In a weapon system, the accurate forecasting of the spare parts demand can help avoid the excess inventory, leading to the efficient use of budget. It can also help develop the combat readiness of the weapon system by improving weapon system utilization. Moreover, as performance-based logistics (PBL) projects have recently emerged, the accurate demand forecasting of spare parts has become an important issue for the PBL contractors as well. However, for the demand forecasting of spare parts, the time series methods, typically used in the military sector, have low prediction accuracies and the PBL contractors are mostly based on the judgment of practitioners. Meanwhile, most of the previous studies in the military sector have not considered the managerial characteristics of spare parts (e.g., reparability and the irregularity of maintenance). No previous work has considered any such features, which can indicate the reliability of spare parts (e.g., mean time between failures (MTBF)), although they can affect the spare parts demand. Therefore, to develop a more accurate forecasting of the spare parts demand of military aircraft, we designed and examined a systematic approach that uses data mining techniques. To fill up the research gaps of related works, our approach also considered the managerial characteristics of spare parts and included the new features that represent the reliability of spare parts. Consequently, given the case of South Korea and the full feature set, we found random forest gave better results than the other data mining techniques and the conventional time series methods. Using the best technique Random Forest, we identified the contribution of each managerial feature set to improving the prediction accuracy, and we found the reliability and operation environment are valuable feature sets in a significant way, so they should be collected, managed more carefully, and included for better prediction of spare parts demand of military aircraft.

Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

textWhen stopping production, the manufacturer has to decide on the lot size in the final production run to cover spare part demand during the end-of-life phase. This decision can be supported by forecasting how much demand is expected in the future. Forecasts can be obtained from the installed base of the product, that is, the number of products still in use. Consumer decisions on whether or not to repair a malfunctioning product depend on the specific product and spare part. Further, consumers may differ in their decisions, for example, for products with fast innovations and changing social trends. Consumer behavior can be accounted for by using appropriate types of installed base, for example, full installed base for cheap but essential spare parts of expensive products, and warranty installed base for expensive spare parts of products with short lifecycle. The paper presents a general methodology for installed base forecasting of end-of-life spare part demand and formulates research hypotheses on which of four installed base types performs best under which conditions. The methodology is illustrated by case studies for eighteen spare parts of six products from a consumer electronics company. The research hypotheses are supported in the majority of cases, and forecasts obtained from installed base are substantially better than simple black box forecasts. Incorporating past sales via installed base supports final production decisions to satisfy future consumer demand for spare parts.

Spare part demand forecasting for consumer goods using installed base information

Since the existing music emotion classification researches focus on the single-modal analysis of audio or lyrics, the correlation among models are neglected, which lead to partial information loss. Therefore, a music emotion classification method based on deep learning and improved attention mechanism is proposed. First, the music lyrics features are extracted by Term Frequency-Inverse Document Frequency (TF-IDF) and Word2vec method, and the term frequency weight vector and word vector are obtained. Then, by using the feature extraction ability of Convolutional Neural Network (CNN) and the ability of Long Short-Term Memory (LSTM) network to process the serialized data, and integrating the matching attention mechanism, an emotion analysis model based on CNN-LSTM is constructed. Finally, the output results of the deep neural network and CNN-LSTM model are fused, and the emotion types are obtained by Softmax classifier. The experimental analysis based on the selected data sets shows that the average classification accuracy of the proposed method is 0.848, which is better than the other comparison methods, and the classification efficiency has been greatly improved.

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Music Emotion Classification Method Based on Deep Learning and Improved Attention Mechanism

Medium of music has evolved specifically for the expression of emotions, and it is natural for us to organize music in terms of its emotional associations. Much of the difficulty in developing emotion-based features is the ambiguity of the ground-truth. Emotion recognition is an advanced trend in research field. Indian Classical music is an ancient tradition and also has very effective mathematical structure. In the field of emotion recognition, most of the work has been done on western music but very less on Indian Classical Music. In this paper, we are proposing a system which will help to recognize an emotion for Indian Classical Music.

A framework for emotion identification in music: Deep learning approach

In this paper, we are working to understand the statistical behavior of acoustic features of audio, for one of the low resource languages, which is Kokborok from North East India. First, we have developed a classification system for Kokborok music by using the traditional machine learning technique. We used mainly Timbre, Rhythm, and Intensity feature to classify songs between four classes having three subclasses. This classification system gives poor performance compared to other Indian languages and western languages. So, we develop a computational method to minimize the errors for each class for the overall system. For such poor low resource language, the ground truth set creation is very tough. So, the behavior of the audio features of each song is analyses mathematically to understand whether the truth set is correct or not. Technically the feature values have to be different for each class and in a similar range for subclasses. We have defined a statistical parameter called “alpha” (α), for estimating the better value of the accuracy rate. This parameter alpha eventually estimates the final accuracy rate. This alpha is calculated, and the final value of the accuracy rate was calculated by extrapolating when the number of songs goes to infinity. The method enhances the actual accuracy rate from 49 to 63%, in the limit when the number of samples goes to infinity. Overall, our approach, when used in conjunction with the machine learning method, can predict a better accuracy rate for Kokborok music.

Building a computational model for mood classification of music by integrating an asymptotic approach with the machine learning techniques

Raga plays an important role in Indian classical music. Raga is made up from the swara or note. According to characteristics of raga, Indian classical music is further divided into two systems Hindustani / North Indian classical music, Carnatic / South India classical music. This paper introduces us with some basic terms in Indian classical music and terms associated with raga. Then we discussed different techniques for raga identification along with their system structure. Different challenges associated with raga identification.

Techniques for Indian classical raga identification- A survey

Now-a-days demand forecasting is used in many countries for military applications such as spare parts of aircraft and for improving budget efficiency. In supply chain management demand forecasting is a major issue. Currently, time series technique is used to demand forecast but this technique resulted in lack of accuracy and improvement in accuracy is needed. So this paper focused on comparing the features which leads to improvement in the accuracy and propose a system for demand forecasting of spare parts of anti-aircraft missiles, which are based on machine learning and neural networks such that equipment's are properly utilized and alongwith that budget is also maintained. We have compared the existing features with the new features added and applied algorithms and looked upon at the accuracy. Experimental results proves that the new features added gave higher accuracy. Here, we also present an end-to-end boosting system called XGBoost and Multi-layer Perceptron. These new techniques were compared with traditional Machine Learning techniques. This experiment is conducted on the Vietnam War dataset.

Analysis on Machine Learning Algorithms and Neural Networks for Demand Forecasting of Anti-Aircraft Missile Spare Parts

Multi-label emotion recognition from Indian classical music using gradient descent SNN model

– Music directly connects to the soul of living creature. It deeply influences mind and brain. Indian Classical Music is closely associated with mood and emotions. Emotion detection in Indian Classical music can serve as the basis for content based music retrieval and recommendation. Music can be mapped to mood by extracting its features that contribute to generation of specific emotions and mapping them with emotional model of valence and arousal. Hence analysis of features of North Indian Classical Music that contribute to emotions becomes the basic step of mood detection in North Indian Classical Music.

Bhavana Tiple

Papers

Techniques for Indian classical raga identification- A survey

A framework for emotion identification in music: Deep learning approach

Analysis on Machine Learning Algorithms and Neural Networks for Demand Forecasting of Anti-Aircraft Missile Spare Parts

Multi-label emotion recognition from Indian classical music using gradient descent SNN model

Analysis of Features for Mood Detection in North Indian Classical Music - A Literature Review