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14. Addition of insoluble fiber to isolation media allows for increased metabolite diversity of lab-cultivable microbes derived from zebrafish gut samples

Author

Condren, Alanna R, Costa, Maria S, Sanchez, Natalia Rivera, Konkapaka, Sindhu, Gallik, Kristin L, Saxena, Ankur, Murphy, Brian T, and Sanchez, Laura M

Subjects
Animals, Bacteria, Culture Media, Dietary Fiber, Gastrointestinal Microbiome, Intestines, Phylogeny, Secondary Metabolism, Zebrafish, Gut microbes, in vitro cultivation, insoluble fiber, natural products, zebrafish, metabolomics, in vitro cultivation, Microbiology
Abstract

There is a gap in measured microbial diversity when comparing genomic sequencing techniques versus cultivation from environmental samples in a laboratory setting. Standardized methods in artificial environments may not recapitulate the environmental conditions that native microbes require for optimal growth. For example, the intestinal tract houses microbes at various pH values as well as minimal oxygen and light environments. These microbes are also exposed to an atypical source of carbon: dietary fiber compacted in fecal matter. To investigate how the addition of insoluble fiber to isolation media could affect the cultivation of microbes from zebrafish intestines, an isolate library was built and analyzed using the bioinformatics pipeline IDBac. While all isolation media encouraged the growth of species from several phyla, the extent of growth was greater with the addition of fiber allowing for easier isolation. Furthermore, fiber addition altered the metabolism of the cultivated gut-derived microbes and induced the production of unique metabolites that were not produced when microbes were otherwise grown on standard isolation media. Addition of this inexpensive carbon source to the media supported the cultivation of a diverse community whose secondary metabolite production may more closely replicate their metabolite production in vivo.

Published
2020

15. Analytical Study of Fluid Pressure-Sensing Mechanism in Microchannel for Microfluidic Device

Author

Saxena, Ankur, Kumar, Mahesh, Singh, Kulwant, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Chaurasiya, Prem Kumar, editor, Singh, Abhishek, editor, Verma, Tikendra Nath, editor, and Rajak, Upendra, editor

Published
2022
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17. Increase in Mental Health Cases Post COVID Outbreak

Author

Majumder, Agnideepa, Arora, Mehardeep Singh, Mantri, Palak, Saxena, Ankur, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Khanna, Ashish, editor, Gupta, Deepak, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2022
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18. Impact of COVID-19 Pandemic on Mental Health Using Machine Learning and Artificial Intelligence

Author

Naiem, Rakshanda, kaur, Jasanpreet, Mishra, Shruti, Saxena, Ankur, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Khanna, Ashish, editor, Gupta, Deepak, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2022
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30. Medical Data Analysis Using Machine Learning with KNN

Author

Mohanty, Sabyasachi, Mishra, Astha, Saxena, Ankur, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Gupta, Deepak, editor, Khanna, Ashish, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2021
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31. mHealth for Mental Health

Author

Saxena, Mohit, Deo, Anveshita, Saxena, Ankur, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Gupta, Deepak, editor, Khanna, Ashish, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2021
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32. Analysis of Diabetes and Heart Disease in Big Data Using MapReduce Framework

Author

Saluja, Manpreet Kaur, Agarwal, Isha, Rani, Urvija, Saxena, Ankur, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Gupta, Deepak, editor, Khanna, Ashish, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2021
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34. Utilizing parametric models for real-time speaker recognition by stimulating frequency characteristics.

Author

Mishra, Ajay Kumar, Saxena, Ankur, Pawar, Sushant Janardhan, and Kakde, Bhagwat

Subjects
*SPEECH perception, *GAUSSIAN mixture models, *ERROR probability, *SIGNAL processing, *PARAMETRIC modeling
Abstract

The speech recognition exercises in this paper are intended to identify the speeches having MFCC and GMM as a basis. As a practical biometric technique for phone applications, speech recognition offers a lot of promise and doesn't require specialized or complex hardware. Signal processing is usually done in two stages: testing and training, to accomplish the objective of speech recognition. Speaker specific feature parameters are computed from the speech during the training phase. The characteristics are utilized to create statistical representations of various speakers. Speech samples from unidentified speakers are compared with the models and categorised throughout the testing phase. This study presents the development of an MFCC & GMM voice recognition algorithm. The well recognised Mel Frequency Cepstral coefficients, or {MFCCs}, have been employed as characteristics because of the documented changes in the crucial bandwidths of the human ear with frequency. We created a system model utilizing the GMM (Gaussian Mixture Model) in order to make the system realistic. With the help of the EM (Expectation Minimization) algorithm, GMM parameters are computed. MFCCs are computed throughout the testing and training stages. During a training session and a subsequent assessment session, speakers repeated distinct sentences. Speech can be falsely rejected or accepted up to a certain threshold. The location of this decision threshold is when the probabilities of the two errors are equal. The MATLAB environment was used to construct the codes. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Comparative analysis of microcantilever MEMS switch designs utilizing electrostatic actuation method.

