2024

Permanent URI for this collectionhttps://dl.bhu.ac.in/bhuir/handle/123456789/36736

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A Cascaded Deep Learning Approach for Detection and Localization of Crop-Weeds in RGB Images
(Springer Science and Business Media Deutschland GmbH, 2024) Rohit Agrawal; Jyoti Singh Kirar
Weeds compete with crops in the fields, thus lowering crop yield with losses of up to 80%. The efficient use of chemical herbicides is desired to reduce the harmful effects on the environment, which requires the location of the weeds to be known. In this paper, we present a deep learning approach capable of detecting and localizing weeds in RGB images, trained using the publicly available Open Sprayer dataset. The adopted methodology consists of a classification step using a pre-trained 2D convolution neural network and a Random Forest classifier, which is used to predict the presence of weeds in an RGB image. If presence is predicted, then an attempt to localize them has been done by cascading a segmentation step using a U-Net architecture. The proposed architecture can classify the presence of weeds in an image with an accuracy of 91.19% and predict the location of weeds in the image by generating binary masks with a mean Dice score of 0.879 on the publicly available Open Sprayer dataset. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
A categorization-modelling procedure to estimate urban goods transport demand with a minimum number of activity categories
(Elsevier B.V., 2024) Jesus Gonzalez-Feliu; Diana G. Ramirez-Rios; Agnivesh Pani; Salma Bouraoua
This paper presents an integrated clustering-regression modelling framework to assess the number of trips that each establishment of a given urban zone, city or conurbation generates. The goal of the method is to construct establishment-based trip models using the categorial FTG methodology using a minimal set of categories of activities. The methodology combines a hierarchical cluster analysis that defines a limited number of coherent categories of activities, then a regression-based modelling method that links the FTG rates (production, attraction and generation) to employment, giving a specific functional form for each category (constant, linear, logarithmic or potential). The models are constructed and calibrated on the basis of the 2011-2012 Freight Trip Survey of the Paris Region. Using a hierarchical clustering technique, 5 categories of establishment are defined and resulting FTG models obtained. Most functional forms are linear or potential, with some particular cases where constant generation is preferred. Results are assessed using the R2 coefficient and discussed. The resulting models are robust and applicable to any French city. © 2024 Elsevier B.V.. All rights reserved.
A Data Driven Approach for Anomaly Detection in Renewable Energy Integrated Power Systems
(IEEE Computer Society, 2024) Rachita R. Sarangi; Prakash K. Ray; Soumya R. Mohanty; Subir Karmarkar; Asit Mohanty
As Distributed Energy Resources (DER) continue to proliferate in power systems, ensuring their reliable and efficient operation becomes paramount. Anomaly detection is a crucial task to maintain the integrity of DER integrated systems and prevent operational disruptions. In this case study, a datadriven approach based Empirical Mode Decomposition (EMD) for anomaly detection in DER integrated systems is presented. Leveraging feature engineering techniques, we analyze the behavior and performance of DER components, including renewable energy sources, energy storage systems, and grid-interconnected devices. This approach utilizes experimental data and real-time measurements to establish normal operating conditions and identify deviations indicative of anomalies, which may encompass equipment malfunctions, load variations, or grid disturbances. Through this case study, the practical implementation is showcased using the proposed method on a real-world DER integrated system, highlighting its ability to enhance system reliability, reduce downtime, and optimize energy management. This case study underscores the efficacy of EMD based anomaly detection in ensuring the reliability of DER integrated systems as they become an integral part of the modern energy landscape. © 2024 IEEE.
