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Understanding feminism on online platforms: Exploration and analysis of two online platforms
This article explores how feminism is practised and communicated on digital plat-forms. Feminism in India and Khabar Lahariya are two online platforms studied with interviews of respondents to understand how the online spaces are used for knowledge sharing that take feminist perspective. New media opened up spaces for people to communicate from any part of the world, create media content and circulate it. Visibility, privacy, accessibility and risks are negotiated by the report-ers and content creators to produce alternative cultural production from an inter-sectional feminist standpoint. 2022 Intellect Ltd Article. -
The Impact and Inheritance of Operating Leverage: A Study with Two Pharmaceutical Companies
International Journal of Management Research and Technology, Vol-7 (2), pp. 145-154. ISSN-0974-3502 -
LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population
Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data. The LRE-MMF method utilizes the complementary strengths of sMRI and rs-fMRI: sMRI provides detailed anatomical information, while rs-fMRI captures functional connectivity patterns. We applied this approach to a dataset consisting of 20 PD patients and 20 healthy controls (HC), all scanned with a 3 T MRI. The primary objective was to determine whether the integration of sMRI and rs-fMRI through the LRE-MMF method improves the classification accuracy between PD and HC subjects. LRE-MMF involves the division of imaging data into localized regions, followed by feature extraction and dimensionality reduction using Principal Component Analysis (PCA). The resulting features were fused and processed through a neural network to learn high-level representations. The model achieved an accuracy of 75 %, with a precision of 0.8125, recall of 0.65, and an AUC of 0.8875. The validation accuracy curves indicated good generalization, with significant brain regions identified, including the caudate, putamen, thalamus, supplementary motor area, and precuneus, as per the AAL atlas. These results demonstrate the potential of the LRE-MMF method for improving early diagnosis and understanding of PD by effectively utilizing both sMRI and rs-fMRI data. This approach could contribute to the development of more accurate diagnostic tools. 2024 The Authors -
Machine Learning-based Currency Information Retrieval for Aiding the Visually Impaired People
Paper currency is one of the most in-demand and long-established payment modes across the globe. People suffering from visual disabilities often face difficulties while handling paper currencies. Over the years, assisting technology has been rekindling itself to serve the aged and disabled person more aptly. Image processing methods and other sophisticated technologies, like Artificial Intelligence, Deep Learning, etc., can be employed to identify banknotes and fetch other valuable pieces of information from them. This paper proposes a framework that focuses on an integrated approach to retrieving data from the paper currency's uploaded image. The current version of the framework focuses on identifying the authenticity of the paper currency and classifying it according to its value. This work is an initiative to help visually impaired people to use paper currencies without assistance from other individuals and support them in living independently. 2021 IEEE. -
Classification of Soil Images using Convolution Neural Networks
Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. 2021 IEEE. -
An AI Approach to Pose-based Sports Activity Classification
Artificial intelligence systems have permeated into all spheres of our life-impacting everything from our food habits to our sleep patterns. One untouched area where such intelligent systems are still in their infancy is sports. There has not been enough indulgence of AI techniques in sports, and most of the works are carried on manually by coaching staff and human appointees. We believe that intelligent systems can make coaching staff's work easier and produce findings that the human eye can often overlook. Here, we have proposed an intelligent system to analyze the beautiful game of tennis. With the use of computer vision architecture Detectron2 and activity-based pose estimation and subsequent classification, it can identify an action from a tennis shot (activity). It can produce a performance score for the player based on pose and movement like forehand and backhand. It can also be used to understand and evaluate the strengths and weaknesses of the player. The proposed approach provides a piece of valuable information for a player's performance and activity detection to be used for better coaching. The study achieves a classification accuracy of 98.60% and outperforms other SOTA CNN models. 2021 IEEE -
Enhancement of Image Classification Using Transfer Learning and GAN-Based Synthetic Data Augmentation
