Keywords : Artificial Intelligence

Research on DNN Methods in Music Source Separation Tools with emphasis to Spleeter

Louis Ansal C A; Ancy C A

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 6S, Pages 24-28

This paper tries to attempt a review on deep neural network (DNN) method in music source separation (MSS) tools with emphasis to Spleeter by Deezer, an enhanced deep learning model for music sourceseparation. It is a set of pre-trainedmodel written in python using the Tensorflow machine learning library used for musicsource separation. It was developed by Deezer, on the need to separate a given mixed music track to its constituentinstrumental or vocal tracks usually known as stems. Spleeter offers 3 pre-trainedmodels namely 2, 4, and 5 stemseparation models that are capable of separating a given mix into 2, 4, and 5 stems respectively, which can be used forvarious needs like remixing, up-mixing,music transcription, etc. This paper is the first of its kind to review on DNN methods in MSS.In this paper, we will learn about the purpose and useof Spleeter developed by Deezer as well as about the technical aspect behind this software product that includes areas like ArtificialIntelligence (AI), Machine Learning and Deep Learning, and further about Time-Frequency (TF) masking and U-NetConvolution Neural Network (CNN) which are the methodology and architecture employed in it respectively. From thereview, we learned that Spleeter by Deezer is one of the latest advancement in MSS problem that comparatively has one of the best signal to distortion ratio (SDR), signal to artifacts ratio (SAR), signal to interference ratio (SIR), and sourceimage to spatial distortion ratio (ISR) and produce a state of the art solution, and it has also paved a way togreater development in MSS problem in the future.

Understanding the Future Communication: 5G to 6G

Vinesh Raj; Ancy C A

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 6S, Pages 17-23

This article enrolls the development and review of the 6G(6th Generation) wireless communication technology which is expected in the 2030s and is the first of its kind to review 6G concepts in IoT (Internet of Things). Wireless communication technology will make communication between entities and these technologies are separated as different generations. 6G wireless communication technology will be an application of IoT by bringing the world closer. 6G network implementation will be a promising and developing field in wireless communication technology. In this paper, we discuss how the 6th Generation of wireless communication technologies overcomes the existing problems faced by the previous generation (5G). We have a general architecture for wireless communication technology. By adding some new key concepts (Artificial Intelligence, optical wireless communication, terahertz frequency, and wireless power transfer) to this architecture we can produce a new generation (6G) of wireless technology. This paper also describes the advantages and challenges which can be faced by the 6th generation of wireless communication. A few of the key points trends behind the growth of the 6G wireless communications are green communication, network traffic, intelligent network, localization, new spectrums, high reliability, low latency, high data bit rate, network availability. The 6G will provide a better communication system in the future by ensuring future trends.

Pest Detection for Rice Using Artificial Intelligence

Madhavi G; Jhansi Rani A.; Srinivasa Rao S.

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICITCA-2021 5S, Pages 54-60

Agriculture not only provides food for humans, but it is also a major source of revenue for any nation. Millions of dollars are spent every year to protect rice crops from insects and pests that cause damage during harvest and storage. Early pest detection, which allows the crop to be protected from pest attack, is one form of crop protection. The best way to learn about the health of a crop is to examine it regularly. If pests are discovered, adequate steps may be taken to prevent the crop from suffering a major loss of yield. Early detection will help to reduce the use of pesticides and direct the pesticide selection process. It has grown into a large field of science, with a lot of work being done around the world to detect pests automatically. The typical way of inspecting the fields is with the naked eye. A farmer must manually search and assess over a vast landscape of fields, risking overlooking different affected areas and conducting thorough research across large lots. To analyse the entire area, several human experts are needed, which is both costly and time-consuming. This proposed system is mainly intended to develop an Intelligent IT-driven system using various Artificial Intelligence and Computer Vision Algorithms for precision farming, enabling the delivery of information directly to the farmer's phone, providing the details of damage localization, crop health, and needs for fertilizer and pesticide application.

Converging Blockchain and AI technology-based Automated and Decentralized (A&D) Trust Management System using Face Detection

Pujah Balasubramaniam; Gokilavani Sagadevan

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICITCA-2021 5S, Pages 11-15

Face detection systems are growing exponentially. Newly emerged technologies are also being involved in the management applications. But they had failed to compensate at least anyone of the essential aspects of the system such as scalability, security, personalization, etc. This paper presents a fundamental platform that provides the ways and techniques to intelligently use the integration of Artificial Intelligence and Blockchain in which AI is used to detect and recognize the face and Blockchain maintains the tamper-proof records. This convergence will provide a tamper-proof and rapidly working A&D access management system for a trust management system that can be used for attendance in an organization & for many other purposes.

