Research on Emotion Classification Based on Multi-modal Fusion

في الوقت الحاضر، لم يعد تعبير الأشخاص على الإنترنت يقتصر على النصوص، خاصة مع ظهور طفرة الفيديو القصير، مما أدى إلى ظهور عدد كبير من البيانات النموذجية مثل النصوص والصور والصوت والفيديو. بالمقارنة مع بيانات الوضع الفردي، تحتوي البيانات متعددة الوسائط دائمًا على معلومات ضخمة. يمكن أن تساعد عملية التنقيب في المعلومات متعددة الوسائط أجهزة الكمبيوتر على فهم الخصائص العاطفية البشرية بشكل أفضل. ومع ذلك، نظرًا لأن البيانات متعددة الوسائط تُظهر ميزات سلسلة زمنية ديناميكية واضحة، فمن الضروري حل مشكلة الارتباط الديناميكي داخل وضع واحد وبين أوضاع مختلفة في نفس مشهد التطبيق أثناء عملية الدمج. لحل هذه المشكلة، في هذا البحث، تم إنشاء إطار استخراج ميزة للتوسع الديناميكي ثلاثي الأبعاد بناءً على البيانات المشتركة متعددة الوسائط، على سبيل المثال الفيديو والصوت والنص. إطار عمل مطابق يعتمد على تحسين الميزات المكانية والزمانية، على التوالي لحل الارتباط الديناميكي داخل الأوضاع وفيما بينها، ومن ثم نمذجة معلومات الارتباط الديناميكي قصيرة وطويلة المدى بين الأوضاع المختلفة بناءً على الإطار المقترح. تُظهر التجارب الجماعية المتعددة التي تم إجراؤها على مجموعات بيانات MOSI  أن نموذج التعرف على المشاعر الذي تم إنشاؤه بناءً على الإطار المقترح هنا في هذه الدراسة يمكنه الاستفادة بشكل أفضل من المعلومات التكميلية الأكثر تعقيدًا بين البيانات المشروطة المختلفة. بالمقارنة مع نماذج دمج البيانات متعددة الوسائط الأخرى، فإن إطار دمج البيانات متعدد الوسائط القائم على الاهتمام المكاني والزماني المقترح في هذه الورقة يحسن بشكل كبير معدل التعرف على المشاعر ودقتها عند تطبيقها على تحليل المشاعر متعدد الوسائط، لذلك فهو أكثر جدوى وفعالية.Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of the three-dimensional dynamic expansion is established based on the common multi-modal data, for example video , sound ,text.Based on the framework, a multi-modal fusion-matched framework based on spatial and temporal feature enhancement, respectively to solve the dynamic correlation within and between modes, and then model the short and long term dynamic correlation information between different modes based on the proposed framework. Multiple group experiments performed on MOSI datasets show that the emotion recognition model constructed based on the framework proposed here in this paper can better utilize the more complex complementary information between different modal data. Compared with other multi-modal data fusion models, the spatial-temporal attention-based multimodal data fusion framework proposed in this paper significantly improves the emotion recognition rate and accuracy when applied to multi-modal emotion analysis, so it is more feasible and effective

HYBRID FLOWER POLLINATION ALGORITHM AND SUPPORT VECTOR MACHINE FOR BREAST CANCER CLASSIFICATION

Microarray technology is a system that enable experts to examine gene profile at molecular level for early disease detection. Machine learning algorithms such as classification are used in detection of dieses from data generated by microarray. It increases the potentials of classification and diagnosis of many diseases such as cancer at gene expression level. Though, numerous difficulties may affect the performance of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data preprocessing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper proposed a new technique for feature selection and classification of breast cancer based on Flower Pollination algorithm (FPA) and Support Vector machine (SVM) using microarray data. The result for this research reveals that FPA-SVM is promising by outperforming the state of the earth Particle Swam Optimization algorithm with 80.11% accuracy. Â

Road traffic crash severity classification using support vector machine

Road traffic crash (RTC) is considered among the leading cause of death in many countries in the world and gives negative impact to the social and economic progress. In Nigeria, 13,583 RTC cases were reported in the year 2013 and this figure rising rapidly. Prediction on injuries severity and analysis on accident contributory factors is vital in order to improve either the road condition or the road safety regulation in attempt to reduce fatalities due to RTC. In this paper, a support vector machine model is developed to predict the road crash severity injuries using human, environment and vehicle contributory factors

A Modelling of Genetic Algorithm for Inventory Routing Problem Simulation Optimisation

This paper presents the simulation optimization modelling for Inventory Routing Problem (IRP) using Genetic Algorithm method. The IRP simulation model is based on the stochastic periodic Can-Deliver policy that allows early replenishment for the retailers who have reached the can-deliver level and consolidates the delivery with other retailers that have reached or fallen below the must-deliver level. The Genetic Algorithm is integrated into the IRP simulation model as optimizer in effort to determine the optimal inventory control parameters that minimized the total cost. This study implemented a Taguchi Method for the experimental design to evaluate the GA performance for different combination of population and mutation rate and to determine the best parameters setting for GA with respect to the computational time and best generation number on determining the optimal inventory control. The result shows that the variations of the mutation rate parameter significantly affect the performance of IRP model compared to population size at 95% confidence level. The implementation of elite preservation during the mutation stage is able to improve the performance of GA by keeping the best solution and used for generating the next population. The results indicated that the best generation number is obtained at GA configuration settings on large population sizes (100) with low mutation rates(0.08). The study also affirms the premature convergence problem faced in GA that required improvement by integrating with the neighbourhood search approach

