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In this competitive world, employees often experience stress at work. Stress for a prolonged period of time is converted to chronic stress. This may lead to high blood pressure, damage to muscle tissue, inhibition of growth, suppression of the immune system and damage to mental health. Generally, stress management is subjective to the realization of the person. For a better mental health management, continuous monitoring and objective evaluation of stress is a need. Nowadays, various sensors are used for the same. This paper investigates how new context-aware pervasive systems can support knowledge workers to diminish stress. The focus is on developing an automatic classifier to infer working conditions and stress-related mental states from a multimodal set of sensor data (computer logging, facial expressions, posture, and physiology). Instead of using all the sensor data (149 features), the further focus is on selecting a subset of features, which are most effective in detecting stress using a hybrid filter-wrapper approach for feature selection. As a final note, implementing such a stress detection system in real-world settings brings additional challenges. Not only sensors have to be installed to collect data in the workplace, but also the signals need to be processed, features extracted and analyzed in real time yielding meaningful results. But selecting a set of features makes the task a lot easier and results in higher accuracy and fast processing. Different filter and wrapper methods and their hybrids were analyzed for the problem at hand. Finally, the hybrid of information gain and best first method resulted in a significant reduction in the number of features in the original feature set and an increase in accuracy.

Machine learning, Stress, Feature selection, Hybrid method, Facial expression, Postures, Computer loggings, Physiological, Filter approach, Wrapper approach

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Monika, Anita Sahoo, "A hybrid filter-wrapper feature selection method for stress detection and monitoring among employees at workspaces," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.831-836, 2018.

A soil test may refer to one or more wide variety of soil analyses conducted for one of several possible reasons. Possibly the most widely conducted soil tests are those done to estimate the plant- available concentration of plant nutrients in order to determine fertilizer recommendations in agriculture. Soil testing is used to facilitate fertilizer composition and dosage selection for land employed in both agriculture industries. Automated soil testing device is an electronic device which can be used to measure soil moisture, soil pH values to ensure the fertility of the soil in the field of agriculture to select the suitable crop using Arduino.

Soil Moisture, Soil pH, Arduino, Agriculture and Fertilizers.

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Balakrishna K, Mahesh Rao, Anupama K P, Chaitra B, Pooja L, "Automatic testing of Soil Moisture, pH using Arduino and Selection of Specific Crop," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.837-841, 2018.

Occupational stress can impact our lives in several aspects. This affects employee’s health, causes absenteeism and overall performance of an organization affected. World Health Organization (WHO) identifies it as epidemics for the modern life. The insurance sector employees have direct customer interaction. The policies and the services introduced to the new customers, convincing the ideas and satisfying the divergent customer needs causes more pressure on the employees which leads to higher level of stress. Occupational stress data mining is an emerging stream which helps in mining stressed data for solving various types of problem. One of the problems is to know the impact of role overload and role ambiguity on occupational stress. In this paper, we have tried to implement a model using machine learning classification techniques for the prediction of Occupational stress related to insurance sector personnel. In this paper, we have applied support vector machine (SVM), Neural network (NN), decision tree (DT) and random forest (RF). The training and testing are done through a stratified tenfold cross-validation. The proposed model obtained an accuracy of 60%, a sensitivity of 80%, and specificity 60%. The evaluation of occupational stress is critically connected to job performance in the organization. So it is essential to identify the causes of occupational stress and can be reduced to the possible extent with the help of proper management techniques.

Occupational Stress, Distress, Predictive model, Classification techniques, SVM, NN, DT, RF

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S.K. Yadav, Arshad Hashmi, "An Investigation of Occupational stress Classification by using Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.842-850, 2018.

Growing competition over the years has seen an increase in getting vital information like customer behaviour, his likes and dislikes before launching a product. Extracting the information from a huge pool of data like internet is what we in technical terms know as Web Mining (WM). With the technology comes the challenges too and getting correct information from a very large pool of data is always a big task. Traditionally WM uses content, structure and usage mining techniques but still the user sometime is not able to retrieve what he is looking for. Proper filtering of the information retrieved in the form of text or in other words text mining could make a lot of difference between correct information and lot of information. The paper focuses on digging the web to create a comprehensive repository for web miners looking for e-learning. 2000 URLs related with different online learning were taken into consideration, the information was read using python and raw text was collected. Python’s punctuation and itemgetter modules were used to retain only the major keywords having counts over a threshold, after performing basic text mining techniques. To check the robustness of the retained data precision, recall and accuracy was calculated and it was found that the precision, recall and accuracy were 0.964, 0.982 and 0.97 respectively.

Web Mining, Text mining, E-learning

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Kamika Chaudhary, Neena Gupta, "Preprocessing and Classifying Web Text Data for E-learning Recommendation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.851-857, 2018.

A single major injury suffered by a miner (or miner’s death) changes the lives of many people connected with the miner such as his/her relatives, friends, neighbours, colleagues etc. Moreover, it creates panic among the co – miners and demotivates them and hence, can result in reducing the dedication, strength, skills etc., among the co - miners. Concerning the above scenario, safety is considered as among the top priorities in many of the industries but when it comes to coal mining, the safety of the workers should be assigned highest priority as even a little negligence can lead to major disasters (such as in the cases of fire, methane explosions, fall of roof, spontaneous heating of coal etc.) in the Coal Mines (CMs). The work presented here addresses the safety issues related with the coal mines by carrying out enhanced due diligence on the past accidents and their causes in the context of coal mining in India and forms a set of recommendations to be followed by the miners and the mining safety authorities in order to ensure the safe return of a father to his son/daughter, a husband to his wife, a wife to her husband, a friend to a friend, a son/daughter to his/her father/mother etc.

