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Quantum optimization for machine learning
Machine learning is a branch of Artificial Intelligence that seeks to make machines learn from data. It is being applied for solving real world problems with huge amount of data. Though, Machine Learning is receiving wide acceptance, however, execution time is one of the major concerns in practical implementations of Machine Learning techniques. It largely comprises of a set of techniques that trains a model by reducing the error between the desired or actual outcome and an estimated or predicted outcome, which is often called as loss function. Thus, training in machine learning techniques often requires solving a difficult optimization problem, which is the most expensive step in the entire model-building process and its applications. One of the possible solutions in near future for reducing execution time of training process in Machine learning techniques is to implement them on quantum computers instead of classical computers. It is conjectured that quantum computers may be exponentially faster than classical computers for solving problems which involve matrix operations. Some of the machine learning techniques like support vector machines make extensive use of matrices, which can be made faster by implementing them on quantum computers. However, their efficient implementation is non-trivial and requires existence of quantum memories. Thus, another possible solution in near term is to use a hybrid of Classical Quantum approach, where a machine learning model is implemented in classical computer but the optimization of loss function during training is performed on quantum computer instead of classical computer. Several Quantum optimization algorithms have been proposed in recent years, which can be classified as gradient based and gradient free optimization techniques. Gradient based techniques require the nature of optimization problem being solved to be convex, continuous and differentiable otherwise if the problem is non-convex then they can find local optima only whereas gradient free optimization techniques work well even with non-continuous, non-linear and nonconvex optimization problems. This chapter discusses a global optimization technique based on Adiabatic Quantum Computation (AQC) to solve minimization of loss function without any restriction on its structure and the underlying model, which is being learned. Further, it is also shown that in the proposed framework, AQC based approach would be superior to circuit-based approach in solving global optimization problems. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
APPLICATION OF ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL INDUSTRY
Artificial Technology is the blockbuster technology today. Pharmaceutical industry is no exception to the technology onslaught. Pharmaceutical industry adapting to the Artificial Intelligence (AI) to improve the overall performance of the industry processes, through improved efficiency in the operations and reduced lead time in the drug discovery. This is done through AIs ability of scanning huge data to speed up the drug discovery stage by identifying prospective drug candidates through technology like Structure-Based Virtual Screening (SBVS) and Fragment-Based Drug Discovery (FBDD). A nascent drug approach called as drug repurposing is very prospective through AI, and AI makes it possible to integrate nanotechnology, targeted drug development and personalised treatment based on genetic and proteomic data. AI has huge applications in the very important drug development stage of clinical trials. Selection of suitable participants, predicting drug responses will have huge cost reduction with the AI technology. In addition to drug trials, AI is transforming the pharmaceutical marketing process. Personalised communication, predictive sales forecasting, automated content generation and sentiment analysis are some of the possible as of now. These applications make the companies offer tailor made marketing strategies specific to physicians and patients and monitor the brand reputation and bring efficiency in the supply chain. Albeit the potential benefits, adoption of AI fully in the pharmaceutical industry has its own challenges. In the areas of data privacy, regulatory compliance and ethics related to drug testing, AI could face serious challenges. As the technology evolves, AI will have its impact on the pharmaceutical industry offering huge growth opportunities. India could emerge as a potential superpower in the pharmaceutical industry if AI is properly harnessed for industry growth. India can be the pharmacy for the entire world in the coming days if industry finds a way to utilize AI properly. 2024, Indian Pharmaceutical Association. All rights reserved. -
Platform Business Model for Intelligent Supply Chain Operations
Platform economy involves technology to connect the dispersed network of participants. The Platform Business Model denotes a triangular participation between; the platform itself, the supplier and the consumer. The global market is witnessing a rise of digital platforms with an increase in the power of algorithms and cloud-based computing, connecting millions of participants in the network. The technological advancement makes the digital platforms a formidable force that ushers in change and brings out economic revolution across the globe. Many entrepreneurs have been created by these platforms, the workforces have the freedom to choose their work time and job, leading to an economically vibrant society. Platform businesses exist across various verticals, even in manufacturing setup. A variety of goods can be produced in a flexible assembly line. Hence the concept of outsourcing may require new definition from low-cost labour-based countries to high technology low-cost countries. There may be a transformation of economies shifting towards service, as major manufacturers may reorient themselves into service operators. Overall, the platform business model would make the entire operation more transparent with real time data transfer between the participants, leading to efficiency across the entire chain of business activities. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Telemedicine-The New Digital Healthcare Platform