Author

Saxena, Ankur, Agrawal, Vimal Kumar, Kumar, Mahesh, and Singh, Kulwant

Subjects
*THRESHOLD voltage, *INDUCTIVE effect, *THIN films, *VOLTAGE, *COMPARATIVE studies
Abstract

A comparative analysis was conducted on microcantilever RF MEMS switches to optimize their pull-in voltage using the electrostatic actuation method. The study considered two distinct types of microcantilever RF MEMS switches, namely the Π-microcantilever and R-microcantilever MEMS switches. These two structures were optimized to achieve maximum deflection at the applied voltage threshold. The implementation of a thin film switch in the RF MEMS switch led to increased switching speed and reduced power consumption at lower pull-in voltages. Additionally, the RF MEMS switch was effective in minimizing fringing field effects and stress. Specifically, the R-microcantilever RF MEMS switch demonstrated a maximum deflection of 0.399 µm, while the Π-microcantilever switch exhibited a minimum deflection of 0.0119 µm at a pull-in voltage of 8.1. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Intelligent fire safety and mitigation system (I-FireS): Adaptive fire extinguishing based on fire type with integrated evacuation assistance.

Author

Baral, Shikhar, Sharma, Jiya, Saxena, Ankur, and Kumar, Mahesh

Subjects
INDIANS (Asians), FIRE extinguishing agents, FIREFIGHTING, FIRE prevention, SENSOR arrays, FIRE detectors
Abstract

In 2019, fire hazards took lives of approximately 2 people for every 100,000 people in India. There were a reported 1.6 million cases of fire hazards which killed 27,027 people. Although the number of people killed by fires has decreased in the last few years, it is a preventable accident that still accounts for more than 2% of accidental deaths. There are many fire safety and suppression systems out there that use either only one sensor to detect fire or only use water for fire suppression which is not efficient for different types of fires. The l-FireS system offers a vital solution, leveraging an array of sensors for swift fire detection and classification. Following detection validation, the system initiates a comprehensive emergency response, alerting occupants and notifying emergency services. It guides safe evacuations through laser-projected paths and directional audio instructions, adapting dynamically based on sensor data. Safety measures include assessing ventilation feasibility and, if safe, opening doors and windows, and activating an exhaust fan. Upon successful evacuation, the system engages precise fire suppression, selecting extinguishing agents based on fire type. This paper highlights the critical role of a potential smart i-FireS system in quick fire detection, fire classification, and automated emergency response for enhanced safety. [ABSTRACT FROM AUTHOR]

Published
2024
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37. An approach to smart autonomous vehicle with cloud integration and natural interaction.

Author

Ali, Suhalia, Senapati, Sagar, Saxena, Ankur, and Kumar, Mahesh

Subjects
SENSOR arrays, CLOUD computing, MOBILE apps, AUTONOMOUS vehicles, ACQUISITION of data
Abstract

Automated vehicles have become increasingly vital for everyday use, albeit often commanding a substantial financial investment. While vehicles with individual enhancements have been developed, there has been a lack of a unified solution. Our My Automated Vehicle (MAV) represents a comprehensive integration of essential services, encompassing interior amenities, safety features, automation capabilities, remote accessibility, personalized services, and a natural interaction interface. This integration relies on an array of sensors and actuators for data collection, which is subsequently transmitted to a cloud-based platform. The cloud platform is then linked to a mobile application, facilitating command exchange through a remote server. This approach significantly enhances the convenience, efficiency, and intelligence of the vehicle. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Automated multi axis depth controlled drilling machine.

Author

Kumar, Mahesh, Sharma, Jiya, Saxena, Ankur, Singh, Prabh Deep, and Singh, Kulwant

Subjects
MECHANICAL movements, STEPPING motors, BOREHOLES, POTENTIOMETERS, MACHINERY
Abstract

Precisely controlling hole depth presents a formidable challenge, particularly in the context of fine boreholes, when utilizing conventional handheld drilling machines. This is often associated with failures due to over-drilling. Consequently, the development of an automatic drilling machine capable of executing precise drilling operations based on user-defined drilling depth parameters is imperative. The proposed machine will incorporate power feed technology, designed to facilitate the drilling of workpieces up to specified depths. The depth control mechanism, integrated with the drilling motor, interfaces with an Arduino board, which can be programmed to execute drilling operations to the desired depth. The depth control mechanism is achieved by linking the drilling motor, via gears, to a power feed motor, specifically a stepper motor. The mechanical movements of the machine are constrained by programming the drilling depth using potentiometers connected to the Arduino board. The Arduino board, driven by user inputs from a keypad, will oversee the direction and the number of steps of rotation of the stepper motor through a program stored in the Arduino Mega. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Evolution of mHealth Eco-System: A Step Towards Personalized Medicine

