Volume 9 | Issue 6 |

 Abstract

In human communication, feelings play an important role. The ability to decipher human emotions by breaking down voice is appealing in a variety of contexts. Feelings recognition can be found in a variety of places, such as the connection between PCs and people and call centres. Feeling recognition had previously relied on simple classifiers on bag-of-words models. Nonetheless, the current work on voice feeling recognition was done with the use of deep learning algorithms on static voice data. The proposed technique focuses on improving the overall precision of feeling acknowledgment during calls including artificial intelligence. The overall goal is to accurately perceive the various emotions that a single utterance conveys semantically.

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 Keywords

Speech conversion, human emotion, feature extraction, machine learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Positive deviance approach, Malnutrition, Healthy practices in children, Nutritional status of children.

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In this paper, comprehensive thermodynamic analysis of an indirect fired gas turbine cogeneration system with multistage reheat system for heating and electricity proposes is reported. An exergy analysis carried out in the present study for improving and optimizing the overall performance of the system. This cogeneration system consists of a gas turbine cycle, a heat recovery steam generator cycle and a steam turbine cycle. A comparative study between no heat and multistage reheat shows that there is a significant improvement in electric power output, process heat production, fuel utilization efficiency and exergy efficiency due to reheat. It is also showing that the power to heat ratio is decreasing with the reheat because improvement in process heat generation is greater than the improvement in electric power output.

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ENERGY AND EXERGY ANALYSIS OF GAS TURBINE COGENERATION POWER PLANT.

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Carbon Nano Materials (CNMs) have many applications because of their super catalytical, mechanical, electrical, optical, thermal and strong electromagnetic wave absorption properties. In this work, CNM were synthesized from plant fiber and decorated with cobalt nanoparticles (Co-NPs). Microwave absorption efficiencies were studied in the frequency range of 2- 8 GHz having thickness ranging from 2-5 mm. The study concludes that CNMs decorated with Co-NPs exhibit excellent microwave absorption capability. The prepared sample shows strong microwave absorption of 96-99%, whereas -20 dB Reflection loss for 2-8 GHz frequency range of 4-5mm thickness having specific surface area of 648 m2.g-1.

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 Keywords

Microwave absorption, Cobalt nanoparticles (Co NPs), BET, Vector Network Analyzer (VNA), Ferromagnetic, paramagnetic.

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 Abstract

Lightweight cryptography is a new concept that refers to a method of bettering data security through using fewer resources and providing higher throughput, conservatism, and low power consumption. Every fraction second, the Internet of Things (IoT), which links billions of devices, collects an enormous amount of data. When the number of devices grows, so does the amount of data generated, and the protection of that data becomes a concern. In IoT architecture, devices are basically smaller and low-powered. Because of their complexity, traditional encryption algorithms are computationally costly and require several rounds to encrypt, effectively wasting the limited resources of IoT devices. A less complex algorithm, on the other hand, can compromise the necessary integrity. There are many lightweight cryptography algorithms available, and we use one of the symmetric encryption algorithms called Advanced Encryption Standard (AES). The speed of this algorithm is six times that of triple DES. Since all around us is automated in today's world, automation is playing an increasingly important role in human life. The term "smart home" encompasses not only the reduction of human effort but also energy conservation and time management. Home automation enables us to control household appliances such as lights, doors, fans, and air conditioners, which require a large number of energy-constrained devices that constantly interact with one another. The security of this home automation system must not be compromised. This problem of security for automated systems is often solved by using lightweight cryptographic algorithms. For this purpose, a lightweight security algorithm is proposed in our paper that is (AES).

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 Keywords

Internet of Things (IoT), Home Automation, LWC, AES, DES.

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This paper deals with three algorithms implementations for square root computation. Newton-Raphson's, Iterative and Binary Search Algorithm implementations are completely designed, verified and compared. The algorithms, entire systems realization and their functioning are described.

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Creative Commons Attribution 4.0 and The Open Definition

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Recent advances in Big Data has prompted health care practitioners to utilize the data available on social media to discern sentiment and emotions expression. Health Informatics and Clinical Analytics depend heavily on information gathered from diverse sources. Traditionally, a healthcare practitioner will ask a patient to fill out a questionnaire that will form the basis of diagnosing the medical condition. However, medical practitioners have access to many sources of data including the patients writings on various media. Natural Language Processing (NLP) allows researchers to gather such data and analyze it to glean the underlying meaning of such writings. The field of sentiment analysis (applied to many other domains) depend heavily on techniques utilized by NLP. This work will look into various prevalent theories underlying the NLP field and how they can be leveraged to gather users sentiments on social media. Such sentiments can be culled over a period of time thus minimizing the errors introduced by data input and other stressors. Furthermore, we look at some applications of sentiment analysis and application of NLP to mental health. The reader will also learn about the NLTK toolkit that implements various NLP theories and how they can make the data scavenging process a lot easier.

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NLP Cosine similarity tf-idf grammar correction

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The formation of crowd in small local shops is inevitable. After the pandemic, situation has changed to such an extent that avoiding the crowd in closed compact spaces would be a priority. Hence, considering this priority, crowd estimation would be of great help. Here, we present a technique for crowd estimation by monitoring the time thus preventing a crowd formation. This approach is implemented with the aid of QR Code Scanning method.

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 Keywords

Crowd, ML, QR scan, pandemic, compact spaces

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 Abstract

Air quality prediction is one of the complex tasks due to its dynamic nature, volatility, and variability in the time and space of pollutants. At the same time, being able to model, predict, and monitor the quality of air is becoming more relevant due to the observed critical impact of air pollution on the health of citizens and the environment. This paper aims to predict the monthly Air Quality Indices using ARIMA (Autoregressive Integrated Moving Average) model. Then categorize AQI into different classes. This knowledge will help us to reduce its level to a lesser than harmful range. The prediction and categorization will be based on a data set of atmospheric conditions in a specific area.

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 Keywords

AQI, ARIMA MODEL, Stationary Series.

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 Abstract

This study has been undertaken to investigate the self sustainable and Ecofriendly model that has been suggested to implement the Power Generation Unit from Road Transport By using the Piezoelectric Sensors, which will convert the pressure and mechanical energy from the vehicles into electrical energy and making a Prototype for the same.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Self sustainable, Piezoelectric Sensors, Prototype.

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Creative Commons Attribution 4.0 and The Open Definition