outper- formed three variants of CNN architectures implemented (a trained CNN model from scratch, LeNet5 and ResNet50) with respect to rotation invariance.
Capsule Network, CNN, Activation function, Deep Learning, Precision Agriculture
For More Details :
https://aircconline.com/csit/papers/vol10/csit100910.pdf
Volume Link :
http://airccse.org/csit/V10N09.html
AN ADAPTIVE UTILIZATION OF CONVOLUTIONAL MATRIX METHODS ON SLICED HIPPOCAMPAL NEURON CELL SEGMENTATION WITH AN APPLICATION INTERFACE
Neeraj Rattehalli1and Ishan Jain2
1Menlo-Atherton High School, Atherton, California, , USA
2Mission San Jose High School, Fremont, California, USA
Current methods of image analysis and segmentation on hippocampal neuron bodies contain excess and unwanted information like unnecessary noise. In order to clearly analyze each neural stain like DAPI, Cy5, TRITC, FITC and start the segmentation process, it is pertinent to preemptively denoise the data and create masked regions that accurately capture the ROI in these hippocampal regions. Unlike traditional edge detection algorithms like the Canny methods available in OpenCv libraries, we employed a more targeted approach based on pixel color intensities to segment out hippocampal neurons from the background. Using the R, G, and B value thresholds, our algorithm checks if a cell is a boundary point by doing neighboring pixel level comparisons. Combined with a seamless GUI interface for cropping the highlighted ROI, the algorithms efficiently work at creating general outlines of neuron bodies. With user modularity from the various thresholding values, the outlining and denoising presents clean data ready for analysis with object detection algorithms like FRCNN and YOLOv3.
Convolutional Matrix, Computer Vision, Machine Learning, Deep Learning, Automation Interface
For More Details :
https://aircconline.com/csit/papers/vol10/csit100911.pdf
Volume Link :
http://airccse.org/csit/V10N09.html
FACIAL EXPRESSION RECOGNITION USING COMBINED PRE-TRAINED CONVNETS
Raid Saabni1,2and Alon Schclar1 1The Academic College of Tel-Aviv Yaffo, Tel-Aviv, Israel 2Traiangle R&D Center, Kafr Qarea, Israel
Automatic Facial Expression Recognition (AFER), has been an active research area in the past three decades. Research and development in this area have become continually active due to its wide range of potential applications in many fields. Recent research in the field presents impressive results when using Convolution Neural Network (CNN's, ConvNets). In general, ConvNets proved to be a very common and promising choice for many computer vision tasks including AFER. Motivated by this fact, we parallelly combine modified versions of three ConvNets to generate an Automated Facial Expression Recognition system. This research aims to present a robust architecture and better learning process for a deep ConvNet. Adding four additional layers to the combination of the basic models assembles the net to one large ConvNet and enables the sophisticated boosting of the basic models. The main contribution of this work comes out of this special architecture and the use of a two-phase training process that enables better learning. The new system we present is trained to detect universal facial expressions of seven\eight basic emotions when targeting the FER2013 and FER2013+ benchmarks, respectively. The presented approach improves the results of the used architectures by 4% using the FER2013 and 2% using FER2013+ data sets. The second round of training the presented system increases the accuracy of some of the basic models by close to 3% while improving the accuracy of the whole net.
Automatic Facial Expression Recognition, Convolutional Neural Networks, Machine Learning, Boosting, Deep Learning
For More Details :
https://aircconline.com/csit/papers/vol10/csit100908.pdf
Volume Link :
http://airccse.org/csit/V10N09.html
FOLLOW THEN FORAGE EXPLORATION: IMPROVING ASYNCHRONOUS ADVANTAGE ACTOR CRITIC
James B. Holliday and T.H. Ngan Le
University of Arkansas, Fayetteville, Arkansas, USA
Combining both value-iteration and policy-gradient, Asynchronous Advantage Actor Critic (A3C) by Google’s DeepMind has successfully optimized deep neural network controllers on multi agents. In this work we propose a novel exploration strategy we call “Follow then Forage Exploration” (FFE) which aims to more effectively train A3C. Different from the original A3C where agents only use entropy as a means of improving exploration, our proposed FFE allows agents to break away from A3C's normal action selection which we call "following" and "forage" which means to explore randomly. The central idea supporting FFE is that forcing random exploration at the right time during a training episode can lead to improved training performance. To compare the performance of our proposed FFE, we used A3C implemented by OpenAI’s Universe-Starter-Agent as baseline. The experimental results have shown that FFE is able to converge faster.