A Novel Deep Learning-based Landsat 7 ETM+ Multi-Spectral to Hyperspectral Reconstruction Model: Application for Water Bodies in an Indian Region
(Institute of Electrical and Electronics Engineers Inc., 2024) Saraah Imran; Subhojit Mandal; Ajanta Goswami; Mainak Thakur; Ashwani Raju
Hyperspectral (HS) remote sensing has the capacity to provide finer spectral information and better identification of objects while multispectral (MS) data are more readily available but with fewer bands. In absence of HS data, spectral reconstruction from MS to HS data can be considered in order to enhance the applicability of HS data. In this study, a deep learning-based Multi-Head Attention enabled Multi-Layer Perceptron (MHA-MLP) model is developed to reconstruct a scene of EO-1 Hyperion (HS) from a Landsat 7 ETM+ (MS) image. The reconstruction is also done using Multi-Layer Perceptron (MLP) and Transformer models. The reconstructed data from the three models is studied for water body locations in Betul, Madhya Pradesh, India. The results are analysed by comparative study of the spectra and calculation of standard statistical metrics. The reconstructed spectra from the MHA-MLP are found to follow the original HS spectra more closely than the other models and show the best values in the statistical metrics. Hence, The MHA-MLP model is found to be the best HS reconstructor model when compared to MLP and Transformer models. The reconstructed spectra are capable of capturing the 670 nm notch important for the study of chlorophyll concentration. This model can be used for water quality assessment applications and can also be extended for other applications. © 2024 IEEE.
A systematic analysis of machine learning algorithms for human emotion detection using facial expression
(American Institute of Physics Inc., 2024) Akhilesh Kumar; And Awadhesh Kumar
Nonverbal signs, which are extremely significant in human interaction, are conveyed through facial gestures. A significant feature in normal human-machine interfaces could be automated interpretation of facial expressions; it may also be used in primary care and social psychology. Due to the fact that humans immediately perceive facial movements for all intensive purposes, recognition of expressions by computer is indeed a task. A face expression may involve deformities of facial sections and their spatial relationships, along with changes in skin pigmentation, from the standpoint of automatic identification. When it comes to facial expressions, this study is dedicated to refining the process of identification of seven specific emotions (joy, sorrow, terror, rage, surprise, disgust and neutrality). The established approaches to the construction of emotion detection systems were examined centered on expressions of human face. This study assesses experimental studies and analytical articles for emotion recognition, as well as a variety of other approaches that have been applied or investigated. The findings of this study provide specific procedures for each category of expression and its severity, as well as a grading system for them. This article can be informative who use facial emotion assessment and interpretation and have to select a tool that is appropriate for their needs or make alternate choices. © 2024 Author(s).
A Transformer Based Emotion Recognition Model for Social Robots Using Topographical Maps Generated from EEG Signals
(Springer Science and Business Media Deutschland GmbH, 2024) Gosala Bethany; Manjari Gupta
Emotions are an integral part of living beings which influence their thoughts, actions, and interactions with other beings. Understanding human emotions is very important in communicating with others. Developing an emotion recognition model that can be implemented in robots is a critical step in human-robot interaction (HRI). With the rise of artificial intelligence, many techniques are available in machine learning and deep learning to solve this problem, one such technique is Transformers. Transformers, are used in trending technologies like BERT, ChatGPT, DALL-E-2, etc., We used transformers in this study as they have an edge over other by providing flexibility, adaptability, transfer learning, multimodality, parallelization etc., The dataset used is GAMEEMO, which contains EEG signals which are collected from 28 subjects while they were playing four computer-based games which emulate emotions like boring, calm, horror, and funny. Using EEG signal for emotion recognition have advantages like direct measure of brain activity, non-invasiveness, good temporal resolution etc., First, we preprocessed the raw EEG signal using bandpass filtering then created a 5-s epoch out of signal. Next, we converted the 1D EEG signal to a 2D topographical image using independent component analysis by taking 10 principal components out of 14 by persevering at least 95% of the variance in the data. From 9 h and 20 min of GAMEEMO EEG signal, we generated 82, 880 topographical images. Finally, these images were fed to a deep learning-based visual transformers model for the classification of emotions, the best accuracy of the model is 84.71%, our model performed better when compared with the other state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Addressing Blockchain Efficiency: A Study on Super Node-Based Consensus Mechanisms
(Institute of Electrical and Electronics Engineers Inc., 2024) Kamal Kant; Sarvesh Pandey; Udai Shanker
The success of applying blockchain technology for transaction processing is based on a consensus mechanism to be utilized. The new solutions for the same are being reported and claiming to fix or overcome it. However, the reported consensus protocols are not capable enough to serve the purpose adequately because the majority of widely used blockchain platforms purposefully exclude metrics like delay and throughput, which are essential for the efficiency of consensus protocols. Therefore, this paper discusses blockchain fundamentals, consensus mechanisms, their variants, and our super node concept is discussed, and we perform extensive experiments and our analysis preliminary results indicate that as the number of nodes increases, the throughput increases rapidly. © 2024 IEEE.
