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Deep Learning-Based Prediction of Physical Activity Intensity for Athletes
Maximizing training plans for athletes and lowering the risk of injury depends on a precise assessment of the degree of physical activity. Existing system in-use systems often employ simplistic models, which leads to inaccurate projections. The paper presents a deep learning-based system that uses convolutional neural networks (CNNs) to create real-time predictions using wearable sensor data. Because it automatically extracts relevant features from raw sensor data, the technique does not need human feature engineering. Utilizing thorough model training and evaluation, it exceeded the most recent methods in terms of accuracy (0.92), precision (0.90), recall (0.92), F1-score (0.91), and ROC AUC (0.94). Results of cross-validation over many data subsets confirm the resilience of the method. Comparisons of confusion matrices also demonstrate how effectively the algorithm forecasts various activity intensities. Overall, the proposed system represents a breakthrough in accurately estimating how much physical activity athletes do, enhancing the efficacy of their training, and reducing the possibility of damage in sporting settings. 2024 IEEE. -
Deep learning: Research and applications
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. Tutorials on deep learning framework with focus on tensor flow, keras etc. Numerous worked out examples on real life applications Illustrative diagrams and coding examples. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Deep neural network architecture and applications in healthcare
Gaining insights related to medical data has always been a challenge, as limited technology delays treatment. Various types of data are collected from the medical field, such as sensor data, that are heterogeneous in nature. All of these are very poorly maintained and require more structuring. For this reason, deep learning is becoming more and more popular in this area. There are many challenges due to inadequate and irrelevant data. Insufficient domain knowledge also adds to the challenge. Modern deep learning models can help understand the dataset. This chapter provides an overview of deep learning, its various architectures, and convolutional neural networks. It also highlights how deep learning technologies can help advance healthcare. 2022 River Publishers. -
Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications
This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premiseenabling machines to learn optimal actions within complex environments through trial and errorhas broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you're an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency. 2024 Scrivener Publishing LLC. -
Deep vs. Shallow: A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection
Internet explosion and penetration have amplified the fake news problem that existed even before Internet penetration. This becomes more of a concern, if the news is health-related. To address this issue, this research proposes Content Based Models (CBM) and Feature Based Models (FBM). The difference between the two models lies in the input provided. The CBM only takes news content as the input, whereas the FBM along with the content also takes two readability features as the input. Under each category, the performance of five traditional machine learning techniques: - Decision Tree, Random Forest, Support Vector Machine, AdaBoost-Decision Tree and AdaBoost-Random Forest is compared with two hybrid Deep Learning approaches, namely CNN-LSTM and CNN-BiLSTM. The Fake News Healthcare dataset comprising 9581 articles was utilized for the study. Easy Data Augmentation technique is used to balance this highly imbalanced dataset. The experimental results demonstrate that Feature Based Models perform better than Content Based Models. Among the proposed FBM, the Hybrid CNN - LSTM model had a F1 score of 97.09% and AdaBoost-Random Forest had a F1 Score of 98.9%. Thus, Adaboost-Random Forest under FBM is the best-performing model for the classification of fake news. 2013 IEEE. -
DeepBBBP: High Accuracy Blood-brain-barrier Permeability Prediction with a Mixed Deep Learning Model
Blood-brain-barrier permeability (BBBP) is an important property that is used to establish the drug-likeness of a molecule, as it establishes whether the molecule can cross the BBB when desired. It also eliminates those molecules which are not supposed to cross the barrier, as doing so would lead to toxicity. BBBP can be measured in vivo, in vitro or in silico. With the advent and subsequent rise of in silico methods for virtual drug screening, quite a bit of work has been done to predict this feature using statistical machine learning (ML) and deep learning (DL) based methods. In this work a mixed DL-based model, consisting of a Multi-layer Perceptron (MLP) and Convolutional Neural Network layers, has been paired with Mol2vec. Mol2vec is a convenient and unsupervised machine learning technique which produces high-dimensional vector representations of molecules and its molecular substructures. These succinct vector representations are utilized as inputs to the mixed DL model that is used for BBBP predictions. Several well-known benchmarks incorporating BBBP data have been used for supervised training and prediction by our mixed DL model which demonstrates superior results when compared to existing ML and DL techniques used for predicting BBBP. 2022 Wiley-VCH GmbH. -
