Shivam Tiwari

Arizona State University

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Deepak Arora

Amity University

K. Anki Reddy

Indian Institute of Technology Tirupati

Vishal Nagar

Indian Institute of Technology Kanpur

Manan Sharma

United States Department of Agriculture

Sarvesh Kumar

National Botanical Research Institute

Vishal Nagar

Indian Institute of Technology Kanpur

Madhusudhana Subramanyam

Koneru Lakshmaiah Education Foundation

Dip Sankar Banerjee

Indian Institute of Technology Jodhpur

Puneet Sharma

IILM Institute for Higher Education

Abhijit Gogoi

Indian Institute of Technology Guwahati

Cooperative Institutions

Arizona State University

Indian Institute of Technology Guwahati

Bhabha Atomic Research Centre

University of North Carolina at Chapel Hill

SRM Institute of Science and Technology

University of Delhi

Amity University

Indian Institute of Information Technology Guwahati

University of Petroleum and Energy Studies

Indian Institute of Science Bangalore

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Elsevier eBooks (2024)

M.S. Anwar Prashasti Ashok Shivangi Chamoli Shraddha Dorlikar Ifrah Farid

10.1016/b978-0-323-98805-6.01002-2

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Effect of an ionic environment on membrane fouling: a molecular dynamics study

Physical Chemistry Chemical Physics (2021)

Shivam Tiwari Abhijit Gogoi K. Anki Reddy

The effect of the ionic environment on membrane fouling was investigated for polyamide (PA) and graphene oxide (GO) membranes using equilibrium molecular dynamics (MD) simulations.

Membrane Fouling

10.1039/d0cp05268j

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Citations (7)

Comparative study of Jrip j48 and naive bayes algorithm in Flower specie prediction

Journal of Physics Conference Series (2021)

Aakanksha Jain Shivam Tiwari Madhusudhana Subramanyam Khalid Jamal Ajay Singh Yadav

Abstract Artificial intelligence and Deep learning techniques propose and provide effective mechanism for classification among several commodities like gender classification on basis of ridge count, spam mail classification and detection etc. Here we have proposed a hybrid module on basis of available AI and ML techniques by which we can achieve more than 90% accuracy for any provided test dataset. We have included over 50423 samples, out of which we use 66 percentage of data for training purpose of our model and 34% remaining sample we use as test dataset, with 8 fold we have achieve approximately 96% accuracy. For collection of dataset and processing of dataset we gone through several phases which include extraction of feature (EoF) using feature extraction technique, for cleaning of dataset we have use dimension reduction technique factor analysis(FA). In next phase for classification of flowers we have used classification techniques in which we found the accuracy score of j48 classifier is higher than Naïve Bayes and Jrip algorithm. Hence the choice of j48 classifier for classification is right.

C4.5 algorithm

Statistical classification

10.1088/1742-6596/1854/1/012046

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Role of Big Data and IoT in Optimizing Post-Harvest Management

International Journal of Research Publication and Reviews (2024)

Shivam Tiwari

10.55248/gengpi.5.1024.2929

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Detection of insomnia using advanced complexity and entropy features of sleep stage data of EEG recordings

Measurement Sensors (2022)

Shivam Tiwari Deepak Arora Vishal Nagar

Sleep disorders affect the physical ability in the function under mental, emotional, and physical forms depending upon its intensity. These abnormalities are observed on the basis of structure of sleep. Most prevalent sleep disorders are insomnia, bruxism, depression, and narcolepsy. Common issues in sleep disorder are sleeplessness, irregular leg movements, problems with fast eye movement behavior and breathing abnormalities. Therefore, an early-stage therapy that might save a patient's life depends on a precise diagnosis and categorization. The much more sensitive as well as significant bio-signal is electroencephalographic (EEG) signal. It has capacity to record sleep-sensitive brain activity. We used an available EEG database which had recordings divided into different types of sleep disturbances as well as a healthy control group. Popular sensor's EEG brain function has been examined. Ultimately, using patterns taken from EEG data, a categorization AI model was created. Extracted characteristics worked well as a biomarker for identifying sleep problems when combined with an AI classifier.