An Efficient Hybrid Algorithm with Novel Inver-over Operator and Ant Colony Optimization for Traveling Salesman Problem
(Springer Science and Business Media Deutschland GmbH, 2024) Dharm Raj Singh; Manoj Kumar Singh; Sachchida Nand Chaurasia
In this research paper, we present a hybrid algorithm that merges the principles of Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Our algorithm consists of two distinct stages. In the first stage, we employ Ant Colony Optimization to establish an initial population, and we utilize the proposed Inver-over (IO) heuristic to obtain suboptimal solutions for the Euclidean Traveling Salesman Problem (TSP). The proposed Inver-over operator is used to refine the solution obtained through ACO. Subsequently, this refined solution is incorporated into the Genetic Algorithm (GA) for the second stage. In the second stage of our algorithm, we apply GA with our proposed crossover operator and a 2-optimal heuristic to further refine the solution with the goal of achieving global optimality. To assess the effectiveness of our proposed algorithm, we rely on standard benchmark data from TSPLIB. The experimental results indicate that our hybrid algorithm outperforms recent methods and exhibits greater efficiency when compared to other reported methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Analysis of Non-isotropic Lorentz Invariance Violation for NOνA Experiment in Disappearance Channel
(Springer Science and Business Media Deutschland GmbH, 2024) Saurabh Shukla; Shashank Mishra; Lakhwinder Singh; Venktesh Singh
Lorentz Invariance Violation (LIV) is a trending topic of Beyond Standard Model Physics. Lorentz symmetry is tasted and well established in the low energy realm of Physics. But there are various theories which suggest its violation for Planck scale phenomenon. As neutrinos, having tiny mass, are the particle that are breaking down the barriers of Standard Model, they may be an excellent tool for searching such Planck-suppressed signals. In order to do this study, we have opted for Standard model extension as the theoretical framewok, which contains all Lorentz violating tems in it. We study the non-isotropic LIV which causes the sidereal effect in the neutrino beamline experiment. Neutrino disappearance channel is simulated for the NOνA far detector. We find that NOνA FD is highly sensitive for the LIV and new limits of LIV coefficients are also predicted. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Appraisal of awareness about single use plastic bags (supbs) among consumers and shopkeepers of Lanka market, Varanasi, Uttar Pradesh, India
(Institute of Physics, 2024) Harpreet Singh; Avadhesh Kumar Meena; Sanjana Sharma; Vineet Kumar
The awareness level of humans varies depending upon various known and unknown factors. Environmental awareness of an individual is the reflection of his/her thoughts, feelings and sensitivities towards the environment and its associated problems like degradation. It is the most important parameter for the measurement of implementation of government’s policies designed for the safeguard of environment and other related issues. The Indian Government decision for the complete ban on the Single-Use Plastic Bags (SUPBs) has been implemented in the whole country since 01 July 2022. Single-use plastic bags pose serious concerns for the environment and the economy of the country. The consumption as well as the disposal of plastic-related products is increasing day by day in India. Around 3.5 million tonnes of plastic waste are generated every year (CPCB 2020). The present paper is about the appraisal of the awareness level of consumers and shopkeepers about the use of plastic bags. In the present study, the primary survey is conducted to understand the awareness of consumers and shopkeepers regarding Single-Use Plastic Bags (SUPBs) in Lanka market, Varanasi, Uttar Pradesh. The study tries to relate the level of literacy, economic status, hesitationto carry own bags and demand for the plastic bags with the environment awareness level. Study reflects, there is a strong relationship between uses of plastic bags with economic, social, cultural, political status of the people. It also reflects the lack of enforcement of policies about the Single-Use Plastic Bags (SUPBs). © 2024 Institute of Physics Publishing. All rights reserved.
