International Journal of Industrial Engineering and Productional Research
Volume:31 Issue: 3, Sep 2020

The Today’s interconnected world generates huge digital data, while millions of users share their opinions, feelings on various topics through popular applications such as social media, different micro blogging sites, and various review sites on every day. Nowadays Sentiment Analysis on Twitter Data which is considered as a very important problem particularly for various organizations or companies who want to know the customers feelings and opinions about their products and services. Because of the data nature, variety and enormous size, it is very practical for several applications, range from choice and decision creation to product assessment. Tweets are being used to convey the sentiment of a tweeter on a specific topic. Those companies keeping survey millions of tweets on some kind of subjects to evaluate actual opinion and to know the customer feelings. This paper major goal would be to significantly collect, recognize, filter, reduce and analyze all such relevant opinions, emotions, and feelings of people on different product or service could be categorized into positive, negative or neutral because such categorization improves sales growth about a companychr('39')s products or films, etc. We initiate that the Naïve Bayes classifier be the mainly utilized machine learning method for mining feelings from large data like twitter and popular social network because of its more accuracy rates. In this paper, we scrutinize sentiment polarity analysis on Twitter data in a distributed environment, known as Apache Spark.

Todaychr('39')s digital world demands about automated sentiment analysis on visual and text content to significantly displaying peoplechr('39')s feelings, opinions and emotions through text, images and videos across popular social networks. Earlier visual sentimental analysis faces many drawbacks like achieve low accuracy and more difficult to understand people opinions due to traditional techniques. Also, another major challenge is a huge number of images generated and uploaded every day across the world. This paper overcomes problems of visual sentiment analysis with the help of deep learning convolution neural network (CNN) and Affective Regions approach to achieve more meaningful sentiment reports with huge accuracy.

With the ever-increasing request for speed and the increasing number of Cyber Attacks are having fast and accurate skill to provide verification that is convenient, rapid and exact. Even though possible that it is very difficult to fool Image Recognition Skill in this makes it helpful in serving preclude fraud. In this paper, we propose a model for pixel wise operations, which is needed for identification of a location point.  The computer vision is not limited to pixel wise operations. It can be complex and far more complex than image processing. Initially, we take the unstructured Image Segmentation with the help of K-Means Clustering Algorithm is used. Once complete the preprocessing step then extracts the segmented image from the surveillance cameras to identify the expressions and vehicle images. In the raw image from the surveillance camera is the image of a person and vehicle is to classify with the help DWT. Further, the images of the appearances stood also taken with phenomenon called Smart Selfie Click (SSC). These two features are extracted in-order to identify whether the vehicle should be permitted into the campus or not. Thus, verification is possible. These two images are nothing but reliable object extracted for location identification.

In the currentworld the best modefor transport is two wheeler transports. But huge risk is involved because of having protection at lower level. This paper proposed an approach for automatically detecting whether the rider wear helmet or not with any manual intervention. If the rider of the bike identified not wearing helmet, then corresponding two wheeler number plate was read and noted. Data records of every offender who were not wore helmet will be automatically saved into the database A database will be generated with records to identify every offender accurately. This paper aims to explain automatic detection of motor cyclists without helmet and sending the messages to detected persons. Our proposed system is to extract the vehicle number from the RTO website using number plate of a vehicle by using the captured images. After getting the information use mobile number as a input to our model using this mobile number sending the fine details to the detected person. Our system maintains a database for number of detections of that particular vehicle numbers and also sending the detecting location details.

Cancer is a consortium of diseases which comprises abnormal increase in cells growth by having potential to occupy and attack the entire body. According to study breast cancer is the most likely occurs in the women and which became the second biggest cause of women death. Due to its wide spread and importance some of the researchers work on this, but still there is a need to improvement. During this work in order to partially fulfill this proposed technique of deep learning along with RNN in predicting breast cancer disease which will help the doctor while diagnosis the patient. To assess the efficiency of the proposed method we used breast cancer data belong to UC Irvine repository. Precision, recall, accuracy and f1 score of proposed method shows good scores and proposed technique performs well Consortium

Todays, the variety of new products will raise the competition between manufacturers. Product portfolio management (PPM) as a suitable tool can influence the customer’s taste and increase the profit of firms. In this paper, the factors of PPM, production planning and a two-player continuous game theory are considered simultaneously. Some constraints are also assumed such as the availability of raw materials and the demand of each product based on some criteria. Two firms have same offered products and compete with each other. The relationships between two producers will be modeled by a non-zero two- player game. A numerical example is presented too. The proposed model is single period that the inventory is equal to zero in the start and finish of period. The objective functions show the profit of products and the constraints are included the utility of products for each customer, the marketchr('39')s share as a function of the probability of customer selection for each section, the type of distribution function for sale quantity, the accessible quantity of the sum of used materials by two producers and etc.The results shows that demand changing effects on the profit of two players, but effects more on the second player. Also the sale price changing effects on the profit of two players, but effects more on the first player. The obtained data shows that if extra sale price increase the profit of first player will increase while the profit of second player is constant approximately.