The concept of Telemedicine was known to the public since 1970s but the real potential was realized only during the pandemic Covid-19. The pandemic has reviewed Telemedicine and has given a wide acceptance among the public within a short span of time. This technology enabled tele-healing offers very little direct interaction between the healthcare providers and the patients. This article explores the technology requirements, ethical, and legal challenges, and opportunity to offer different healthcare services through Telemedicine. There is a scope for this technology morphing into platform models like what we have for social media. As more people start using telemedicine, the other healthcare services could be identified and offered for each individual. It can be concluded that telemedicine offers huge potential to take health benefits at places where health infrastructure could not be offered due to scalability issues. 2023, Indian Pharmaceutical Association. All rights reserved. -
Usage of social media in education: A paradigm shift in the Indian education sector
The pandemic is anticipated to have a significant economic impact, and it already has a terrible effect on schooling worldwide. Due to the coronavirus's quick spread, educational institutions worldwide are making the drastic leap from delivering course materials in person to doing so online. The rapid use of digital technology represents a significant paradigm change that may ultimately transform the Indian educational system. The COVID-19 scenario provides an opportunity to test new tools and technology to make education more relevant for students who cannot travel to campuses. With online learning and evaluation, there is a chance to increase knowledge and productivity while acquiring new skill sets and expedited professional talents. In this chapter, the authors have examined the educational difficulties and opportunities brought on by the sudden COVID-19 epidemic, followed by a discussion of how the Indian educational system has to be recalibrated. Copyright 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 2023 by IGI Global. All rights reserved. -
Social media usage in indian banking and financial institutions
Due to technological innovation, the economy is transitioning from a market-driven to a network-oriented status, and social media has seized the leading I.T. trends in the technology sector. A paradigm change in banking and inance operations has occurred due to the upswing in innovation, transformation, and digitalisation in Indian banking and inancial organisations. The development of online banking, mobile apps, mobile banking, and tools like debit and credit cards has changed how customers utilise banking and inancing services. Thanks to social media and digital marketing, banks may now be practical tools for supporting customers' enterprises and gaining target prospects. To provide customers with rapid and eicient service in the post-pandemic age, Indian banks and inancial institutions are rushing to modernise their technology infrastructure and digital goods. Social media ofers users attractive options for 24-hour access to information and the use of inancial services across temporal and geographic boundaries. 2023 by IGI Global. All rights reserved. -
An Effective BiLSTM-CRF Based Approach to Predict Student Achievement: An Experimental Evaluation
Currently, massive volumes of data are accumulated in databases when people configure new requirements and services. Data mining techniques and intelligent systems are emerging for managing large amounts of data and extracting actionable insights for policy development. As digital technology has grown, it has naturally become intertwined with e-learning practices. In order to facilitate communication between instructors and a diverse student body located all over the world, distance learning programs rely on Learning Management Systems (LMSs). Colleges can better accommodate their students' individual needs by using and analyzing interaction data that reveals variances in their learning progress. Predicting pupils' success or failure is a breeze with the help of learning analytics tools. Better learning outcomes might be achieved through early prediction leading to swift focused action. Preprocessing, feature selection, and model training are the three components of the proposed method. Data cleansing, data transformation, and data reduction are the preprocessing steps used here. It used a CFS to enable feature selection. This study has used a BiLSTM-CRF hybrid approach to train the model. When compared to tried-and-true techniques like CNN and CRF, the proposed method performs effectively. 2024 IEEE. -
Surface Roughness Analysis in AWJM for Enhanced Workpiece Quality
Abrasive Water Jet Machining is a distinctive manufacturing process that effectively removes material from a workpiece by employing a high-pressure stream of water combined with abrasive particles. The final quality of the machined surface is directly influenced by various process parameters, such as the traverse speed, hydraulic pressure, stand-off distance, abrasive flow rate, and the specific type of abrasive used. In recent times, extensive research has been undertaken to enhance the performance of AWJM, with a specific focus on critical performance measures like surface roughness. This paper presents the latest advancements in AWJM research, with particular attention given to enhancing performance measures, implementing process monitoring and control, and optimizing process variables for applications involving high-carbon steel. 2024 E3S Web of Conferences -
