Design and testing of solar charged voice recognized Arduino car
This research study is an advent of voice recognition technology that has ushered in a new era of human-machine interaction, enabling seamless and intuitive control of various devices and applications. In this research paper, we present a novel application of voice recognition technology in the realm of robotics - a Voice-Activated Arduino Car Control System. This system combines the power of Arduino microcontrollers, voice recognition software, and motor control mechanisms to create a versatile and user-friendly platform for controlling a robotic car using voice commands. The primary objective of this research is to design and implement a voice-activated control system that enhances the accessibility and usability of robotic cars. To achieve this, we have developed a comprehensive system architecture comprising hardware and software components. The hardware includes an Arduino microcontroller, motor drivers, sensors, and a microphone. At the same time, the software incorporates a voice recognition algorithm, and the solar panel is used to recharge the battery which is user-friendly.
Published by: Joel David, Joel Prince P., Kathireshkumar G.
Author: Joel David
Paper ID: V9I6-1192
Paper Status: published
Published: November 28, 2023
Comparative phytochemical and in vitro antioxidant studies of ethanolic, hydroethanolic, and aqueous root extracts of rhinacanthusnasutus(l.) Kuntze
Rhinacanthus nasutus(L.)Kuntze (Nagamalli) has long been used in traditional medicine to alleviate abscess pain, tinea versicolor, pruritic rash, ringworm, and skin diseases. The current study sought to explore the existence of numerous phytochemicals as well as the total phenolic, flavonoid, and tannin levels of Rhinacanthus nasutus. In qualitative analysis, the various phytochemical compounds were screened using standard methods. In quantitative estimation, the total phenolic content of each extract was 74.75, 82.47 & 61.25 mg of GA/g extract, the total flavonoid was 368.2, 512.25&160.6 mg of Ru/g extract, and the total tannin was 17.5, 38.5&22.6 mg of CTN/g extract respectively. In-vitro antioxidant activity of ethanolic, hydroethanolic, and aqueous extracts of Rhinacanthus nasutus root was determined by DPPH free radical scavenging assay and determination of total antioxidant activity. The hydroethanolic extract had shown very significant DPPH (1, 1-diphenyl-2-picryl-hydrazyl) radical scavenging activity compared to other extracts. In the DPPH free radical scavenging assay IC50 value of alcoholic, hydroalcoholic, and aqueous root extracts was found to be 282.28, 114.62, and 597.96 µg/mL respectively. The total antioxidant activity of the Rhinacanthus nasutus root extracts was calculated from the calibration curve of ascorbic acid, the results show the antioxidant activity of ethanolic, hydroethanolic, and aqueous extracts were 37.675, 50.85, and 28.225 mg of AA/g extract respectively. The quantitative phytochemical and antioxidant assays showed significant activity for hydroalcoholic extract when compared with ethanolic and aqueous extracts. Thus the present study revealed the comparative phytochemical and in-vitro antioxidant potential of ethanolic, hydroethanolic, and aqueous root extracts of Rhinacanthus nasutus.
Published by: Ajmi Shahul, Suja S.R
Author: Ajmi Shahul
Paper ID: V9I6-1178
Paper Status: published
Published: November 25, 2023
Improvised approach to Deepfake detection
Deepfakes are realistic, human-synthesized videos that are incredibly simple to produce thanks to the advent of sophisticated algorithms fueled by advances in the field of deep learning. Deepfakes are being used to create fictitious news stories about terrorism, politics, and retaliation in order to incite societal unrest. The development of efficient techniques for identifying deepfakes is imperative, given the mounting concerns surrounding them. Our work in this field offers a brand-new deep learning-based method that effectively distinguishes between authentic videos and artificial intelligence-generated phony ones. Our technique can identify deepfakes that are both reenactments and replacements. To counter the threat posed by artificial intelligence (AI), we suggest a system that makes use of AI. In order to train an InceptionV3 model to categorize films as either real or manipulated, depending on whether they have undergone any kind of alteration, this method uses an MTCNN neural network to extract frame-level information. We assess our method on a large-scale balanced and mixed data set in order to mimic real-time scenarios and improve the model’s performance on real-time data. This dataset was painstakingly created by combining multiple available datasets.
Published by: Lipika Chadha, Hiya Kulasrestha, Vishesh Bhargava, Varun Jindal
Author: Lipika Chadha
Paper ID: V9I6-1183
Paper Status: published
Published: November 25, 2023
Efficient establishment of foreign manufacturing units in India: utilizing operation research approaches amidst changing import regulations
With increasing regulatory policies from countries, companies have to find an efficient way to set up their manufacturing units in other countries as well. Currently majority of the companies set up their manufacturing units in China as they are able to manufacture products at a cheaper price, however this will not work for the long term, as if the companies intend to sell their products in countries with restrictive import policies, they will have to diversify and set up their manufacturing units in that/those countries as well. To select the right location for setting up a manufacturing unit, a company must look into what we classified as Internal and External factors which we will be going in-depth further into the research paper. This research paper sets out to provide a framework to those companies that are looking to set up their own manufacturing units in a country using operation research tools such as Linear programming which includes visualizing the “Internal” factors as a transportation problem. One probable limitation of this research paper is assuming that the current demand for the products in the economy will remain equal to the forecasted demand or demand in the coming years after setting up the manufacturing units, demand will be used in the linear programming problems later in this research paper.
Published by: Bhuvan Mahesh, Ashray Kamath, Bharath Ram, Diya Jindal, Aditya Maurya, Atharva Kasat
Author: Bhuvan Mahesh
Paper ID: V9I6-1158
Paper Status: published
Published: November 16, 2023
Optimizing waste recycling through data science: a deep learning approach with Tensorflow
The intricate relationship between recycling practices and the generation of solid waste emphasizes the multifaceted character of sustainable waste management. A comprehensive comprehension of these dynamics is crucial for formulating specific interventions and policies that optimize the positive effects of recycling on our environment. This research explores the application of deep learning, specifically utilizing TensorFlow, in the domain of waste classification for enhanced waste recycling. The study focuses on the efficient categorization of diverse waste materials, including cardboard, glass, trash, metal, and plastic, through the implementation of advanced neural networks. By leveraging TensorFlow's capabilities, our research demonstrates the successful development and deployment of deep learning models that exhibit accurate and reliable waste classification. The utilization of deep learning model was able to select accurately the most important types of garbage worthy for recycling processes. And not only streamlines recycling processes but also addresses the environmental challenges associated with inefficient waste management. The findings finally highlight the transformative potential of integrating cutting-edge technologies into waste sorting systems, paving the way for a more sustainable and eco-friendly approach to waste disposal. This research contributes to the ongoing efforts in environmental conservation by presenting a viable solution to enhance waste recycling practices through the power of deep learning.
Published by: Oghenetega Adogbeji
Author: Oghenetega Adogbeji
Paper ID: V9I6-1167
Paper Status: published
Published: November 16, 2023
Cloud Migration Challenges for IP PBX in Critical Hospital Environments
Migrating to the cloud for IP PBX systems in critical environments like hospitals is a transformative endeavor offering significant benefits, but it also introduces a unique set of challenges. This article delves into the complexities associated with this migration, addressing key concerns such as data security and privacy, reliability, network connectivity, cost management, legacy system integration, compliance with healthcare regulations, and user training and adoption. By proactively recognizing and effectively tackling these challenges, hospitals can harness the potential of cloud-based IP PBX systems to enhance patient care, streamline operations, and maintain the highest standards of security and reliability.
Published by: Saju Thanislas
Author: Saju Thanislas
Paper ID: V9I6-1153
Paper Status: published
Published: November 16, 2023
