brahma shukla

Production scheduling is a branch of operational research that uses discrete approaches to address a combinational optimization problem. This broad category includes a wide range of issues such as truck routing, bin packing, and work prioritization. Operational research uses two primary ideas to address these issues: precise techniques, which offer the absolute best answer but only solve minor problems, and approximate approaches, which provide just a decent solution but solve problems that are close to real life scale. The second group of approaches includes heuristics, which are problem-specific procedures, and met heuristics, which are more general methods. Many of these met heuristic approaches, such as Genetic Algorithm, Neural Network, and Fuzzy Logic, have dominated the literature on production scheduling over the past two decades. This study reveals that only a few studies have compared heuristic methods for scheduling problems. Scholars must concentrate on evolutionary manu...

In the current situation, modern engineering and industrial built-up units are encountering a jumble of issues in a variety of areas, including machining time, electricity, manpower, raw materials, and client restraints. One of the most important industrial behaviors, particularly in manufacturing planning, is job-shop scheduling. This study provides a new updated suggested approach of johnson's algorithm as well as the gupta's heuristic algorithm to solve the permutation flow shop sequencing problem with the goal of making the makespan as little as possible. This work is about determining the processing order of n tasks in m machines. Although, because the problem is np-hard for three or more computers, this results in a near-optimal solution to the given issue. The suggested approach is straightforward and easy to comprehend, and it is accompanied with a numerical example.

In a manufacturing industries, scheduling process in production plays a vital role in different aspects like in reducing the cost of product, productivity to be increased, satisfaction of customer and competition in the market. Proper scheduling provides and leads to proper utilization of parameters like available goods, manpower, machinery and sufficiently reaches the satisfaction of customer and also demand of product. There are different systems of production scheduling process includes flow shop where the jobs are processed through series of machines to reach the final product. To deliver the product in time Multi objective scheduling system is necessary to introduced to process where all machines work and all jobs work in a parallel manner. The SDST (sequence dependent setup time) is critical schedule task used to improve the scheduling process in the flow shop. Here, in this paper an attempt is made to analyse the task of using multi objective minimizing of weighted sum of tot...

Facial gestures and feelings are nothing but reactions to human beings' external and internal events in real life situations. Recognition of the end user's gestures and feelings from video streaming plays a very important role in human computer interaction. In such systems, the complex changes in human face movements need to be quickly monitored in order to provide the necessary response system. In order to avoid road collisions, the only real-time application is physical fatigue detection based on facial detection and expressions such as driver fatigue detection. Physical fatigue analysis or detection based on face expression is beyond the scope of this paper, but this paper reveals research on various methods recently proposed for facial expression and/or video recognition of emotions. This paper presents the methodologies in their comparative analysis in terms of feature extraction and classification used in methods of facial expression and/or emotion detection. The compa...

In today environment it is very complicated to choose between the object oriented Model and the relational model, many factors should be considered. The most important of these factors are single level and multilevel access controls, protection against inference, and maintenance of integrity etc. When determining which distributed database model will be more secure for a particular application, the decision should not be made purely on the basis of available security features. One should also question the efficiency of the delivery of these features. Do the features provided by the database model provide adequate security for the Intended application? Does the implementation of the security controls add an unacceptable amount of Computational overhead? In this paper, we describe the object databases advantages and disadvantages over RDBMS. We also give the description of implementation issues of object oriented databases.

The speech recognition process is conserved as a separate issue with dependable models for predicting and classification decisions that improves behaviours and makes assignments less dependent on human experience. The speech recognition solution proposes to distinguish text from speech, which have range diminishing in completing their actions. This proposed work provides the speech that observe an appropriate prediction in a using CNN approach for adequate performance. For this, the training dataset and discriminative models are effective improvements for speech recognition that proposed SSVM is an appropriate possibility for substantial language for continuous speech recognition. SSVM features are possible to be extracted delivers continuous speech recognition that have specified optimal segmentation into the training process uses convex optimisation procedure. The supervised learning and artificial neural network, recognize speech for datasets to have interpretation of speech recognition. In this work, speech recognition uses SSVM to observe the protocol and have an acceptable performance. Also, in this work uses speech recognition to have better accuracy with CNN is provided in this research work, which calculates the optimum performance as a consequence.

The study proposes an evolutionary algorithm-based improvement heuristic for the permutation flow-shop problem. The method uses a constructive heuristic to arrive at a good first solution. The GA-based improvement heuristic is used in conjunction with CDS, Gupta's algorithm, and Palmer's Slope Index, which are all well-known constructive heuristics. The method is put to the test on a series of ten issues that vary from 4 to 25 tasks and 4 to 30 machines. The outcomes are also compared to some of the most well-known lower-bound options