Artificial Intelligence articles list

Estimation of snr based adaptive-feedback equalizers for feedback control in hearing aids

Despite the evolution of modern technology, the users of hearing aids do not realize the persistence of feedback, while wearing the device until the condition becomes worse. The feedback cancellation algorithms, instead of cancelling the acoustic feedback, limits speech intelligibility. The paper presents a novel method for estimation of SNR based adaptive-feedback equalizers (SBAFE) algorithm to develop an optimized hearing aid for the feedback less sound transmission and achieving better speech discrimination. The data gathered for the optimization is visualized and compared with the traditional technology, which provides the subjective and objective quality of the hearing aids.

Jayanthi G

Implementation of big data analytics for simulating, predicting and optimizing the solar energy production

The notable developments in renewable energy facilities and resources help reduce the cost of production and increase production capacity. Therefore, developers in renewable energy evaluate the overall performance of the various equipment, methods, and structure and then determine the optimal variables for the design of energy production systems. Variables include equipment characteristics and quality, geographical location, and climatic variables such as solar irradiance, temperature, humidity, dust, etc. This paper investigated and reviewed the current big data methods and tools in solar energy production. It discusses the comprehensive two-stage design and evaluation for examining the optimal structure for renewable energy systems. In the design stage, technical and economic aspects are discussed based on a robust analysis of all input/output variables for determining the highest performance. Next, assess and evaluate the effectiveness of each method under different circumstances conditions. Then convert each qualitative indicator into a quantitative measure using extensive data analysis methods to determine the overall performance of the various qualitative variables. The paper also provides an in-depth analysis of the mathematical techniques used in measuring the efficiency of the renewable energy production system and discussing future axes of work in the field of specific energy.

ACAA PUB

Credit risk analysis- a case study of canara bank

All the people who need loan may turn to their local banks, credit unions or peer to peer lenders. Every lending institution has its own advantages and drawbacks. In this scenario credit risk management becomes increasingly important element as the same is concerned with managing the financial debts and safeguarding the interest of the banks. The purpose of credits given by banks is to earn interest and make profits. The important function of credit management is to decide how much credit should be given to the borrower and ensuring compliances with the credit terms of repayment and avoid Non-Performing Assets (NPA) to the banks. Credit risk is the biggest risk the bank faces by the virtue of nature of business, inherits. The ability of commercial banks to formulate and adhere to policies and procedures that promote credit quality and curtail non-performing loans is the means to survive in the stiff competition. Inability to create and build up quality loans and credit worthy customers leads to default risk and bankruptcy as well as hamper the economic growth of the country

Shaila Kamath

A comparative study of credit risk management: a case study of canara bank and karnataka bank.

Banks need finance to carry out their day to day activities smoothly. There will be times where the borrowers fail to repay the money leading a risk to the lenders. There are various types of risks faced by the banks such as financial and non-financial risk in the unstable environment. These risks may be a threat for the existence and achievements of banks. A Credit risk is the risk which arises when the borrower fails to make required payments. It is a huge loss to the lender where he loses both the principal and interest which leads to the interruption of the cash flows and increase in collection costs. Banks usually follow a certain framework while lending loans so that they can manage the credit risks. The main purpose of credit risk management is to find out how much credit should be provided to the borrowers and the different ways to collect the amount back. The success of banks depends on the formulation of the policies and procedures of lending the loans and collecting the amount back and avoid Non-Performing Assets (NPA) to the banks. When banks collect their debts systematically and avoid the Non- Performing Assets (NPA), they can survive in the competitive market. The study is focused on the comparison of two banks such as Canara Bank and Karnataka Bank with regard to loans, advances, interest received and expended and the variation in the levels of Non- Performing Assets. Methodology used is the secondary source of data where the balance sheet of the banks and the income and expenditure statement of the banks are being used to explore the credibility and the capacity of the banks in managing the credit risk.

Shaila Kamath

Formulation and evaluation of herbal face pack

formulation and evaluation of herbal face pace pack

Sumaira Taj

A novel heuristic for the transportation problem: dhouib-matrix-tp1

The transportation problem is widely applied in the real world. This problem aims to minimize the total shipment cost from a number of sources to a number of destinations. This paper presents a new method named Dhouib-Matrix-TP1, which generates an initial basic feasible solution based on the standard deviation metric with a very reduced number of simple iterations. A comparative study is carried out in order to verify the performance of the proposed Dhouib-Matrix-TP1 heuristic.

Souhail Dhouib

N-gram and k-nearest neighbour based igbo text classification model

The evolution in Information Technology has gone a long way of bringing Igbo, one of the major Nigerian languages evolved. Some online service providers report news, publish articles and search with this language. The advancement will likely result to generation of huge textual data in the language, that needs to be organized, managed and classified efficiently for easy information access, extraction and retrieval by the end users. This work presents an enhanced model for Igbo text classification. The classification was based on N-gram and K-Nearest Neighbour techniques. Considering the peculiarities in Igbo language, N-gram model was adopted for the text representation. The text was represented with Unigram, Bigram and Trigram techniques. The classification of the represented text was done using the K-Nearest Neighbour technique. The model is implemented with the Python programming language together with the tools from Natural Language Toolkit (NLTK). The evaluation of the Igbo text classification system performance was done by calculating the recall, precision and F1-measure on N-gram represented text. The result shows text classification on bigram represented Igbo text has highest degree of exactness (precision); trigram has the lowest level of precision and result obtained with the three N-gram techniques has the same level of completeness (recall). Bigram text representation technique is extremely recommended for any text-based system in Igbo. This model can be adopted in text analysis, text mining, information retrieval, natural language processing and any intelligent text-based system in the language.

Dr. Nkechi Ifeanyi-Reuben

Design and development of framework for big data based smart farming system

Improving the agricultural productivity is an imminent need to meet the food requirement of constantly growing population rate. It can be gracefully satisfied if the farming process is integrated through technologies such as big data and IoT. The integration of agricultural processes with modern technologies has emerged as the smart agriculture technology. This research work is focused on proving the suitability of the big data analytics for smart agricultural processes in terms of increasing production and quality of yields with less resources and overhead. This research paper expounds the extensive review carried out on the related works in smart agricultural farming, challenges in implementing the smart farming technologies at large scale, followed by the conceptual framework model for the effective implementation of big data together with IoT devices in smart farming.

Dr H Shaheen