Reinforcement Learning, Multi Agents, Exploration, Asynchronous Advantage Actor Critic, Follow Then Forage
For More Details :
https://aircconline.com/csit/papers/vol10/csit100909.pdf
Volume Link :
http://airccse.org/csit/V10N09.html
CODING WITH LOGISTIC SOFTMAX SPARSE UNITS
Gustavo A. Lado and Enrique C. Segura
Universidad de Buenos Aires, Argentina
This paper presents a new technique for efficient coding of highly dimensional vectors, overcoming the typical drawbacks of classical approaches, both, the type of local representations and those of distributed codifications. The main advantages and disadvantages of these classical approaches are revised and a novel, fully parameterized strategy, is introduced to obtain representations of intermediate levels of locality and sparsity, according to the necessities of the particular problem to deal with. The proposed method, called COLOSSUS (COding with LOgistic Softmax Sparse UnitS) is based on an algorithm that permits a smooth transition between both extreme behaviours -local, distributed- via a parameter that regulates the sparsity of the representation. The activation function is of the logistic type. We propose an appropriate cost function and derive a learning rule which happens to be similar to the Oja's Hebbian learning rule. Experiments are reported showing the efficiency of the proposed technique.
Neural Networks, Sparse Coding, Autoencoders
For More Details :
https://aircconline.com/csit/papers/vol10/csit101019.pdf
Volume Link :
http://airccse.org/csit/V10N10.html
DATA PREDICTION OF DEFLECTION BASIN EVOLUTION OF ASPHALT PAVEMENT STRUCTURE BASED ON MULTI-LEVEL NEURAL NETWORK
Shaosheng Xu1, Jinde Cao2and Xiangnan Liu2
1,2Southeast University, Nanjing, China
Aiming at reducing the high cost of test data collection of deflection basins in the structural design of asphalt pavement and shortening the long test time of new structures, this paper innovatively designs a structure coding network based on traditional neural networks to map the pavement structure to an abstract space. Therefore, the generalization ability of the neural network structure is improved, and a new multi-level neural network model is formed to predict the evolution data of the deflection basin of the untested structure. By testing the experimental data of RIOHTRACK, the network structure predicts the deflection basin data of untested pavement structure, of which the average prediction error is less than 5%.
multi-level neural network, Encoding converter, structural of asphalt pavement, deflection basins, RIOHTRACK
For More Details :
https://aircconline.com/csit/papers/vol10/csit101304.pdf
Volume Link :
http://airccse.org/csit/V10N13.html
STABILITY ANALYSIS OF QUATERNIONVALUED NEURAL NETWORKS WITH LEAKAGE DELAY AND ADDITIVE TIME-VARYING DELAYS
Qun Huang and Jinde Cao
Southeast University, Nanjing, China
In this paper, the stability analysis of quaternion-valued neural networks (QVNNs) with both leakage delay and additive time-varying delays is proposed. By employing the Lyapunov Krasovskii functional method and fully considering the relationship between time-varying delays and upper bounds of delays, some sufficient criteria are derived based on reciprocally convex method and several inequality techniques. The stability criteria are established in two forms: quaternion-valued linear matrix inequalities (QVLMIs) and complex-valued linear matrix inequalities (CVLMIs),in which CVLMIs can be directly resolved by the Yalmip toolbox in MATLAB. Finally, an illustrative example is presented to demonstrate the validity of the theoretical results.
Quaternion-valued Neural Networks, Stability Analysis, Lyapunov-KrasovskiiFunctional, Leakage Delay, Additive Time-varying Delays
For More Details :
https://aircconline.com/csit/papers/vol10/csit101305.pdf
Volume Link :
http://airccse.org/csit/V10N13.html
Data Driven Soft Sensor for Condition Monitoring of Sample Handling System (SHS)
Abhilash Pani, Jinendra Gugaliya and Mekapati Srinivas
Industrial Automation Technology Centre, ABB, Bangalore, India
Gas sample is conditioned using sample handling system (SHS) to remove particulate matter and moisture content before sending it through Continuous Emission Monitoring (CEM) devices. The performance of SHS plays a crucial role in reliable operation of CEMs and therefore, sensor-based condition monitoring systems (CMSs) have been developed for SHSs. As sensor failures impact performance of CMSs, a data driven soft-sensor approach is proposed to improve robustness of CMSs in presence of single sensor failure. The proposed approach uses data of available sensors to estimate true value of a faulty sensor which can be further utilized by CMSs. The proposed approach compares multiple methods and uses support vector regression for development of soft sensors. The paper also considers practical challenges in building those models. Further, the proposed approach is tested on industrial data and the results show that the soft sensor values are in close match with the actual ones.