Basic Design for the development of Autonomous Underwater Vehicle

Muniyandy Elangovan; Balaji T

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 11, Pages 12-17
DOI: 10.47392/irjash.2020.213

The artificial intelligence (AI) is used to automate the operation by taking a decision based on the situation or conditions. It is expected that around 2025, Robotics can move on earth similar to a human and support human society. Artificial Intelligence (AI) is in every field to automate the decision making intelligence to execute the desired operation. Understanding the need and importance of AUV for underwater operation, the authors have identified the need an AUV for the monitor, search and rescue operation. During the development unmanned vehicle, it was identified that the major task and key functionalities to be known to the researchers who want to contribute in this AI domain. The functionalities of AUV is decided by features which can be grouped into six categories as (i) Design of AUV shape (ii) Functionalities  (iii) Design of Mechanical and Electrical system (iv) Control system (v) Navigation system (vi) Embedded system/software programming and (vii) Propulsion system. As a researcher, understanding each task or goal is very much needed and useful. Keeping the new researchers need, each task has been studied and highlighted the critical issue in each task which was faced in our development process. This paper can encourage many new researchers to do research in the upcoming domain of AUV.

Intelligence slicing: A synthesized framework to integrate artificial intelligence into 5G networks

Chandrakala V; Surya Kumar M S R

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 8, Pages 57-61
DOI: 10.47392/irjash.2020.94

For best-performing networks from 5G and above, it must support a wide range of needs. It is understood that more transmission, resource assistance and communication systems will be required. Achieving these tasks can be challenging as network infrastructure becomes more complex and massive. A good solution is to incorporate more robust AI technology that has been tested to provide answers ranging from channel prediction to autonomous network management, as well as network security. Today, however, the latest technology to integrate AI into wireless networks is limited to using a unique AI algorithm to solve a specific problem. A comprehensive framework that can fully utilize the power of AI in solving various network problems remains an open problem. Therefore, this paper introduces the idea of the spy pieces on which the AI unit is installed and delivers on one condition. Intelligence units are used to flexibly control the intelligence of AI algorithms with two comprehension strategies to perform different intellectual tasks: 1) Neural network-based channel predictions and 2) Industrial network-based security acquisition, to illustrate this framework.

Enhancement in the World of Artificial Intelligence

Suneetha V; Salini Suresh; Niharika Sinha; Sabyasachi Prusty; Syed Jamal J

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue Special Issue ICARD 2020, Pages 276-280
DOI: 10.47392/irjash.2020.132

Artificial Intelligence is a developing zone in the field of innovation and furthermore attempts to show that the eventual fate of AI gains ground so that machines would function according to a human and would likewise convey the action of the person. It is difficult to create a machine like individuals who can appear feelings or think like individuals in different conditions. Directly we have recognized that AI is the examination of how to form things which can accurately fill in as people do. A working framework that utilizes AI reasoning procedures has a computerized reasoning motor, and experience scientific and Statistical module, an adjustment module and a UI. The computerized reasoning motor processes an accomplished expository boundary from a front code and a back code. The experience of scientific and Statistical module records and changes the experience's systematic boundary. The alteration module changes the front code and the back code as per the consequence of the experience logical and Statistical module computation of the experience systematic boundary. The UI inputs information or showcases the consequence of the computation. In the man-made consciousness motor, the experience diagnostic boundary is then again added to either the front code or the back code to register another experience investigative boundary. Such a game plan, the working framework can consequently change the consequence of the computation as per the decision or past decisions of the client.

Machine Learning: An Intuitive Approach In Healthcare

Salini Suresh; Suneetha V; Niharika Sinha; Sabyasachi Prusty; Sriranga H.A

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 7, Pages 67-74
DOI: 10.47392/irjash.2020.67

Health is a crucial resource for a person's being to measure in our society from any disease. The fast development of the population appears to be trying to record and dissect the massive measure of knowledge about patients. Healthcare may be a need, and clinical specialists are constantly attempting to get approaches to actualize innovations and give effective outcomes. The main problem faced by the healthcare industry is the rising costs which include diagnosis and prediction of diseases, drug discovery, medical imaging diagnosis, personalized medicine, behavior therapy, and smart health records. Machine learning gives us an advantage of processing these information naturally which helps in making the human services framework progressively powerful. Getting the correct determination may be a key part of Healthcare. It clarifies a patient's medical issue and suggests health care treatment. The disease diagnostic technique is a complex, community-oriented action that has clinical, intelligent and data social events to make a decision about a patient's medical issue. Google has built up a ML model to assist recognize dangerous tumours on mammograms. Stanford’s profound learning calculation to differentiate skin malignancy. This paper is focused on the importance of Machine Learning in Healthcare just like the different application areas, latest research works in healthcare, wise machine learning contribution in Healthcare, and so on. Machine Learning is an application of Artificial Intelligence that helps in automatically learning and improving itself from experience. It is used in many other sectors like Law, Marketing & Advertising, Finance, Retail& Customer Services and Healthcare which also includes Covid-19. This paper presents various research in the Medicine and Healthcare sector