Business Information System Muneesh Kumar Pdf [WORK] Download
With the planning, analysis, modeling, design, implementation and maintenance (in short: the development) of such highly complex, dynamic, and integrated information systems, an attractive and at the same time challenging task for the academic discipline of business and information systems engineering BISE arises, which can secure and further develop the competitiveness of industrial enterprises.
Business Information System Muneesh Kumar Pdf Download
The central focus of this article is the estimation of the level of interest and efforts required in incorporating ML techniques into the present manufacturing industries. Furthermore, by identifying subpopulations of related and relevant literature, this work aims to identify critical areas of the ML technologies application. Also, the paper identifies the relevant gaps in the deployment of ML techniques which are presented as future research scopes. The technology available now enables us to design and develop products as per the industrial needs. With the advent of digital media, it has become a piece of cake to search and download the research literature, which was quite cumbersome in manual methods (Chonde, 2016). Various search engines and repositories such as google scholar and ScienceDirect are available to receive detailed information by entering the keywords of simple phrases. These customized searches are relied on the input words and do not fetch any underlying mechanisms or concepts involved. In this review work, Latent Semantic Analysis (LSA) was used to develop the relation between sentences and documents (Dumais, 2004). This emphasizes not just simple word matching as with traditional searches but core concept justification.
The manufacturing industry produces a vast amount of data every day (Chand & Davis, 2010). These data compromise various formats, for example Monitoring information from the manufacturing line, meteorological specifications, process performance, machining time, and machine tool settings, to name a few examples (Davis et al., 2015). Different countries have used unlike names for this process; for example, Germany uses Industry 4.0, the USA uses Smart Manufacturing, while in South Korea, it is known as smart Factory. The vast amount of research publications increases the massive amount of data, sometimes called Big Data (Lee et al., 2013). Such data helps to improve the process performance by giving active feedback to the machine. The extracted useful information from the Big Data helps to expand the process and product quality sustainably (Elangovan et al., 2015). However, the negative impact of such a huge amount of data will confuse or lead to a false conclusion. If the system used to manage such massive data is well-established, it is always a boon to the manufacturing industries. It can also be noted that the availability of such a reliable data system helps improve the process quality, cost reduction, understanding of the customers' expectations, and analysing business complexity and dynamics involved (Davis et al., 2015; Loyer et al., 2016).
Several ML models are developed to tackle massive data (Multivariate Statistical Methods in Quality Management xxxx). However, factors like probable over-fitting must be considered in the implementation process (Widodo & Yang, 2007). Several options are available for reducing dimensions if it ascertains to be an issue, even though it is improbable because of the influence of the algorithms used (Pham & Afify, 2005). Using ML in manufacturing can be vital in extracting outlines from available data that can estimate the possible output (Nilsson & Nilsson, 1996). This new technique could help process owners make better decisions or automatically improve the system for a better marginal profit in the business. Lastly, the objective of specific ML algorithms is to find patterns or regularities that explain relationships between the various causative parameters involved in the process. Due to the challenges of a quickly changing, complex manufacturing setting, machine learning (ML) as part of AI has the ability to understand and evolve. Hence, the developer has the freedom of analyzing without expecting the consequences of the situation. As a result, ML makes a compelling case for its implementation in manufacturing compared to other prevailing models. A significant power of ML models is that it automatically learns from and adapt to changing situations (Lu, 1990).
Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.