Sample Handling System, Soft-Sensor, Variance Inflation Factor (VIF), Local Outlier Factor (LOF), Support Vector Regression.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101423.pdf
Volume Link :
http://airccse.org/csit/V10N14.html
FACE RECOGNITION USING PCA INTEGRATED WITH DELAUNAY TRIANGULATION
Kavan Adeshara and Vinayak Elangovan
Division of Science and Engineering, Penn State Abington, PA, USA
Face Recognition is most used for biometric user authentication that identifies a user based on his or her facial features. The system is in high demand, as it is used by many businesses and employed in many devices such as smartphones and surveillance cameras. However, one frequent problem that is still observed in this user-verification method is its accuracy rate. Numerous approaches and algorithms have been experimented to improve the stated flaw of the system. This research develops one such algorithm that utilizes a combination of two different approaches. Using the concepts from Linear Algebra and computational geometry, the research examines the integration of Principal Component Analysis with Delaunay Triangulation; the method triangulates a set of face landmark points and obtains eigenfaces of the provided images. It compares the algorithm with traditional PCA and discusses the inclusion of different face landmark points to deliver an effective recognition rate.
Delaunay Triangulation, PCA, Face Recognition
For More Details :
https://aircconline.com/csit/papers/vol10/csit101424.pdf
Volume Link :
http://airccse.org/csit/V10N14.html
OBSTACLE AVOIDANCE AND PATH FINDING FOR MOBILE ROBOT NAVIGATION
Poojith Kotikalapudi and Vinayak Elangovan
Division of Science and Engineering, Penn State Abington, PA, USA
This paper investigates different methods to detect obstacles ahead of a robot using a camera in the robot, an aerial camera, and an ultrasound sensor. We also explored various efficient path finding methods for the robot to navigate to the target source. Single and multi-iteration anglebased navigation algorithms were developed. The theta-based path finding algorithms were compared with the Dijkstra’s Algorithm and their performance were analyzed.
Image Processing, Path Finding, Obstacle Avoidance, Machine Learning, Robot Navigation
For More Details :
https://aircconline.com/csit/papers/vol10/csit101425.pdf
Volume Link :
http://airccse.org/csit/V10N14.html
USING MACHINE LEARNING IMAGE RECOGNITION FOR CODE REVIEWS
Michael Dorin1,2, Trang Le2, Rajkumar Kolakaluri2and Sergio Montenegro1
1Universität Würzburg, Würzburg, Germany
2Engineering, University of St. Thomas, St. Paul, MN, USA
It is commonly understood that code reviews are a cost-effective way of finding faults early in the development cycle. However, many modern software developers are too busy to do them. Skipping code reviews means a loss of opportunity to detect expensive faults prior to software release. Software engineers can be pushed in many directions and reviewing code is very often considered an undesirable task, especially when time is wasted reviewing programs that are not ready. In this study, we wish to ascertain the potential for using machine learning and image recognition to detect immature software source code prior to a review. We show that it is possible to use machine learning to detect software problems visually and allow code reviews to focus on application details. The results are promising and are an indication that further research could be valuable.
Code Reviews, Machine Learning, Imagine Recognition, Coding Style
For More Details :
https://aircconline.com/csit/papers/vol10/csit101514.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
AN INTELLIGENT AND DATA-DRIVEN MOBILE PLATFORM FOR YOUTH VOLUNTEER MANAGEMENT USING MACHINE LEARNING AND PREDICTIVE ANALYTICS
Alyssa Huang1and Yu Sun2
1Arnold O. Beckman High School, Irvine, CA 92602, USA
2California State Polytechnic University, Pomona, CA, 91768, USA
Volunteering is very important to high school students because it not only allows the teens to apply the knowledge and skills they have acquired to real-life scenarios, but it also enables them to make an association between helping others and their own joy of fulfillment. Choosing the right volunteering opportunities to work on can influence how the teens interact with that cause and how well they can serve the community through their volunteering services. However, high school students who look for volunteer opportunities often do not have enough information about the opportunities around them, so they tend to take whatever opportunity that comes across. On the other hand, as organizations who look for volunteers usually lack effective ways to evaluate and select the volunteers that best fit the jobs, they will just take volunteers on a first-come, firstserve basis. Therefore, there is a need to build a platform that serves as a bridge to connect the volunteers and the organizations that offer volunteer opportunities. In this paper, we focus on creating an intelligent platform that can effectively evaluate volunteer performance and predict best-fit volunteer opportunities by using machine learning algorithms to study 1) the correlation between volunteer profiles (e.g. demographics, preferred jobs, talents, previous volunteering events, etc.) and predictive volunteer performance in specific events and 2) the correlation between volunteer profiles and future volunteer opportunities. Two highest-scoring machine learning algorithms are proposed to make predictions on volunteer performance and event recommendations. We demonstrate that the two highest-scoring algorithms are able to make the best prediction for each query. Alongside the practice with the algorithms, a mobile application, which can run on both iPhone and Android platforms is also created to provide a very convenient and effective way for the volunteers and event supervisors to plan and manage their volunteer activities. As a result of this research, volunteers and organizations that look for volunteers can both benefit from this data-driven platform for a more positive overall experience.nt policy and faster training procedure
Machine learning, Predictive Analytics, Flutter, Volunteer Management, Scikit-learn
For More Details :
https://aircconline.com/csit/papers/vol10/csit101515.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
EXPLAINABLE AI FOR INTERPRETABLE CREDIT SCORING
Lara Marie Demajo, Vince Vella and Alexiei Dingli
University of Malta, Msida, Malta
With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance-based) that are required by different people in different situations. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness.