Defect originated photoluminescence tuning of silica nanoparticles prepared by electron beam irradiation and their applications
Considering the imminent importance of Silica (SiO2) nanoparticles (NPs), a highly rapid and one-pot scalable approach is being reported for their preparation. Electron-beam was used to derive the formation of SiO2 NPs, while in situ functionalization was carried out by ?-Cyclodextrin (?-CD). XRD pattern of as prepared ?-CD functionalized SiO2 NPs (i.e., ?-CD@SiO2 NPs) revealed their amorphous nature, while imaging studies showed self-assembling of NPs into a porous structure. UVvisible absorption spectra showed multiple peaks at 233, 323, 390 and 455 nm, which signifies the presence of different kind of defects in the as prepared NPs. An interesting aspect of this work is tuning of the photoluminescent properties of NPs from blue to green by simply varying the absorbed dose. This could be attributed to the formation of a particular kind of defects at a proportionate absorbed dose. These defects act as emission centers (ECs) and were analysed through steady state and time-resolved emission studies. Notably, ?-CD played significant role in influencing the composition of the NPs, whilst enhancing their colloidal stability and quantum yield. The prospective applications of ?-CD@SiO2 NPs were explored in latent fingerprinting and thermosensing. 2020 Elsevier Ltd and Techna Group S.r.l. -
DeFi's transformative influence on the global financial landscape
The rise of decentralized finance (DeFi) has fundamentally reshaped the financial industry, challenging traditional banking systems and opening up a world of possibilities in global finance. This chapter explores the multifaceted impact of DeFi on the global economic landscape, addressing critical themes through a series of subtitles. DeFi is disrupting traditional banking models by offering alternative financial services directly on blockchain networks, such as lending, borrowing, and trading. One of the remarkable achievements of DeFi is its ability to provide financial services to previously underserved and unbanked populations. Tokenization is a crucial aspect of DeFi, enabling the representation of real-world assets as digital tokens on the blockchain. DeFi offers numerous advantages but poses security challenges, including smart contract vulnerabilities and hacks. This chapter provides an overview of the major themes and implications of DeFi's influence on finance, highlighting its opportunities and challenges. 2024, IGI Global. All rights reserved. -
Defiance in the Shadows: Flames of Resilience in the Selected North Korean Memoirs
The resilient autobiography focuses on the interpersonal dynamics of life narratives, including the relationships that have exacerbated the hardships described and the ones that have provided the support and strength necessary to overcome them. The selected text for this paper is A Thousand Miles to Freedom: My Escape from North Korea by Eunsun Kim and Sebastien Falletti and In Order to Live: A North Korean Girls Journey to Freedom by Yeonmi Park and Maryanne Vollers. These two texts talk about their catastrophic journey from North Korea because of poverty caused by famine and their migration to China, where they were trafficked and subjected to humiliation and their final escape to South Korea. The memoirs depict the individual?s embodiment of resilience as they narrate their own struggles and victories in overcoming hardship. Resistance to adversity and suffering, as well as the ability to bounce back from painful experiences in one?s own life and in the lives of others, are the hallmarks of resilience. Trauma becomes ingrained in attempts for survival in both memoirs, which illustrate the catastrophic impacts of famine, relocation, and personal loss. One effective approach to enhance resilience is reorganizing and reestablishing control over one's life after a traumatic event. Interpretations and writings of the personal narrative are offered from both the subject?s and an outsider?s points of view. Thus, the life story is formed in a dual sense: autobiographically and biographically. 2024 Sciedu Press. All rights reserved. -
Defluoridation of Drinking WaterFluoride Wars
Fluorine is also known as two-edged sword. At lower doses, it influences tooth by inhibiting tooth caries, while in high doses, it causes dental and skeletal fluorosis. It is known that some quantity of fluoride is important for the formation of tooth enamel and mineralization in tissues. The present work aims at providing safe and potable water to rural areas where this element has created a menace. This work also suggests the use of few adsorbents such as paddy husk and coir pith which are affordable and removes fluorine to greater extent. The study concludes that materials which are used as adsorbents and can be safely inculcated as fluorine removal adsorbents which help people to have safe potable water. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deforestation, Climate Change and the Sustainability of Agriculture: A Review