Direct digital image processing-based techniques have been extensively employed in prior research to track tool faults and breakage due to their reliability and low cost. Because of the geometry of cutting, the unpredictability of the wear nature, and the lack of knowledge about how wear can alter the measured signals, indirect approaches are exceedingly difficult to design and implement. Additionally, there are some limitations to the use of these approaches, and the cost of the sensors are still very expensive (Sortino, 2003). Modern manufacturing and process monitoring systems have undergone a full transformation, thanks to machine learning (ML). Artificial neural network (ANN) (Ross et al., 2022), hidden Markov model (HMM) (Li & Liu, 2019), support vector machine (SVM) (Lu et al., 2013), and other techniques were specifically used in feature identification of TW monitoring and prediction. A method for using machine vision during cutting to predict the escalating tool flank side wear was presented by (Dutta et al., 2016). They developed a technique to extract information on feed marks and waviness from the machined surface using distinct approaches. The decision-making approach of SVM has been applied to accurately describe the tool state. Li & An (2016) established a novel micro-vision system for TW monitoring, which is a crucial facet of intelligent manufacturing. To reach each section of TW, an adaptive version of the Markov Random Field (MRF) technique was designed. According to the findings, automatic focusing and segmentation of the TW area by region are likely to improve precision and resilience, in addition to enabling the collecting of TW images in real-time. Although monitoring and predicting tool wear had seen significant progress, the methods employed above for doing so had major flaws. In order to monitor and predict TW using typical ML techniques, features must first be extracted. The particular extraction of features and method selection had a significant impact on how well various ML approaches performed. Deep belief networks (DBN),Convolutional neural networks (CNN), and other deep learning (DL) models have been developed in the last ten years as solutions to these issues. DL could address the aforementioned problems, as it related to a class of ML approaches in which several layers of data processing steps in hierarchical architectures were used for pattern categorization and prediction (Wang et al., 2021). In order to forecast surface unevenness and precise energy usage during 5-axis milling, Serin et al., (2017) used DMLP neural networks. To identify TW conditions Ou et al., (2021) projected an online sequential learning by means of a stacked denoising autoencoder to take out abstract characteristics. Cao et al., (2019) created a 2D CNN for TW monitoring. The input (i/p) parameters of the CNN comprised of a high signal-to-noise ratio for vibration signals. For the TW estimate, Aghazadeh et al., (2018) utilized a CNN with a mixed feature extraction strategy to estimate the volume of TW. This method employed wavelet time-frequency transformation and spectrum subtraction methods. The raw i/p data were converted into a CNN model by Martínez-Arellano et al., (2019) who developed the model using time series photography. An LSTM network was developed by Sun et al., (2020) to forecast several flank wear metrics based on raw data. Bidirectional LSTM networks were used by Zhao et al., (2017) to monitor the fault in milling tools after machining.
An extensive study of the existing literature has been carried out to identify the various techniques and methodologies along with the existing challenges available for speech recognition that could trigger further research in the field. Speech has been classified into isolated or connected words, continuous or spontaneous speech. The basic modes of speech include speaker-dependent and speaker-independent. Each speech recognition system is attributed to several challenges. First, there is a wide variation of the speakers in uttering a word leading to the pronunciation difference. This variation is further attributed to the age, gender, and dialect of the physical appearance of the speaker. Secondly, background noise can add to the problems of recognizing speech accurately in different environments and real-time applications. The next challenge includes the physiological aspect of pronouncing the words by how stress is given on different syllables, phones, and vowels. This particularly affects the speech recognition of tonal languages. A continuous speech system is rather difficult to implement owing to the uninterrupted speech we use in real life. This further poses problems in speech recognition systems. Other factors contributing to the lower recognition rates include poor microphone quality, position, and direction of the microphone relative to the speaker. Despite these challenges, environment variation, channel variation, style of speaking, age, and gender contributes to the challenging task of speech recognition, e.g., Kirchhoff and Vergyri  mentioned that the Arabic language script falls short of the vowels as well as other information related to the phones. The major difficulties, which posed issues for speech recognition of the Russian language, are the variations in the informal speech of the language and the non-existence of standard dictionaries . There is some speech corpus that has been collected by some researchers/research agencies in India, but this corpus has not been made available to the researchers for carrying out their research in the field. Thus, a lack of a standard database for Indian languages is a dearth. Shanthi Therese and Lingam  reported that for designing an efficient speech recognition system, selecting and extracting the most relevant parametric information are very crucial. Segmentation of words into corresponding phonemes in Indian languages is a tedious task to pursue. Owing to the linguistic variations in different languages of India, a single language may have many scripts and multiple languages may have one script. Furthermore, different people of different regions speak one common language in different ascents or tones. For instance, Punjabi, being a tonal language, is spoken differently in different parts of Punjab. Although a lot of work has been done in other languages, Punjabi, being one of the most popularly used languages across the globe, needs some attention from the researchers in terms of speech processing. Accuracy, noise removal, information retrieval, and varying bit rate are some of the most considerable parts of speech recognition challenges.