Plastic bottle recycling has a crucial role in environmental degradation and protection. Position and background should be the same to classify plastic bottles on a conveyor belt. The manual detection of plastic bottles is time consuming and leads to human error. Hence, the automatic classification of plastic bottles using deep learning techniques can assist with the more accurate results and reduce cost. To achieve a considerably good result using the DL model, we need a large volume of data to train. We propose a GAN-based model to generate synthetic images similar to the original. To improve the image synthesis quality with less training time and decrease the chances of mode collapse, we propose a modified lightweight-GAN model, which consists of a generator and a discriminator with an auto-encoding feature to capture essential parts of the input image and to encourage the generator to produce a wide range of real data. Then a newly designed weighted average ensemble model based on two pre-trained models, inceptionV3 and xception, to classify transparent plastic bottles obtains an improved classification accuracy of 99.06%. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Unleashing human potential: Integrating cognitive behavioral neuroscience into HR strategies
The world of work is transforming, driven by insights from the frontiers of science. Human resource (HR) practices are no longer limited to traditional methods and increasingly incorporate knowledge from disciplines like Cognitive Behavioral Neuroscience (CBN). By understanding how our brains work, we can design HR practices that enhance employee well-being, engagement, and, ultimately, performance. Drawing from neuroscientific research on decision-making, communication, stress, learning, motivation, and workplace design, this chapter delves into the intersection of CBN and HR, offering evidence-based practices that support a thriving workforce. This interdisciplinary approach holds promise for maximizing human potential in the context of the modern workplace. 2024 by IGI Global. All rights reserved. -
The red terror and a state of uncertainty: United Nations' role In the Indian maoist struggle
In this paper, the authors argue that the long drawn armed conflict between the Maoists and the Indian State has acquired the status of a non-international armed conflict due to the organized nature of the Maoists and the scale of violence arising out of the conflict. The systematic human rights abuses by both parties and forceful displacement of civilians is a tangible threat to international peace and security in the region. In light of the deadlock between the parties, the authors make a case for United Nations' intervention in mediating an end to the conflict and restoring peace and security in the region. Drawing inspiration from the role played by the UN in ending civil wars across the globe, this paper argues for a similar intervention in the non-international armed conflict in India. The authors argue that the UN should venture to exert pressure on the State to eliminate any further abuses of human rights, and remove the impasse between both the parties to facilitate a constructive dialogue. Copyright 2012 De Gruyter. All rights reserved. -
Intelligent Agents System for Vegetable Plant Disease Detection Using MDTW-LSTM Model
When it comes to agricultural output, nation, India, ranks first in the world, and agriculture is unparalleled. The need to categorize and trade agricultural goods is paramount. Manual organization, which is tedious and laborious, is not a choice. When agricultural products are graded automatically, a lot of time is saved. The application of image processing techniques facilitates the examination and evaluation of the products. A technique for identifying diseased vegetables is the focus of this effort. Feature extraction, preprocessing, segmentation, and training the model are all heavily dependent on sequence. Among the preprocessing technologies at disposal are image segmentation and filtering. Using Kapur's thresholding based segmentation method, the image's sick areas can be located during the segmentation process. Use k-means clustering for feature extraction to identify vegetable plant diseases. The training of an MDTW-LSTM model relies heavily on feature selection. In terms of performance, the proposed method surpasses two cutting-edge algorithms: LSTM and DTW. The results showed an accuracy of 97.35 percent, indicating a remarkable improvement. 2024 IEEE. -
Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme Learning Machine
Through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. The solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India's harvest every year. The agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. This proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. That improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. The GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. The proposed approach to outperforms other two models such as CNN and ELM. 2023 IEEE. -
Hybrid Subset Feature Selection and Importance Framework
Feature selection algorithms are used in high-dimensional data to remove noise, reduce model overfitting, training and inference time, and get the importance of features. Features subset selection is choosing the subset with the best performance. This research provides a Hybrid subset feature selection and importance (HSFSI) framework that provides a pipeline with customization for choosing feature selection algorithms. The authors propose a hybrid algorithm in the HSFSI framework to select the best possible subset using an efficient exhaustive search. The framework is tested using the Bombay stock exchange IT index's companies' data collected quarterly for 16 years consisting of 71 financial ratios. The experimental results demonstrate that models created using 12 features chosen by the proposed algorithm outperform models with all features with up to 6% accuracy. The importance-based ranks of all features are generated using the framework calculated using 13 implemented feature selection techniques. All selected feature subsets are cross-validated using prediction models such as support vector machine, logistic regression, KNeighbors classffier, random forest, and deep neural network. The HSFSI framework is available as an open-source Python software package named ''feature-selectionpy'' available at GitHub and Python package index. 2023 IEEE. -
Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review
This paper systematically reviews the literature related to stock price prediction systems. The reviewers collected 6222 research works from 12 databases. The reviewers reviewed the full-text of 10 studies in preliminary search and 70 studies selected based on PRISMA. This paper uses the PRISMA-based Python framework systematic-reviewpy to conduct this systematic review and browser-automationpy to automate downloading of full-texts. The programming code with comprehensive documentation, citation data, input variables, and reviews spreadsheets is provided, making this review replicable, open-source, and free from human errors in selecting studies. The reviewed literature is categorized based on type of prediction systems to demonstrate the evolution of techniques and research gaps. The reviewed literature is 7 % statistical, 9% machine learning, 23% deep learning, 20% hybrid, 25% combination of machine learning and deep learning, and 14% studies explore multiple categories of techniques. This review provides detailed information on prediction techniques, competitor techniques, performance metrics, input variables, data timing, and research gap to enable researchers to create prediction systems per technique category. The review showed that stock trading data is most used and collected from Yahoo! Finance. Studies showed that sentiment data improved stock prediction, and most papers used tweets from Twitter. Most of the reviewed studies showed significant improvements in predictions to previous systems. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Tuning the output of the higher plants Circadian Clock
The circadian clock is an ascribed regulator found in the cells of creatures, that keeps biological and behavioral processes in stnc with dailt environmental changes throughout the 24-hour ctcles. When the circadian clock in humans malfunctions or is misaligned with environmental signals, the timing of the sleep-wake ctcle is altered and several circadian rhtthm sleep disorders result. Due to the Earth's rotation on its axis, predictable environmental changes are anticipated bt complex processes. The combined term for these ststems is the circadian clock. The circadian rhtthm regulates photostnthesis and photoperiodism, making it the "primart controller of plant life." The circadian clock is made up of post-translational alterations to core oscillators, epigenetic tweaks to DNA and histones, and auto regulatort feedback loops in transcription. In addition, the circadian clock is cell-autonomous and regulates the circadian rhtthms of distinct organs. Biochemical elements such as photostnthetic products, mineral nutrients, calcium ions, and hormones are used bt the core oscillators to communicate with one another. Arabidopsis is utilized to identift clock-related genes that govern plant growth, germination, pollination, flowering, abiotic and biotic stress responses, and more. The biological ctcles of all species, notablt humans, are undoubtedlt impacted bt other elements, including high altitude and changing ecoststems, in addition to the ones alreadt stated. Although it hasn't tet published ant experimental or scientific evidence to support them, the implication that living things have lives does appear inescapable. Hence, the present studt elaborates on the higher plants related to the circadian clock. The Author(s). -
Influence of Coronavirus Disease 2019 on human biological timekeeping
To stay in sync with environmental cues, the body's metabolic activities must be rhythmic, and these rhythmic functions are known as circadian rhythms, which repeat every 24 h. People's sleep-wake and eating patterns were interrupted as a result of house confinement, making them more vulnerable to noncommunicable chronic diseases during the coronavirus disease 2019 (COVID-19) period. During the epidemic, there was a greater degree of misalignment with this synchronization. The effects of severe acute respiratory syndrome coronavirus 2 (COVID-19) on the human circadian clock are studied in depth. The literature review was conducted fully online, with the website utilized to collect all of the papers from PubMed, and duplicates were handled only in the first phase. Researchers found that individuals of all ages who are pushed to adjust their daily routines shift to the later chronotype, resulting in lifestyle modifications and an altered biological timing system that contributes to noncommunicable chronic illnesses. Chronic illnesses have bidirectional conductance, which means they can be caused by both environmental and self-modification in daily activity, as was the case during the COVID-19 outbreak, which forced people to stay at home. This review comes to the conclusion that fighting the pandemic may be best done by changing medications and focusing on immune health. 2023 Wolters Kluwer Medknow Publications. All rights reserved. -
Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities
The use of intelligence in manufacturing has emerged as a fascinating subject for academics and businesses everywhere. This book focuses on various manufacturing operations and services which are provided to customers to achieve greater manufacturing flexibility, as well as widespread customization and improved quality with the help of advanced and smart technologies. It describes cyber-physical systems and the whole product life cycle along with a variety of smart sensors, adaptive decision models, high-end materials, smart devices, and data analytics. Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities focuses on Intelligent Manufacturing and the design of smart devices and products that meet the demand of Industry 4.0, manufacturing and cyber-physical systems, along with real-time data analytics for Intelligent Manufacturing. The usage of advanced smart and sensing technologies in Intelligent Manufacturing for healthcare solutions is discussed as well. Popular use cases and case studies related to Intelligent Manufacturing are addressed to provide a better understanding of this topic. This publication is ideally designed for use by technology development practitioners, academicians, data scientists, industry professionals, researchers, and students interested in uncovering the latest innovations in the field of Intelligent Manufacturing. Features: Presents cutting-edge manufacturing technologies and information to maximise product exchanges and production Discusses the improvement in service quality, product quality, and production effectiveness Conveys how a manufacturing companys competitiveness can increase if it can manage the turbulence and changes in the global market Presents how intelligence production is essential in Industry 4.0 and how Industry 4.0 offers greater manufacturing flexibility, as well as widespread customisation, improved quality, and increased productivity Covers the ways businesses handle the challenges of generating an increasing number of customised items with quick time to market and greater quality Includes popular use cases and case studies related to intelligent manufacturing to provide a better understanding of this discipline. 2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors. -
An Assessment of Farmers' Perception and Adaptive Capacity for Climate Change
In the past decades, various regions in U.P. had experienced severe floods. The effects of climate change also affected agricultural production. This study investigated the farmers' perception of climate change and suggested strategies for mitigating its effects using a primary survey with the help of a pre-structured schedule. Change in rainfall pattern, problems in seed quality, the emergence of new pests and diseases, changes in the crop cycle were the few effects that farmers' perceived due to climate change. Even the most mitigation efforts by the farmers cannot prevent some of the impacts of climate change within the following decades. It makes adaptation a must-have for addressing these impacts. 2022, The Society of Economics and Development. All rights reserved. -
Studies on phase transitions and dielectric properties of biowaste synthesized porous carbon nanoparticlesferroelectric liquid crystal mixture
Ferroelectric liquid crystals(FLCs), an exciting class of liquid crystals(LCs), found potential applications in the display as well as non-display regimes due to their fast response, low driving voltage and nonvolatile memory. The amalgamation of nanoparticles into FLCs has opened up new avenues in the LCs research field by alterations/modification of the existing properties of LCs. In this work, porous carbon nanoparticles (PCNPs) were dispersed into FLC mixture (W206E) and investigated their doping effect on FLCs textural, phase transition temperatures and dielectric studies in planar-aligned cells. Dielectric spectroscopy was carried out in the frequency range of 20 Hz to 10 MHz to explore the frequency as well as the temperature dependent of FLC in the entire SmC* region. The transition temperature of FLC mixture is increased by 4 C in PCNPs doped FLC sample then undoped FLC sample. Nearly 8.42% increase in permittivity is observed. A Gold stone relaxation mode at ?627 Hz was observed at lower frequency. 2024 Taylor & Francis Group, LLC. -
Financing for SDGs in India in Post Pandemic era - Challenges & Way forward
In 2015, a resolution known as Agenda 2030 was passed by United Nations General Assembly in which seventeen goals for Sustainable Development were laid down for global dignity, peace and prosperity. The post- pandemic era became full of uncertainties in pursuing those Sustainable Development Goals (SDGs) and its implementation became a challenge especially for the developing economies like India. The country is facing a tremendous gap in arranging for resources to meet the climatic changes and attaining the SDGs. India requires 170 billion dollars per year from 2015-2030 to fulfill the Sustainable Development Goals as per the estimation done by National Determined Contribution, a body setup after Paris agreement 2015 to monitor the efforts of the country towards reducing national emissions and adapting to climate change. There is a huge concern amongst the various agencies on exploring the ways to fill this financing gap especially after the economic slowdown seen in the post pandemic era. This research paper analyses the challenges imposed by the COVID 19 pandemic on financing for SDGs and also explores the options to mitigate them. The articles and research papers related to SDG financing are reviewed by the researchers to arrive at the above mentioned statements. This paper is an attempt to draw the attention of worldwide authorities towards this grim situation as sustainable finance is far from reality in India and requires immediate up scaling. The Electrochemical Society