This study aims to survey the literature and factual evidence on the nexus between deforestation and agriculture through an assessment of the potential impacts of climate change in the context of the world, India, and the Western Ghats. The Western Ghats region was chosen for this study because of its deep ecological significance. A few underlying themes were created and findings were documented under each theme that ranged from the causes of deforestation, the transformation of forest land for agriculture, the nexus between agriculture, deforestation and climate change, climate-driven agricultural vulnerability and the reconciliation of forest protection with agriculture. These findings suggest that shifting agriculture has been a dominant source of deforestation. The primary climatic impacts on agriculture are seen through crop yield falls. Indias arid and semiarid tropical regions have witnessed high climate-driven agricultural sensitivity. This could be on account of the fact that Indias tropical forests have witnessed high deforestation. The presence of higher tree densities in areas under Joint Forest Planning and Management in the Western Ghats create the potential for sparing remaining land areas for non-forest uses such as agriculture. 2024, Editorial office of Journal of Resources and Ecology. All rights reserved. -
Deformation Diagnostic Methods for Transformer Winding through System Identification
Transformers play a critical role in the power system. Dynamics of the power system changes if the transformers are out of service for scheduled and unscheduled maintenance work under contingency situations. Faults, overloading, and mechanical abnormalities causes the incipient and critical damages to the transformer. The isolation of transformers leads to the voltage profile change, load curtailments, high compensation, economic loss, and many more problems. It is very important to know the problems occurred in the transformer parts to repair and restore it into the system to attain better stability, reliability, and economics. The transformer health monitoring system consisting of prediction, identification, and diagnostics in online as well as offline mode that will provide sufficient content to the managerial utility to take actions against the problem anticipated or occurred. The heuristic survey inks, the probability of damage in the transformer winding is more compared to the other parts. A novel method using system identification is proposed for the diagnosis of transformer winding. The location and extent of mechanical deformations can be ascertained along with specifically detecting radial and axial deformations in the transformer windings. A system identification approach in frequency and time domain were employed in the diagnostic algorithms for the sweep frequency response dataset. For both transfer function and state space model, a reference table called deformation information tableau has been synthesized for lumped parameter transformer model by varying series and shunt circuit elements systematically. The details of deformation are extracted from the tableau for actual frequency response data for a specified frequency range and winding type. The crosscorrelation of two-dimensional frequency response arrays, one being a signature array and other being deformation array, is used to represent relativity as a singleton. A toolbox is developed for the generation of heuristic deformation information tableau and to diagnose using the diagnostics algorithm developed. The proposed algorithms were verified and simulated for continuous disk type winding. 2019 IEEE. -
Degradation of azodyes in wastewater by using hydrodynamic cavitation technique
The organic waste water discharged from various industries consists of large amounts of dyes & cyanides & other toxic carcinogenic pollutants which are harmful to human health & ecosystem. Release of carcinogenic dyes is hazardous & has a detrimental effect on the well being of an individual. The present work is focussed at finding the viability of hydrodynamic cavitations process in the degradation of dyes. To study the degradation, influence of various parameters on degradation rate has been studied. BEIESP. -
Degree of Children Influence on Parents Buying Decision Process
European Journal of Business Management Vol. 4, No. 14, pp 49-57, ISSN No. 2222-2839 -
Delay Minimization Technique to improve the efficiency of Parameter Optimized Hysteretic Current Controlled Parallel Hybrid ETPA in Mobile Communication
This paper proposes a delay minimization technique to improve the efficiency of a parameter-optimized hysteretic current-controlled parallel hybrid envelope tracking power amplifier (etpa). In a hysteretic current-controlled hybrid topology, a linear amplifier operates parallel with a hysteretic current-controlled switching converter. Block level simulation of etpa is performed using the simulink tool. The traditional parameter optimization technique is first implemented, and its limitation is analysed. The proposed delay minimization technique helps to overcome the limitation of the traditional approach and has been proven to be valid for any input frequency. The proposed technique offers an efficiency improvement of 14.9% compared to the traditional technique for an input frequency of 20mhz and provides an average efficiency improvement of 6.26% for an input frequency range of 2mhz to 60mhz. 2024 IEEE. -