Identification of sharp edge non-slender delta wing aerodynamic coefficient using neural network

Delta wing formed a vortical flow on its surface which produced higher lift compared to conventional wing. The vortical flow is complex and non-linear which requires more studies to understand its flow physics. However, conventional flow analysis (wind tunnel test and computational flow dynamic) comes with several significant drawbacks. In recent times, application of neural network as alternative to conventional flow analysis has increased. This study is about utilization of Multi-Layer Perceptron (MLP) neural network to predict the coefficient of pressure (Cp) on a delta wing model. The physical model that was used is a sharp edge non-slender delta wing. The training data was taken from wind tunnel tests. 70% of data is used as training, 15% is used as validation and another 15% is used as test set. The wind tunnel test was done at angle of attack from 0°-18° with increment of 3°. The flow velocity was set at 25m/s which correspond to 800,000 Reynolds number. The inputs are angle of attack and location of pressure tube (y/cr) while the output is Cp. The MLP models were fitted with 3 different transfer functions (linear, sigmoid, and tanh) and trained with Lavenberg-Marquadt backpropagation algorithm. The results of the models were compared to determine the best performing model. Results show that large amount of data is required to produce accurate prediction model because the model suffer from condition called overfitting

Multi-objective planning using linear programming

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Research on Emotion Classification Based on Multi-modal Fusion

Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of the three-dimensional dynamic expansion is established based on the common multi-modal data, for example video , sound ,text.Based on the framework, a multi-modal fusion-matched framework based on spatial and temporal feature enhancement, respectively to solve the dynamic correlation within and between modes, and then model the short and long term dynamic correlation information between different modes based on the proposed framework. Multiple group experiments performed on MOSI datasets show that the emotion recognition model constructed based on the framework proposed here in this paper can better utilize the more complex complementary information between different modal data. Compared with other multi-modal data fusion models, the spatial-temporal attention-based multimodal data fusion framework proposed in this paper significantly improves the emotion recognition rate and accuracy when applied to multi-modal emotion analysis, so it is more feasible and effective

Review on scheduling techniques of preventive maintenance activities of railway

Maintenance is vital in any service/industrial organizations as it could prevent unexpected breakdown of equipment's that may result in unexpected cost associated with productivity and quality of services or products. Maintenance is very expensive, therefore an effective maintenance strategies and optimal maintenance schedule are required to reduce the overall maintenance budget cost without reducing the maintenance itself and neglecting the serviceability level of the equipment's/machines. This study will investigate state-of-art the of preventive maintenance scheduling algorithm and provide an optimal schedule for the maintenance activities that aims to minimize the overall maintenance cost and optimize make span of preventive maintenance activities. This case study takes few examples of countries that applied preventive maintenance scheduling in railway. This paper will discuss four types of scheduling techniques which are used in solving preventive maintenance scheduling problem

Aerodynamic characteristics and laminar bubble separation study on a generic light aircraft model

This paper presents the aerodynamic characteristics, flow separation and laminar bubble analysis of a generic UTM-LST light aircraft model at low Reynolds number. The complex interaction between flow separation and laminar bubble is unclear to date. The model has overall length of 1.3m and wingspan of 1.5m and has been designed for wind tunnel experiments in Universiti Teknologi Malaysia Low Speed Wind Tunnel, Aerolab. The aircraft model is equipped with several control surfaces such as ailerons, rudder, elevators and flaps. The experiments were conducted at the speed of 35 m/s corresponding to Reynolds number of 0.515 x 106 and at angles of attack ranging from 0° to 16°. The experiments were performed at several pitching and yawing configurations. In order to investigate the effects of control surfaces, several control surfaces were changed during the experiments; for this paper, however, only elevator changes will be highlighted. Three measurement techniques were employed during the experiments; the first one was the Steady balance, the second was the surface pressure while the last one was the tuft flow experiment. The main observation from steady balance data was that the aircraft possesses longitudinal static stability for all test cases. The main observation from the surface pressure measurement and tuft experiments is that the laminar bubble separation occurred at lower angles of attack of the wing. This separation is seen to be travelling towards the leading edge as the angle of attack is increased and eventually results in flow separation

Enhancing extreme learning machines using cross-entropy moth-flame optimization algorithm

Extreme Learning Machines (ELM) learn fast and eliminate the tuning of input weights and biases. However, ELM does not guarantee the optimal setting of the weights and biases due to random input parameters initialization. Therefore, ELM suffers from instability of output, large network size, and degrade generalization performance. To overcome these problems, an efficient co-evolutionary hybrid model namely as Cross-Entropy Moth-Flame Optimization (CEMFO-ELM) model is proposed to train a neural network for the selection of optimal input weights and biases. The hybrid model balanced the exploration and exploitation of the search space, and then selected optimal input weights and biases for ELM. The co-evolutionary algorithm reduced the chances of been trapped into the local extremum in the search space. Accuracy, stability, and percentage improvement ratio (PIR%) were the metrics used to evaluate the performance of the proposed model when simulated on some classification datasets for machine learning from the University of California, Irvine repository. The co-evolutionary scheme was compared with its constituent individual ELM-based enhanced meta-heuristic schemes (CE-ELM and MFO-ELM). The co-evolutionary meta-heuristic algorithm enhances the selection of optimal parameters for ELM. It improves the accuracy of ELM in all the simulations, and the stability of ELM was improved in all, up to 53% in Breast cancer simulation. Also, it has better convergences than the comparative ELM hybrid model in all the simulations