In this study, the electrochemical machining (ECM) of the 304 stainless steel with the response surface methodology (RSM) approach for designing, analyzing and mathematical modeling was used. The electrolyte type, concentration and current parameters were considered as the machining parameters. The mathematical model for the responses was presented and based on the type of electrolyte including NaCl, NaNO3 and KCl. The results showed that the current has the highest effect on Surface Roughness (SR) and Material Removal Rates (MRR) and respectively it improves them to 0.465μm and 0.425gr/min. The electrolyte concentration has the highest effect on Over Cut (OC) and causes to increase its values. Under the conditions of NaCl electrolyte, 1 molarity concentration and 55 A current, the optimum condition 0.4006 gr/min MRR, 0.75 mm OC and 0.465mm SR was achieved.

Laser marking is an advanced technology in material processing that has a permanent effect on materials. With the use of laser engraving, the material is removed, layer by layer, in the laser path through melting displacement, ablation, and evaporation. Al-SiC is a metal matrix composite, widely used in aerospace, automobile manufacturing, and electronic packaging. Accumulative roll bonding (ARB) is one of the newest manufacturing processes of metal matrix composites, which leads to the production of materials with high strength, low weight, and great environmental compatibility. In this paper, we present the laser engraving of Al-SiC composite samples, which are produced through ARB process, using Q-switched Nd:YAG laser. A 2k factorial design is used to analyze the effect of factors, including assistant gas flow, distance of sample from beam focus location (distance), pulse repetition frequency, and pumping current on the qualitative characteristics of engraved zone (width, depth and contrast of engraved zone). Desirability function is used for optimization. Results based on experimental data indicate the optimal setting of input factors which leads to pre-specified target values of responses.

In this paper, a novel data driven approach for improving the performance of wastewater management and pumping system is proposed, which is getting knowledge from data mining methods as the input parameters of optimization problem to be solved in nonlinear programming environment. As the first step, we used CART classifier decision tree to classify the operation mode -number of active pumps- based on the historical data of the Austin-Texas infrastructure. Then SOM is applied for clustering customers and selecting the most important features that might have effect on consumption pattern. Furthermore, the extracted features will be fed to Levenberg-Marquardt (LM) neural network which will predict the required outflow rate of the period for each operation mode, classified by CART. The result show that F-measure of the prediction is 90%, 88%, 84% for each operation mode 1,2,3, respectively. Finally, the nonlinear optimization problem is developed based on the data and features extracted from previous steps, and it is solved by artificial immune algorithm. We have compared the result of the optimization model with observed data, and it shows that our model can save up to 2%-8% of outflow rate and wastewater, which is significant improvement in the performance of pumping system.

Today due to the globalization and competitive conditions of the market, decisions are generally made in group and in accordance with different attributes. In addition, all of the information is associated with uncertainty. In such situation, the emergence of inconsistency and facing with the contradictions will be obvious. Having regarded this fact, the development and application of tools that adequately address the uncertainty in decision making process and also be appropriate for group decision making is an important area of multi-criteria decision making (MCDM). Therefore, in this paper, firstly we developed the traditional best-worst method (BWM) and proposed an interval-valued intuitionistic fuzzy best-worst method (IVIFBWM), then introduced a novel approach for fuzzy multi-attribute group decision making based on the proposed method. Finally, in order to demonstrate how the introduced approach can be applied in practice, it is implemented in an Iranian investment company and the experimental results are examined. From the experimental results, we can extract that not only the introduced approach is simple in calculation but also it is convenient in implementation especially in interval-valued intuitionistic fuzzy environments.

In this paper, a new five-parameter distribution is proposed that is called MarshallOlkin Gompertz Makeham distribution(MOGM). This new model is applicable in analysis lifetime data, engineering and actuarial. In this research, some properties of the new model such as mode, moment, Reyni entropy, Tsallis entropy, quantile function and the hazard rate function which is decreasing and unimodal, are studied. The unknown parameters of the MOGM distribution are estimated using the maximum likelihood and Bayes methods. Then these methods are compared using Monte Carlo simulation and the best estimator is proposed. Finally, applications of the proposed model are illustrated to show its usefulness.

Retailers selling fresh products often encounter unsold inventory remains at the end of each period. The leftover product has a lower perceived quality than the new product. Therefore, retailers try to influence consumers’ preferences through price differentiation that leads to an internal competition based on product age and prices. This paper addresses the pricing and inventory control problem for fresh products to capture the influence of this competition on the supply chain members’ decisions and profits. A new coordination model based on a return policy with the revenue and cost-sharing contract is developed to improve the profits of independent supply chain members. The supply chain consists of one supplier and one retailer, where consumers are sensitive to the product’s retail price and freshness degree. Firstly, the retailer’s optimal decisions are derived in a decentralized decision-making structure. Then a centralized approach is used to optimize the supply chain decisions from the whole supply chain viewpoint. Eventually, a new coordination contract is designed to convince the members to participate in the coordination model. Numerical examples are carried out to compare the performance of different decision-making approaches. Our findings indicate that the proposed contract can coordinate the supply chain effectively. Furthermore, the coordinated decision-making model is more profitable and beneficial for the whole supply chain compared to the decentralized one. The results also demonstrate that when consumers are more sensitive to freshness, the simultaneous sale of multiple-aged products at different prices is more profitable.