Author

Saxena, Mohit, Saxena, Ankur, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Khanna, Ashish, editor, Gupta, Deepak, editor, Bhattacharyya, Siddhartha, editor, Snasel, Vaclav, editor, Platos, Jan, editor, and Hassanien, Aboul Ella, editor

Published
2020
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41. Predicting the Outcome of an Election Results Using Sentiment Analysis of Machine Learning

Author

Saxena, Ankur, Kushik, Neeraj, Chaurasia, Ankur, Kaushik, Nidhi, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Khanna, Ashish, editor, Gupta, Deepak, editor, Bhattacharyya, Siddhartha, editor, Snasel, Vaclav, editor, Platos, Jan, editor, and Hassanien, Aboul Ella, editor

Published
2020
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42. Genetic Variance Study in Human on the Basis of Skin/Eye/Hair Pigmentation Using Apache Spark

Author

Saxena, Ankur, Chandra, Shivani, Grover, Alka, Anand, Lakshay, Jauhari, Shalini, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Khanna, Ashish, editor, Gupta, Deepak, editor, Bhattacharyya, Siddhartha, editor, Snasel, Vaclav, editor, Platos, Jan, editor, and Hassanien, Aboul Ella, editor

Published
2020
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43. Comparing Machine Learning Algorithms to Predict Diabetes in Women and Visualize Factors Affecting It the Most—A Step Toward Better Health Care for Women

Author

Agarwal, Arushi, Saxena, Ankur, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Khanna, Ashish, editor, Gupta, Deepak, editor, Bhattacharyya, Siddhartha, editor, Snasel, Vaclav, editor, Platos, Jan, editor, and Hassanien, Aboul Ella, editor

Published
2020
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45. A Comprehensive Case Study on Traditional Weaving of Western Rajasthan

Author

Srivastava, Ankita, Saxena, Ankur, Srivastava, Ankita, and Saxena, Ankur

Abstract

The present paper discusses an age-old craft practised in India named Pattu weaving. This weaving technique is based on the extra weft weave design. Along with the ground warp and weft, few extra bright colored yarns are used in this technique to weave beautiful motifs on the fabric during the weaving process. The craft follows the sustainable way of fabric manufacturing as the fabrics are made on the handloom and follow the traditional ways for pre-weaving process also. The craft becomes more relevant in the present scenario considering the fact that for the last few decades unsustainable practices have increased drastically due to the imbalance of demand and supply, scarcity of natural resources, advent of unsustainable technologies among the other factors. While traditionally, sustainable practices were an integral part of the lifestyle of human beings and balanced the eco-system of the earth. One of the ways for balancing the natural energy consumption is utilization of all natural resources wisely by reinventing traditional craft practices. Hence, the present study focuses on the materials, tools, techniques and processes used in the Pattu weaving craft. PASTLE analysis of the craft has also been done to understand the opportunity and challenges of the craft. Along with this, a comparative analysis of the traditional and contemporary raw materials, techniques, motifs, colour range and application of the craft have also been covered in this paper. For this study, the primary research is based on the information that was collected during Craft Research Documentation activity funded by the Ministry of Textiles, India. Data from Secondary research has also been added to strengthen the analysis.

Published
2024

46. On Intra Prediction for Screen Content Video Coding

Author

Chen, Haoming, Saxena, Ankur, and Fernandes, Felix

Subjects
Computer Science - Graphics
Abstract

Screen content coding (SCC) is becoming increasingly important in various applications, such as desktop sharing, video conferencing, and remote education. When compared to natural camera- captured content, screen content has different characteristics, in particular sharper edges. In this paper, we propose a novel intra prediction scheme for screen content video. In the proposed scheme, bilinear interpolation in angular intra prediction in HEVC is selectively replaced by nearest-neighbor intra prediction to preserve the sharp edges in screen content video. We present three different variants of the proposed nearest neighbor prediction algorithm: two implicit methods where both the encoder, and the decoder derive whether to perform nearest neighbor prediction or not based on either (a) the sum of the absolute difference, or (b) the difference between the boundary pixels from which prediction is performed; and another variant where Rate-Distortion-Optimization (RDO) search is performed at the encoder to decide whether or not to use the nearest neighbor interpolation, and explicitly signaled to the decoder. We also discuss the various underlying trade-offs in terms of the complexity of the three variants. All the three proposed variants provide significant gains over HEVC, and simulation results show that average gains of 3.3% BD-bitrate in Intra-frame coding are achieved by the RDO variant for screen content video. To the best of our knowledge, this is the first paper that 1) points out current HEVC intra prediction scheme with bilinear interpolation does not work efficiently for screen content video and 2) uses different filters adaptively in the HEVC intra prediction interpolation.

Published
2015

49. Handling Big Data Using MapReduce Over Hybrid Cloud

Author

Saxena, Ankur, Chaurasia, Ankur, Kaushik, Neeraj, Kaushik, Nidhi, Kacprzyk, Janusz, Series Editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Gupta, Deepak, editor, Khanna, Ashish, editor, and Pan, Indrajit, editor

Published
2019
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