Credit Scoring, Explainable AI, BRCG, XGBoost, GIRP, SHAP, Anchors, ProtoDash, HELOC, Lending Club
For More Details :
https://aircconline.com/csit/papers/vol10/csit101516.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
A NEW FRAMEWORK OF FEATURE ENGINEERING FOR MACHINE LEARNING IN FINANCIAL FRAUD DETECTION
Chie Ikeda, Karim Ouazzane and Qicheng Yu
London Metropolitan University, London, UK
Financial fraud activities have soared despite the advancement of fraud detection models empowered by machine learning (ML). To address this issue, we propose a new framework of feature engineering for ML models. The framework consists of feature creation that combines feature aggregation and feature transformation, and feature selection that accommodates a variety of ML algorithms. To illustrate the effectiveness of the framework, we conduct an experiment using an actual financial transaction dataset and show that the framework significantly improves the performance of ML fraud detection models. Specifically, all the ML models complemented by a feature set generated from our framework surpass the same models without such a feature set by nearly 40% on the F1-measure and 20% on the Area Under the Curve (AUC) value.
Financial Fraud Detection, Feature Engineering, Feature Creation, Feature Selection, Machine Learning
For More Details :
https://aircconline.com/csit/papers/vol10/csit101517.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
3-D OFFLINE SIGNATURE VERIFICATION WITH CONVOLUTIONAL NEURAL NETWORK
Na Tyrer1, Fan Yang1, Gary C. Barber1, Guangzhi Qu1,Bo Pang1and Bingxu Wang1,2
1Oakland University, Rochester, Michigan, 48309, USA
2Zhejiang Sci-Tech University, Hangzhou, Zhejiang, 310018, P.R.China
Signature verification is essential to prevent the forgery of documents in financial, commercial, and legal settings. There are many researchers have focused on this topic, however, utilizing the 3-D information presented by a signature using a 3D optical profilometer is a relatively new idea, and the convolutional neural network is a powerful tool for image recognition. The present research focused on using the 3 dimensions of offline signatures in combination with a convolutional neural network to verify signatures. It was found that the accuracy of the data for offline signature verification was over 90%, which shows promise for this method as a novel method in signature verification.
Signature Verification, 3D Optical Profilometer, Convolutional Neural Network regression, lr.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101518.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
NEGATIVE SAMPLING IN KNOWLEDGE REPRESENTATION LEARNING: A MINI-REVIEW
Jing Qian1,2, Gangmin Li1, Katie Atkinson2and Yong Yue1
1Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, China
1University of Liverpool, Liverpool, United Kingdom
Knowledge representation learning (KRL) aims at encoding components of a knowledge graph (KG) into a low-dimensional continuous space, which has brought considerable successes in applying deep learning to graph embedding. Most famous KGs contain only positive instances for space efficiency. Typical KRL techniques, especially translational distance-based models, are trained through discriminating positive and negative samples. Thus, negative sampling is unquestionably a non-trivial step in KG embedding. The quality of generated negative samples can directly influence the performance of final knowledge representations in downstream tasks, such as link prediction and triple classification. This review summarizes current negative sampling methods in KRL and we categorize them into three sorts, fixed distribution-based, generative adversarial net (GAN)-based and cluster sampling. Based on this categorization we discuss the most prevalent existing approaches and their characteristics.