Sleep Stages

Sleep

10.1016/j.measen.2022.100498

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Citations (8)

Conceptualizing trust in community-academic research partnerships using concept mapping approach: A multi-CTSA study

Carolina Digital Repository (University of North Carolina at Chapel Hill) (2018)

Gaurav Dave Leah Frerichs J. Jones Myunghee Kim J. Schaal

10.17615/6y79-j860

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Behavior Analysis through Routine Cluster Discovery in Ubiquitous Sensor Data

Manan Sharma Shivam Tiwari Suchetana Chakraborty Dip Sankar Banerjee

Behavioral analysis (BA) on ubiquitous sensor data is the task of finding the latent distribution of features for modeling user-specific characteristics. These characteristics, in turn, can be used for a number of tasks including resource management, power efficiency, and smart home applications. In recent years, the employment of topic models for BA has been found to successfully extract the dynamics of the sensed data. Topic modeling is popularly performed on text data for mining inherent topics. The task of finding the latent topics in textual data is done in an unsupervised manner. In this work we propose a novel clustering technique for BA which can find hidden routines in ubiquitous data and also captures the pattern in the routines. Our approach efficiently works on high dimensional data for BA without performing any computationally expensive reduction operations. We evaluate three different techniques namely LDA, the Non-negative Matrix Factorization (NMF) and the Probabilistic Latent Semantic Analysis (PLSA) for comparative study. We have analyzed the efficiency of the methods by using performance indices like perplexity and silhouette on three real-world ubiquitous sensor datasets namely, the Intel Lab Data, Kyoto Data, and MERL data. Through rigorous experiments, we achieve silhouette scores of 0.7049 over the Intel Lab dataset, 0.6547 over the Kyoto dataset and 0.8312 over the MERL dataset for clustering.

Non-negative Matrix Factorization

Perplexity

10.1109/comsnets48256.2020.9027377

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Identification of PLMS Sleep Disorder using EEG Signal feature-based classification by Machine Learning Techniques

Research Square (Research Square) (2024)

Shivam Tiwari Deepak Arora Vishal Nagar Durgesh Srivast Suhaib Ahmed

Abstract It has been demonstrated that periodic leg movements during sleep (PLMS) are connected to alterations in features of EEG signal. Data mining evaluates hemispheric/cortical activity-related hemodynamic changes. We used data mining and machine learning to examine whether there are changes in brain hemodynamics associated with PLMS. Nighttime EEG recordings were made while brain activity was monitored in PLMS patients. Scores from EEG feature data were examined to find relevant differences. PLMS were consistently accompanied by variations in brain activity that increased in magnitude when related to changes in EEG and persisted even in the absence of any arousal that could be seen visually in the EEG. This study is the first to show PLMS-related alterations in brain activity. Clinical relevance of these observations has yet to be established. We have used wavelet decomposition with or without it to complete the aforementioned classification tasks. For binary disease to identify tasks employing the sleep stage of N3, we have achieved classification accuracy ranging between 92% and 96% and AUC ranging between 0.85 and 0.89. But, as the use of the suggested wavelet-based features is performed, a superior classification accuracy is achieved, with an AUC of 0.99 and a range of 94–98%. This is because the best wavelet-based features have a high degree of discrimination.

Feature (linguistics)

Identification

SIGNAL (programming language)

Sleep

10.21203/rs.3.rs-3913324/v1

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An Ultrawideband 5G Interference Mitigation Filter for C-Band Ground Based SATCOM Receivers

Parthasarathi Samanta Shivam Tiwari Hitesh Modi Vineet Kumar Dad Praveen Kumar Ambati

This paper presents the design and development of a compact and high Q microwave filter with stringent rejection and group delay requirement for C-Band ground receiver applications to mitigate terrestrial 5G interference. The proposed filter is designed using coaxial interdigital resonators with non-resonating nodes. The filter provides more than 60 dB rejection in the frequency band of 3.3-3.67 GHz and more than 30 dB rejection above 4.23 GHz. The designed filter is ultra-wideband with 13% fractional bandwidth at 3950 MHz. Filter is compact (185 mm length), light-weight (mass about 300 gms), and designed with WR-229 waveguide interface.

Communications satellite

Adjacent-channel interference

10.1109/mapcon58678.2023.10464108

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Moisture-driven CO2 direct air capture and delivery for cultivating cyanobacteria

SSRN Electronic Journal (2025)

Justin Flory Shuqin Li Sterling A. Taylor Shivam Tiwari Jason Kmon

A system called AUDACity was developed and demonstrated for continuously delivering ~100 g per day of direct air-captured (DAC) CO2 into aqueous alkaline solutions within a 4.2 m2 outdoor raceway pond in a moisture-driven process. Anion-exchange resin particles contained within elongated mesh tube packets were found to reduce drying and CO2 loading time ~4-fold over larger mesh bags, which allowed the sorbent to clump and reduce surface area exposed to air, as measured in a closed loop wind tunnel. The system was used for cultivating the cyanobacterium Synechocystis sp. PCC 6803 in a 4.2 m2 outdoor raceway pond. The culture grown in the AUDACity pond did not show visible contamination whereas a control pond without AUDACity showed contamination from amoeba and diatoms. Biofouling of the sorbent packets during cultivation reduced its CO2 binding capacity by ~50%, which was partially restored to 75% of its initial capacity after sequentially washing in 0.04% bleach, 1 mM HCl and then 0.5 M NaHCO3.

10.2139/ssrn.5070812

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