Analysis of multimode oscillations caused by subsynchronous resonance on generator shaft /
European Journal of Electrical Engineering, Vol.20, Issue 4, pp.455-468 -
Characterization and comparison studies of Bentonite and Flyash for electrical grounding
Earthing or Grounding is an Electrical system consists of electrodes which serves as an electrical connection from an electric circuit in the system to the earth or ground. Traditional Earthing- where we mix charcoal and salt offers low resistance to the fault current flow developed from a Low operating Voltages. Since operating voltages are high now a days, Short circuit current also increased. Traditional method of Earthing is replaced by chemical Earthing.Bentonite which is mainly used in chemical Earthing serves the requirement of Low resistance Earthing pits and also have the property to retain the moisture. In this paper an attempt had been made to assure the Flyash usage in the grounding pit and this paper discusses the Characterization, Comparison and Field Studies on Earthing Pit constructed with Bentonite and Fly ash layers. 2015 IEEE. -
Study of generator shaft behaviour during subsynchronous resonance using finite element method
Scientific research in electric power stations includes various online monitoring and control of equipments. Turbine and generator plays a key role in generating power. Frequency response analysis of the shaft which connects turbine and generator is used to detect the steady state response. It will enable the user to understand and design the system in such a way that it can withstand resonance, fatigue and other vibrations. Subsynchronous resonance which arises during line compensation by series capacitors increases oscillations in the turbine generator shaft system. The oscillations developed at low frequency causes physical damage to the shaft. There are several real time monitoring of the rotor shaft and turbine shaft misalignment by using laser technologies. The aim of this research paper is to use frequency response and modal analysis technique to detect the stress in the shaft and improve the design of it. A viscous damper is designed in the 3D model at the point of highly stressed area to control the resonance effect caused by series capacitors. 2020, Levrotto and Bella. All rights reserved. -
Determination of stress on turbine generator shaft due to subsynchronous resonance using finite element method
Power Capacitors plays a vital role in reactive power compensation. When the capacitors are connected to the transmission line, it improves the reactive power. Although the reactive power is improved, there is a possibility for sub synchronous resonance created by this capacitors in the transmission line which can impact the generator frequency. The sub synchronous resonance causes electro-mechanical stress in the generator shaft which ultimately leads to malfunction of the entire power generating unit. It is necessary to find out operating modes of the generator and turbine when the line is compensated with capacitors. Once the operating modes are clear, it is possible to damp the sub synchronous resonance. In this paper, three phase generator is coupled with a prime mover and capacitors are connected before the load. The stress on the turbine is analysed based on the torque of two rotating machines. Finite element method is used to estimate the stress in the turbine generator shaft system. 2006-2019 Asian Research Publishing Network (ARPN). -
Analysis of multimode oscillations caused by subsynchronous resonance on generator shaft
Series capacitors are installed in high voltage alternating current transmission lines to counteract the inductive reactance of the line. The resonance caused by series capacitors between electric system and mechanical system at frequencies less than the synchronous speed, leads to torsional oscillations. Undamped oscillations ma y cause a severe fatigue in the turbine generator shaft system. Rotating component undergoes various modes of oscillations when it is subjected to resonance. Rotor oscillate in different modes such as swing mode, super synchronous mode, electromechanical mode and torsional mode. Rotor dynamics of rotating structure depends on several factors like Coriolis Effect, moment of inertia and stiffness coefficient. Modal analysis using finite element method gives the natural frequency and mode shapes of any rotating structures. In this paper, a two mass rotating system which is analogous to turbine generator is subjected to resonance by adding series capacitors and its dynamic behavior is studied using finite element method. 2018 Lavoisier. -
Dampers to Suppress Vibrations in Hydro Turbine-Generator Shaft Due to Subsynchronous Resonance
There are numerous applications to evaluate the damage caused by subsynchronous resonance (SSR) to a turbine-generator shaft. Despite multiple applications, there are relatively few studies on shaft misalignment in the literature. In this paper, stresses in the existing turbine-generator shaft due to subsynchronous resonance were studied using finite element analysis (FEA). The 3D finite element model reveals that the most stressed part of the shaft is near the generator terminal. A new nonlinear damping scheme is modeled to reflect the torsional interaction and to suppress the mechanical vibration caused by subsynchronous resonance (SSR). Stresses developed due to the addition of capacitors in the system at high rotational speeds and deformation of the shaft during various modes of oscillations were evaluated. Experimental investigations are carried out in reaction turbine connected to a 3kVA generator. Simulation is carried out for the experimental setup using ANSYS. According to the simulation results, the damper installed near the generator terminal provides satisfactory damping performance and the subsynchronous oscillations are suppressed. 2021, Springer Nature Singapore Pte Ltd. -
Rotor Dynamics of TurbineGenerator Shaft with Dampers During Subsynchronous Resonance Generated by Series Capacitors