CM safety; disasters; causes of accidents; recommendations; safety at work

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Mohd Shirazuddin, Syed Musthak Ahmed, Anupama Deshpande, "A Meticulous Study on Improving UCM’s Safety: Analysing Causes of Accidents and Suggesting Recommendations," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.858-862, 2018.

With the expansion of credit business, the prediction models for taking decision of credit permissions with least risk are becoming more and more admired by banking sectors. The use of Machine Learning (ML) based models has confirmed to be of practical value in resolving a range of banking risk prediction problems. The model for Credit risk prediction seeks to predict feature factors, whether an individual is bad or good applicant for loan or not. Such problems can be better solved using ML. Also, Ensemble classifiers in ML play a key role in prediction problems. The use of Ensemble Methods (EMs) for classification is among the recent areas of research in ML. Many recent researches specify that EMs lead to a major improvement in classification performance by choosing suitable class. For this work, several ML techniques are explored and evaluated on real credit card datasets. Most ML methods have achieved an accuracy of less than 80 percent. Predictive model for Credit Risk Detection based on ensemble technique is proposed. The proposed model is evaluated on basis of various performance metrics and comparison is done with base classifier (learner) resulted in 81 percent prediction accuracy and better correlation coefficient.

Predictive Modelling, Machine Ensemble Method, Credit Risk, Data Mining

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Anand Motwani, Goldi Bajaj, Sushila Mohane, "Predictive Modelling for Credit Risk Detection using Ensemble Method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.863-867, 2018.

WSN is a wireless sensor network. It is the multi-hop network where large numbers of sensor nodes are connected together by wireless medium. WSN network used in various application like Military, Whether Detection, Agriculture etc. In this network data transfer occurs through wireless medium so we always need an efficient security scheme for this type of network which provides better security. WSN is a network contains low power sensor nodes so we need an efficient scheme which requires less power. In this security scheme implementation key management phase plays an important role because security of any scheme depends on key security. Many Key Management Scheme proposed in previous years like LEAP, PANJA, SEHKM etc. We explored these schemes and proposed a scheme for general purpose sensor networks which provide region able security level with less time requirement. The performance is measure in term of time requirement.

Wireless Sensor Network [WSN], Key Management

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[7]. Sencun Zhu, Sanjeev Setia and Sushil Jajodia, LEAP+: Efficient security mechanisms for large-scale distributed sensor networks. ACM Transactions on Sensor Networks2: 500-528.
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K. Derashri, N. Chaudhary, "An Efficient Key Management Scheme For Secure WSN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.868-873, 2018.

In this paper, we compute the Harary energy of grid, cylinder, torus, extended grid networks by using MATLAB code. Also we obtained Milovanovi`c bounds for Harary energy of a graph.

MATLAB code, Harary energy, Grid, Cylinder, Torus, Extended grid

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Pradeep Kumar R, Soner Nandappa D, M.R. Rajesh Kanna, "MATLAB Program to Generate Harary energy of Certain Mesh Derived Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.874-897, 2018.

The purpose of this paper is to present a numerical analysis of an unsteady three dimensional MHD flow of dusty fluid past an infinite inclined porous plate. The Thermal diffusion (Soret), Diffusion thermo (Dufour) and radiation effects on natural convection heat and mass transfer of viscoelastic fluid over a fixed inclined porous plate are presented. The governing non-linear partial differential equations are transformed into a system of partial differential equations using similarity transformations. After transformation the resulting equations are then solved numerically by the use of Crank-Nicolson implicit finite difference method. Profiles of dimensionless velocity, temperature and concentration are shown graphically for various values physical parameter like Prandtl number , Schmidt number , magnetic parameter , Hall parameter , Soret number, Dufour number, Viscoelastic parameter radiation parameter, time , permeability parameter , dusty fluid parameter , dust particle parameter , thermal Grashof number , solutal Grashof number , inclination angle . Skin friction coefficient, Nusselt number and Sherwood number are discussed with help of tables.

Free convection, MHD flow, Dusty fluid, Viscoelastic fluid, Radiation effect, Heat and Mass transfer, Soret-Dufour effects, Crank-Nicolson finite difference method

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N. Pandya and R. K. Yadav, "Numerical Simulation of Soret-Dufour and Radiation effects on Unsteady MHD flow of Viscoelastic Dusty fluid over Inclined Porous Plate," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.898-908, 2018.

Convolutional Neural Networks (CNNs) have been confirmed as a powerful technique for classification of visual inputs like handwritten digits and faces recognition. Hindi handwritten character recognition (HHCR) is one of the challenging issues in machine vision. This study aims to investigate the performance of Convolutional neural networks (CNNs) on HHCR problems. To investigate the performance of different CNNs, a dataset of Hindi handwritten characters has been used as ground truth data. Different optimizers have been implemented on different parameters to determine the test accuracy of the proposed architecture.

Convolutional neural network, Handwritten character recognition, Deep learning, Hindi character dataset

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[15] Samad Roohi, Behnam Alizadehashrafi, “Persian Handwritten Character Recognition Using Convolutional Neural Network,” 10th Iranian Conference on Machine Vision and Image Processing, Nov, 22-23, 2017.
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[17] Jia xiaodong, gong wednog, yuan jie, “Handwritten Yi Character Recognition with Density Based Clustering Algorithm and Convolutional Neural Network”, IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference Embedded and Ubiquitous Computing (EUC) 2017.
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[19] Ajay Indian, Karamjit Bhatia, “A combination of feature extraction for offline handwritten hindi numerals recognition”, vol-6, Issue-5, May 2018

Karishma Verma, Manjeet Singh, "Hindi Handwritten Character Recognition using Convolutional Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.909-914, 2018.