A Comparative Study on the Prediction of Fake Job Posts using Various Data Mining Techniques

In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different datamining techniques and classification algorithm like KNN, decision tree, support vector machine, naive bayes classifier, random forest classifier , multi-layer perceptron and deep neural network to predict a job post if it is real or fraudulent. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing18000 samples. Deep neural network as a classifier, performs great for this classification task. We have used three dense layers for this deep neural network classifier. The trained classifier shows approximately 98% classification accuracy (DNN) to predict a fraudulent job post. Index Terms--false job prediction, deep learning, data mining.</jats:p

Emotion-net: Automatic emotion recognition system using optimal feature selection-based hidden markov CNN model

Emotion recognition plays a crucial role in understanding human behaviour and improving human–computer interaction. However, the conventional methods were failed to detect the multiple emotions from image, speech, and video data. So, this study proposes a comprehensive approach for emotion recognition from multiple modalities, including face, speech, and video data, which is named as Emotion-Net. In the preprocessing stage, the input data from face, speech, and video sources is preprocessed to enhance the quality and consistency of the information. Various techniques such as noise removal, normalization, and alignment are applied to ensure reliable feature extraction. Then, the Histogram of Oriented Gradients (HOG) technique is employed to capture important visual patterns and spatial information from facial images, video frames, and speech data. It provides a compact and descriptive representation of facial features, enabling effective emotion recognition. To select the most informative and discriminative features, Effective Seeker Optimization Algorithm (ESOA) feature selection algorithm is employed. The ESOA used to select the best features from HOG using similarity selection process. Finally, the Hidden Markov Convolutional Neural Network (HMCNN) classifier is utilized for emotion recognition. This classifier combines the strengths of Hidden Markov Models (HMMs) and CNNs, allowing the model to capture temporal dynamics and spatial dependencies in the input ESOA features. Experimental evaluations show that, the proposed Emotion-Net achieves high performance in recognizing different emotions from face, speech, and video data

Numerical simulation of fibrous biomaterials with randomly distributed fiber network structure

This paper presents a computational framework to simulate the mechanical behavior of fibrous biomaterials with randomly distributed fiber networks. A random walk algorithm is implemented to generate the synthetic fiber network in 2D used in simulations. The embedded fiber approach is then adopted to model the fibers as embedded truss elements in the ground matrix, which is essentially equivalent to the affine fiber kinematics. The fiber-matrix interaction is partially considered in the sense that the two material components deform together, but no relative movement is considered. A variational approach is carried out to derive the element residual and stiffness matrices for finite element method (FEM), in which material and geometric nonlinearities are both included. Using a data structure proposed to record the network geometric information, the fiber network is directly incorporated into the FEM simulation without significantly increasing the computational cost. A mesh sensitivity analysis is conducted to show the influence of mesh size on various simulation results. The proposed method can be easily combined with Monte Carlo (MC) simulations to include the influence of the stochastic nature of the network and capture the material behavior in an average sense. The computational framework proposed in this work goes midway between homogenizing the fiber network into the surrounding matrix and accounting for the fully coupled fiber-matrix interaction at the segment length scale, and can be used to study the connection between the microscopic structure and the macro-mechanical behavior of fibrous biomaterials with a reasonable computational cost