Delayed in sensorimotor reflex ontogeny, slow physical growth, and impairments in behaviour as well as dopaminergic neuronal death in mice offspring following prenatally rotenone administration
The environment is varying day by day with the introduction of chemicals such as pesticides, most of which have not been effectively studied for their influence on a susceptible group of population involving infants and pregnant females. Rotenone is an organic pesticide used to prepare Parkinson's disease models. A lot of literature is available on the toxicity of rotenone on the adult brain, but to the best of our knowledge, effect of rotenone on prenatally exposed mice has never been investigated yet. Therefore, the recent work aims to evaluate the toxic effect of rotenone on mice, exposed prenatally. We exposed female mice to rotenone at the dose of 5mg/Kg b.w. throughout the gestational period with oral gavage. We then investigated the effects of rotenone on neonate's central nervous systems as well as on postnatal day (PD) 35 offspring. In the rotenone group, we observed slow physical growth, delays in physical milestones and sensorimotor reflex in neonates and induction of anxiety and impairment in cognitive performances of offspring at PD-35. Additionally, immunohistochemical analysis revealed a marked reduction in TH-positive neurons in substantia nigra. Histological examination of the cerebellum revealed a decrease in Purkinje neurons in the rotenone exposed group as compared to the control. The data from the study showed that prenatally exposure to rotenone affects growth, physical milestones, neuronal population and behaviour of mice when indirectly exposed to the offspring through their mother. This study could provide a great contribution to researchers to find out the molecular mechanism and participating signalling pathway behind these outcomes. 2023 International Society for Developmental Neuroscience. -
DELHI: A NOVEL by Khushwant Singh
[No abstract available] -
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Delving into the Bubble Detection of Specific NSE Sector Indices
This study meticulously examines market bubbles within specific sectors of the National Stock Exchange (NSE) over the period from January 2017 to December 2023, employing robust methodologies like RADF, SADF, and GSADF tests. The analysis, centered on 11 sectoral indices, integrates GSADF values with RADF and SADF, offering nuanced perspectives that underscore the sector-specific nature of bubbles. Notably, the study highlights bubble occurrences during the 2020 global crisis due to pandemic, emphasizing their dynamic and diverse manifestations amid the pandemic. Exclusive identification of bubbles in NSE IT, NSE Metal, and NSE Pharma enriches the strategic insights available to investors, facilitating informed decision-making and risk management. The sector-wise approach contributes to a holistic understanding of market dynamics, providing investors with valuable tools to navigate the intricacies of the financial landscape. Future research avenues may delve into regulatory impacts on sector-specific bubbles and explore the interplay between macroeconomic indicators and sectoral bubbles, offering deeper insights into market dynamics. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Delving into the Exchange-Traded Funds (ETFs) Market: Understanding Market Efficiency
Exchange-traded funds (ETFs) are the most popular products in the financial sector today. There is extensive literature on the multifractal analysis of some stock markets, but not about the multifractal behaviour of the ETF market. This study examines the efficiency of stock index ETFs worldwide from an Efficient Market Hypothesis (EMH) perspective, using the ETFs: Ishares Msci World ETF (URTH), Ishares Russell 1000 ETF (IWB), SPDR S&P 500 ETF TRUST (SPY), Ishares Global Clean En. ETF (ICLN), Ishares USD Green Bond ETF (BGRN), from 1 January 2021 to 24 May 2024. It analyses a pre-conflict and a geopolitical conflict to uncover distinct patterns of behaviour reflecting significant changes in market conditions. Before the conflict, the Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500 and Ishares USD Green Bond ETFs showed signs of anti-persistence in returns, indicating a lack of strong relationship or predictability between short-term price movements. The Ishares Global Clean Energy ETF did not reject the random walk hypothesis, suggesting that returns follow a pattern closer to random, where market prices already efficiently reflect all available information. During the conflict, there was a transition in the ETFs' behaviour patterns, as evidenced by the increases in slope values for Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500, Ishares Global Clean Energy and Ishares USD Green Bond. Thus, the possible transition from anti-persistence to long-term memories in ETF returns during the conflict. For portfolio managers, these findings highlight the need to continually adapt investment strategies to manage risks better and take advantage of opportunities in a dynamic and complex investment environment. 2024, Creative Publishing House. All rights reserved.