Knowledge Representation Learning, Negative Sampling, Generative Adversarial Nets.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101519.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
LOCAL BRANCHING STRATEGY-BASED METHOD FOR THE KNAPSACK PROBLEM WITH SETUP
Samah Boukhari1, Isma Dahmani2and 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
In this paper, we propose to solve the knapsack problem with setups by combining mixed linear relaxation and local branching. The problem with setups can be seen as a generalization of 0–1 knapsack problem, where items belong to disjoint classes (or families) and can be selected only if the corresponding class is activated. The selection of a class involves setup costs and resource consumptions thus affecting both the objective function and the capacity constraint. The mixed linear relaxation can be viewed as driving problem, where it is solved by using a special blackbox 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.
Knapsack, Setups, Local Branching, Relaxation Learning.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101606.pdf
Volume Link :
http://airccse.org/csit/V10N16.html
LINEAR REGRESSION EVALUATION OF SEARCH ENGINE AUTOMATIC SEARCH PERFORMANCE BASED ON HADOOP AND R
Hong Xiong
University of California – Los Angeles, Los Angeles, CA, USA
The automatic search performance of search engines has become an essential part of measuring the difference in user experience. An efficient automatic search system can significantly improve the performance of search engines and increase user traffic. Hadoop has strong data integration and analysis capabilities, while R has excellent statistical capabilities in linear regression. This article will propose a linear regression based on Hadoop and R to quantify the efficiency of the automatic retrieval system. We use R's functional properties to transform the user's search results upon linear correlations. In this way, the final output results have multiple display forms instead of web page preview interfaces. This article provides feasible solutions to the drawbacks of current search engine algorithms lacking once or twice search accuracies and multiple types of search results. We can conduct personalized regression analysis for user’s needs with public datasets and optimize resources integration for most relevant information.
Hadoop, R, search engines, linear regression, machine learning
For More Details :
https://aircconline.com/csit/papers/vol10/csit101607.pdf
Volume Link :
http://airccse.org/csit/V10N16.html
HOW TO ENGAGE FOLLOWERS: CLASSIFYING FASHION BRANDS ACCORDING TO THEIR INSTAGRAM PROFILES, POSTS AND COMMENTS
Stefanie Scholz1and Christian Winkler2
1Wilhem Loehe University of Applied Sciences, Fuerth, Germany
2datanizing GmbH, Schwarzenbruck, Germany
In this article we show how fashion brands communicate with their follower on Instagram. We use a continuously update dataset of 68 brands, more than 300,000 posts and more than 40,000,000 comments.
Starting with descriptive statistics, we uncover different behavior and success of the various brands. It turns out that there are patterns specific to luxury, mass-market and sportswear brands. Posting volume is extremely brand dependent as is the number of comments and the engagement of the community.
Having understood the statistics, we turn to machine learning techniques to measure the response of the community via comments. Topic models help us understand the structure of their respective community and uncover insights regarding the response to campaigns.
Having up-to-date content is essential for this kind of analysis, as the market is highly volatile. Furthermore, automatic data analysis is crucial to measure the success of campaigns and adjust them accordingly for maximum effect.
Instagram, Fashion Brands, Data Extraction, Marketing, Analysis, Artificial Intelligence, Netnography, Descriptive Statistics, Visualization, Community Engagement, Artificial Intelligence, Unsupervised Learning, Topic Modelling
For More Details :
https://aircconline.com/csit/papers/vol10/csit101704.pdf
Volume Link :
http://airccse.org/csit/V10N17.html
REGULARIZATION METHOD FOR RULE REDUCTION IN BELIEF RULE-BASED SYSTEM
Yu Guan
Fuzhou University, Fuzhou, China
Belief rule-based inference system introduces a belief distribution structure into the conventional rule-based system, which can effectively synthesize incomplete and fuzzy information. In order to optimize reasoning efficiency and reduce redundant rules, this paper proposes a rule reduction method based on regularization. This method controls the distribution of rules by setting corresponding regularization penalties in different learning steps and reduces redundant rules. This paper first proposes the use of the Gaussian membership function to optimize the structure and activation process of the belief rule base, and the corresponding regularization penalty construction method. Then, a step-by-step training method is used to set a different objective function for each step to control the distribution of belief rules, and a reduction threshold is set according to the distribution information of the belief rule base to perform rule reduction. Two experiments will be conducted based on the synthetic classification data set and the benchmark classification data set to verify the performance of the reduced belief rule base.
Knowledge-based system, Belief rule base, Regularization method, Rule reduction.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101705.pdf
Volume Link :
http://airccse.org/csit/V10N17.html
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