Bimetallic Copper/Zinc Metal Organic Framework-MoS2Nanohybrid based Electrochemical Sensor
(Institute of Electrical and Electronics Engineers Inc., 2024) Divya; Shubhangi; Pranjal Chandra
Acetaminophen is a globally used antipyretic analgesic drug to relieve pain. The excess usage of acetaminophen leads to various health implications including cardiovascular ailments, asthma, liver and kidney damage. Bimetallic MOFs are emerging materials in the field of electrochemical sensing domain utilizing the synergistic effect of both the metal ions present within. In this study, we report a sensing matrix comprising of an electrochemically fabricated novel bimetallic MOF (CoZn) conjugated with Mos2nanosheets to form an electroconductive nanocomposite. Layer-by-layer characterization of CoZn-MOF/MoS2modified electrode surface was done through different electrochemical analytical techniques like CV and EIS. The composite (GCE/CoZn-MOF/MoS2) can find its applications in sensing a plethora of analytes based on the catalytic potential of the metal nodes. In this work we have attempted the application of the developed nanocomposite probe in the electrochemical oxidation and thereby detection of acetaminophen. The developed nanocomposite was able to detect acetaminophen with enhancement in signal, proving the improved electroconductivity of the surface due to synergistic effect of CoZn-MOF and Mos2. © 2024 IEEE.
Comparative Analysis of ELM and Sparse Bayesian ELM for Healthcare Diagnosis
(Springer Science and Business Media Deutschland GmbH, 2024) Vivek Singh; Abhishek Pandey; Jyoti Singh Kirar
Extreme Learning Machine is a popular technique that became a center for research because of its easier implementation. It is s faster machine learning algorithm that has shown to have higher accuracy in various classification problems and has proven to be less time-consuming than traditional neural networks. But it also suffers from various drawbacks which are resolved with the help of the Bayesian paradigm along with the use of a Bayesian system known as Automatic Relevance Determination. We have tested how it performs on 3 different kinds of datasets. Various metrics are computed for all the 3 datasets and compared with the results from the conventional ELM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Comparative Analysis of YOLO Models for Plant Disease Instance Segmentation
(Institute of Electrical and Electronics Engineers Inc., 2024) Agamjot Singh; Aryan Yadav; Anshul Verma; Prashant Singh Rana
Accurate detection and segmentation of plant diseases are essential for sustaining agricultural productivity and global food security. This report conducts a comparative analysis of three state-of-the-art YOLO models-YOLOv5, YOLOv7, and YOLOv8-emphasizing their efficiency in instance segmentation of plant diseases. The research employs a comprehensive dataset featuring various crops like rice, sugarcane, wheat, bell pepper, potato, and tomato, each suffering from different diseases. The methodology includes fine-tuning YOLO models with pretrained weights and optimizing them with the curated dataset.The assessment of performance is based on crucial metrics such as recall, mean average precision (MAP) and precision. YOLOv8 exhibits superior performance, with over 9 0 % average precision across all disease categories, significantly outperforming YOLOv5 and YOLOv7. This report offers detailed insights into the architectural features, training procedures, and evaluation metrics of each model. It also addresses the implications of the findings for practical agricultural applications, highlighting the role of advanced deep learning techniques in improving crop protection and management strategies. Despite the positive results, the report acknowledges limitations like dataset dependency and challenges in real-world deployment. © 2024 IEEE.
