A Strategic Double-Loop Learning Method for Organisational Decision-Making toward Servitisation

In recent years, manufacturing industries have been expected to achieve servitisation—namely, a shift from product sales to product-service systems—in order to achieve sustainable production and consumption patterns. In order to achieve servitisation, manufacturing firms should grasp the business environment and encourage organisational learning to develop the knowledge for servitisation in their environment. The existing knowledge management studies enable the empirical acquisition and reuse of knowledge from past case studies and make efforts to support organisational learning. However, they do not cover the guiding of firms engaged in servitisation to learn appropriately for their business environment. The learning required for manufacturing firms engaged in servitisation is learning that focuses on questioning and modifying existing product-oriented premises—double-loop learning. This paper proposes a method to support strategic double-loop learning within manufacturing companies engaged in servitisation. This method evaluates the compatibility between the implicit premises that manufacturers refer to as the rationale for their decision toward servitisation and the external environment and enables to formulate a practical strategy for double-loop learning. The proposed method was applied to the case of a cassette tape music player to demonstrate its usefulness. This study suggests theoretical foundations for future research into knowledge management for traditional manufacturing companies’ decisions concerning servitisation, and suggests that these should be carried out dynamically according to the business environment.

Web Application for Emotion-based Music Player

Human expressions play an important role in the extraction of an individual's emotional state. It helps in determining the current state and mood of an individual, extracting and understanding the emotion that an individual has based on various features of the face such as eyes, cheeks, forehead, or even through the curve of the smile. A survey confirmed that people use Music as a form of expression. They often relate to a particular piece of music according to their emotions. Considering these aspects of how music impacts a part of the human brain and body, our project will deal with extracting the user’s facial expressions and features to determine the current mood of the user. Once the emotion is detected, a playlist of songs suitable to the mood of the user will be presented to the user. This can be a big help to alleviate the mood or simply calm the individual and can also get quicker song according to the mood, saving time from looking up different songs and parallel developing a software that can be used anywhere with the help of providing the functionality of playing music according to the emotion detected. Keywords: Music, Emotion recognition, Categorization, Recommendations, Computer vision, Camera

MACHINE LEARNING BASED CLOUD MUSIC APPLICATION WITH FACIAL RECOGNITION USING ANDROID STUDIO (MUSYNC)

This paper output is a music player application but when it comes to its features it will be way more than a simple music player. It is developed on Android Studio and other tools like: Firebase is used as database, Android phone camera, Music library of Android Phone are used in the development of application. When user changes his phone or reset his phone then all of his data is lost or user has to put all the data in his computer and then back to his mobile phone except data that is backed up online. Message data, photos and contacts are that things that users backed up online. But music files normally don’t get backed up and user troubles in re downloading the files or moving files in computer and back to phone. In this purposed work the targeted problem is resolved as MUSYNC application is be able to automatically backup all the mp3 data from the phone and user will get all of his data by just signing in the application in his new phone. The purposed application has a feature of sync music. Users can sync music with another one and that person will able to listen to same music instantly. Application also provides a unique feature of mood detection using digital image processing DIP. This feature is able to check your face emotion and play music according to it. User just has to take a picture and that is it, this music player plays the music according to your mood. This feature is useful when user having tough time what to listen.

ESPOTIFY (An Emotion Based Music Player)

Human emotion plays an essential role in social relationships. Emotions are reflected from verbalization, hand gestures of a body, through outward appearances and facial expressions. Music is an art form that soothes and calms the human brain and body. To analyze the mood of an individual, we first need to examine its emotions. If we detect an individual's emotions, then we can also detect an individual's mood. Taking the above two aspects and blending them, our system deals with detecting the emotion of a person through facial expression and playing music according to the emotion detected that will alleviate the mood or calm the individual and can also get quicker songs according to the emotion, saving time from looking up different songs. Different expressions of the face could be angry, happy, sad, and neutral. Facial emotions can be captured and detected through an inbuilt camera or a webcam. In our project, the Fisherface Algorithm is used for the detection of human emotions. After detecting an individual's emotion, our system will play the music automatically based on the emotion of an individual.

Gesture Controlled Music Player

A music player is a computer program for playing audio files or songs encoded in MP3 format. This application will reside in the user's computer, such as iTunes, Windows Media Player and RealPlayer that are used to organize a music collection and play audio files. Our player provides an easy-to-use graphical user interface with symbols provided on the buttons to enable varied types of users to use the player efficiently. Along with the basic functionalities of a music player like playing, pausing, stopping and playing next or previous song, we have provided some interesting features which make our player intelligent. Hence, we have rightly named it ‘Brain Waves Music Player’. It has the ability to judge our sentiments and play the suitable songs accordingly. Also, the player can be controlled using gestures.

A Review on Life Cycle Assessment of Solar PV Panel

Humans tend to connect the music they hear, to the emotion they are feeling. The song playlists though are, at periods too large to sort out automatically. It would be accommodating if the music player was “smart enough” to sort out the music based on the current state of emotion the individual is feeling. The main idea of this project is to automatically play songs based upon the emotions of the adherent. Based on the emotion, the music will be played from the predefined playlist. It aims to deliver user-preferred music with emotional attentiveness. In the existing system user want to manually select the songs, randomly played songs may not accede to the feel of the adherent, user has to classify the songs into various emotions and for playing the songs user has to manually choose a particular emotion. These difficulties can be avoided by using our project. This is a novel way that helps the handler to automatically play songs based on the emotions of the handler. It recognizes the facial emotions of the adherent and plays the songs based on their emotion. The emotions are recognized using a machine learning method Support Vector Machine (SVM) algorithm. The human twist is an important organ of an individual's body and it especially plays an important role in the heritage of an individual's behaviours and emotional appearance.