Comparisons of Biomechanical Properties of Intact and Implanted Level in Pedicle Section of Patient-Specific Cervical Spine: An In-Silico Study
(Springer Science and Business Media Deutschland GmbH, 2024) Ram Kumar; Amit Kumar; Shabnam Kumari
Cervical pedicle screw-rod fixation is a challenging technique for spinal surgery due to its ability to provide better stabilization of the spine in treating traumatic injuries, degenerative changes, and orthopaedic and oncological diseases. Pedicle screw-rod fixation can increase the mechanical strength of the spine by using small diameter screw implants that are accurately placed. Pedicle screw has minimally invasive techniques, which results in smaller incisions and less pain for the patient. The goal of this research is to comprehend the comparisons of Range of motion (ROM), stress on the implant and the bone next to it of screw-rod in pedicle section of cervical spine during loadings of flexion–extension. CT scan data is used to develop a 3-D cervical spine FE model (C2–C7 vertebrae), pedicle screw is formed in SOLIDWORKS and single level pedicle screw-rod fixation at C5–C6 vertebrae is used in ANSYS 17.2. C2 vertebrae are subjected to a 53-N compressive force and a 1-Nm moment, and C7 is fixed. The ROM is reduced by 78% at C5-C6 level for screw-rod compared with intact spine during flexion–extension loading conditions. The ROM slightly increases at the adjacent levels of cervical spine. During the flexion–extension of pedicle screw-rod, the highest stresses developed in PEEK screw-rod implants range from 21 to 26 MPa, these values fall below the yield stress of the material. The pedicle screw-rod operation decreased range of motion at fixation segment and increased ROM to some extent at adjacent segment for flexion–extension motions as compared to intact cervical spine. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Comprehensive assessment of subsidence in Eastern Gangatic plain region with relation to groundwater storage change and seasonal derivatives
(Institute of Electrical and Electronics Engineers Inc., 2024) Praveen Kumar Kannojiya; Ashwani Raju
Subsidence is a critical and emerging geo-hazard prominent in metropolitan hubs and coastal areas due to exponential unscientific extraction of groundwater and climate change. The Eastern Gangatic plains (EGP) has dynamic geomorphology stating with alluvial deposits from large streams and host prolific aquifer system. An exponential increase in population over the years in the region had created a metropolitan hub leading to over drafting of groundwater subsequently inducing subsidence in the urban region. With advancement in satellite microwave remote sensing in subsidence monitoring at global and regional scale has been quite efficacious. To understand the dynamics of the groundwater change with that of subsidence along with the seasonal influence, Satellite data analysis is carried out using Sentinal 1-A and GRACE in order to monitor the rate and evolutionary pattern of emerging land subsidence. This study explores the multi-temporal analysis of 192 Sentinel-1A SAR scenes acquired between February 2017 and August 2023 and GRACE data from 2003 to 2023. The SAR dataset were processed using Persistent Scatterer Interferometry (PSI), an advanced time series synthetic aperture radar technique (InSAR) to identify the potential subsidence hotspots. The potential area under extreme subsidence are classified into 13 blocks. The results shows the range of rate of subsidence to be -2.9 to 5.1 mm/yr. The mean cumulative subsidence is ~3 cm for the blocks which is in line with the declining groundwater storage trend. Results depicts that the unconfined aquifer is under significant stress and is experiencing a progressive loss of storage capacity over time, according to the discovered displacement trends, which also considerably match the city's ongoing decline in groundwater levels. © 2024 IEEE.
Deep Learning Models for Predicting Cognitive Impairment in Parkinson's Disease Detection
(Institute of Electrical and Electronics Engineers Inc., 2024) Shraddha Jain Sharma; Ratnalata Gupta
A chronic neurodegenerative disorder affecting the motor system is called Parkinson's disease. Cell degeneration results from it over time as it advances slowly. This is one of the most prevalent diseases in society and is difficult to diagnose. The body experiences both motor and non-motor deficits (such as smell and speech) as a result of a dopamine cell shortage in the brain. Speech problems are common among Parkinson's sufferers, as is well documented. When compared to normal individuals, speech signals from Parkinson's sufferers show notable changes. This study proposes to use voice signals' acoustic properties to classify Parkinson's diseases using a deep learning-based approach. First, a genetic algorithm is used for the auditory characteristics in order to identify useful aspects. The ReliefF feature selection approach and the genetic algorithm's performance are also contrasted. The second phase involves feeding the chosen characteristics into the Convolutional Neural Network (CNN) architecture that has been created. An accuracy of 93.29% is attained without feature selection, whereas 97.62% is attained with feature selection. © 2024 IEEE.
Disinvestment and Technical Efficiency of State-Owned Utility Sector Enterprises in India
(Springer Nature, 2024) Shrabana Tripathi; Bhanu Pratap Singh
The Indian utility sector is currently experiencing a phase of disinvestment, driven by various factors like advancements in technology, the ever-changing landscape of global energy markets, shifts in governmental policies, and evolving consumer preferences. In light of these circumstances, it becomes crucial to assess the utility sector’s condition and its existing efficiency level. The present study primarily examines the repercussions of disinvestment on the technical efficiency of the state-owned utility sector enterprises in India during the period spanning from 2003 to 2023. To gauge the technical efficiency, the study employs the stochastic frontier analysis (SFA). The study also investigates how factors such as the size of the firm, its age, and the disinvestment process influence inefficiencies. The findings of the study indicate that among the considered factors, the size of the firm holds the most substantial sway on inefficiency levels, while the age of the firm and the occurrence of disinvestment do not exhibit a significant impact. On average, the utility sector enterprises within the sample demonstrated a technical efficiency of around forty-five percent. This statistic underscores the need for more comprehensive and profound structural reforms to enhance economic outcomes in the sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
District-wise rainfall trend analysis by using non-parametric approach: A case of the State of Haryana, India
(Institute of Physics, 2024) Manoj Panwar; Chetna Rathee; Harsimran Kaur
The current water usage in different sectors and climate change have created pressure on the universal availability of essential resources for life. Rainfall is an integral part of the entire water cycle. Planning for sustainable water management requires proper forecasting of Rainfall, which further involves trend analysis. Mann-Kendall analysis and Sen Slope estimation are established non-parametric tests for trend forecasting in hydrological data. The present research analysed the monthly and annual rainfall data of all districts of the state of Haryana for 1991-2019 using Mann Kendall and Sen Slope techniques at 95%, 90% and 80% significance level. The results show that overall rainfall in Haryana has a decreasing trend. The decreasing trend is more prominent for February, June, July and August. August has the highest number of districts, showing a negative direction. Only a few sections show an increasing trend at a low significance level. The researchers suggest implementing stringent and integrated sustainable water management policies in the state of Haryana for water security in future in light of climate change. © Published under licence by IOP Publishing Ltd.
Effect of Low Versus High-Intensity Exercise on Renal Biomarkers in Athletes: A Comparative Study
(Institute of Electrical and Electronics Engineers Inc., 2024) Pradeep Singh Chahar; Jagdeep Singh
Exercise offers significant health benefits, promoting normal body system functioning, healthy growth, development, and overall well-being. The objective of the present study was to determine the effect of high and low intensity exercise on renal biomarkers acute response in athletes. This cross sectional study was carried out in the department of physical education, Banaras Hindu University, Varanasi (UP). A total of 20 healthy male athletes with age range from 22 to 26 years were randomly selected. All subjects performed high and low intensity exercise for 5 minutes with a gap of 7 days. Blood samples for selected renal biomarkers were collected at pre-exercise, immediately after exercise, 10 and 20 minutes after the high and low intensity exercise. The serum urea and creatinine count decreased significantly after high-intensity exercise compared to low-intensity exercise (p<0.05). When compared to the baseline, this effect was still observable after ten and twenty minutes of passive recovery (p<0.05). There was a substantial increase in serum sodium and potassium from baseline immediately after exercise (p<0.05), after ten and twenty minutes of passive recovery without reaching to baseline. When the same subjects were exposed to different exercise intensities, the acute and short-term effects of exercise on renal biomarkers were intensity-dependent immediately after exercise, after ten and twenty minutes of passive recovery. © 2024 IEEE.
Effect of Multidirectional Forging on Mechanical and Tribological Behavior of Hypereutectic Al–20wt.% Si Alloy
(Springer Science and Business Media Deutschland GmbH, 2024) Ishwari Narain Choudhary; Nitesh Kumar Sinha; Manik Mahali; Jayant Kumar Singh
Enhancement in the material properties for a specific application is of major concern in all types of industries, e.g., automobile, aerospace, marine, etc., grain size plays an important role in affecting the mechanical and tribological properties of metals and alloys. The present work investigates how the hyper-eutectic Al–Si alloy properties can be affected by the application of multidirectional forging (MDF). Hyper-eutectic Al–Si alloy containing 20%Si was prepared by casting and a 40% reduction in height in each direction at 300 ± 10 ℃ was given using a power hammer machine during forging. The results show that mechanical and tribological properties such as tensile, hardness, density, porosity, and wear properties improved. The microstructural improvement from dendrites to fine grain size also happened and was uniformly distributed after multidirectional forging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.