Purpose In this paper, an electromechanical approach to study the turbinegenerator shaft stability with and without dampers is made. The shaft is subjected to electrical disturbances created by series capacitors. The high power capacitors help the electric power system to improve the reactive power in high voltage transmission lines. Methods Finite element method is used to study the stability of the shaft under subsynchronous resonance when compared to the traditional methods like eigenvalue analysis, frequency scanning method and digital time simulation techniques. At the same time, it leads to subsynchronous resonance. Results Electromechanical stress in the rotating shaft arises when the resonance is created in the system. Maximum stress and strain of the shaft are calculated with other necessary parameters to prove the system instability. In order to maintain stability, dampers are installed at an optimum location. Conclusion Best location of installing damper is found using ANSYS 16.0 by modal analysis, harmonic and phase response analysis. The damper installed at the point reduces the stress caused by subsynchronous resonance and maintains the stability of the system. 2021, Springer Nature Singapore Pte Ltd. -
Experimental Investigations on Turbine-Generator Shaft Under Subsynchronous Resonance
Energy exchange takes place between turbine and generator in the power system during subsynchronous resonance (SSR) which leads to torsional interaction between the shafts. Resonance in the power system is caused by the series capacitors connected to the transmission line. This paper aims to present an electromechanical approach to analyse and interpret subsynchronous resonance using the Finite element method. Subsynchronous resonance is introduced in two test rigs consisting of turbine, generator, shaft, and coupler with capacitors. Experiments and simulations (torque analysis and frequency response analysis) are conducted in test rigs and ANSYS workbench 16.0. Moreover, a spring damper is modelled to improve the stability of the shaft. From the results, it is clear that mechanical stress is increased when capacitors are connected to the test rig. A spring damper is installed at the point where the deformation is high. The damper reduced the stress and the vibration. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Impact on cardioprotective effect of Psidium guajava leaves extract in streptozotocin-induced Wistar mice with molecular in silico analysis
Cardiovascular disease (CVD) and its complications have been regarded as the leading cause of morbidity and mortality. The drugs available in the market are effective to treat CVD, but with many adverse reactions. Nowadays, herbal products are the attention of researchers because of their less adverse effects. In this study, the cardioprotective effects of ethanolic leaves extract of Psidium guajava Linn. (Guava) (P. guajava) were evaluated in streptozotocin (STZ)-treated animal models. Mice acquired for the study were divided into five groups, each consisting of six mice. The toxin-induced mice were treated with the ethanolic leaves extract of P. guajava (300 mg/ kg body weight [b.w.]). The results were compared to the standard drug (glibenclamide)-treated mice (3 mg/kg b.w.). The following parameters were considered for further investigations: creatine kinase-muscle brain (CK-MB), creatine kinase (CK), troponin, lysosomal, and mitochondrial enzymes. Then the docking study was accomplished. The levels of cardiac marker enzymes and lysosomal enzymes increased significantly in the toxin-induced mice, while the level of mitochondrial enzyme decreased significantly. During treatment with the ethanolic leaves extract of P. guajava, the levels of all parameters were notably reversed to normal range (P < 0.05). Further, in docking analysis, the interaction of compounds, such as alpha-terpineol, cyclopentanecarboxamide, guaiol (a sesquiterpenoid alcohol), 1H-cyclopropanaphthalene, tetracyclotridecan-9-ol, dormin/abscisic acid, and epiglobulol, with the respective protein molecules, evidenced the cardioprotective effect of P. guajava leaves. Hence, it was concluded that the ethanolic leaves extract of P. guajava leaves have a cardioprotective effect. 2023 Codon Publications. -
Leveraging Deep Learning in Hate Speech Analysis on Social Platform
The scope and usage of the Internet have surpassed the expected growth and have proven beyond the basic purpose of being used for networking and telecommunications. It serves as the backbone of the web, and one of the predominant domains that uses the Internet is social media. The concept was conceived in the early 1990s and went on to grow as a powerful medium of people networking along with the Internet. Social networking sites (SNS) acquired a predominant element of the Internet owing to their use and services they offer through the Internet. A few of the most used social networking sites include Twitter and Facebook, which are used synonymous to expressions of text. These SNS allow the users to post photos, videos, and other multimedia content along with text and voice messages that are shared among other users. As with any technology or application, these also have the risk of users posting offensive material and textual content. Hate is being spread through messages, which are in the form of text and also through other materials posted. There is no control to check for the message for the hate content as and when it is posted, and by the time it is deleted by admins, it could have already reached millions of users. This chapter proposes a technique for detecting hate texts in reviews from registered users in the Twitter dataset. The proposed work makes use of improved principle component analysis (IPCA) and modified convolution neural network (MCNN) for detecting hate texts. The advantage of natural language processing is used for building an automated system for the analysis of syntax and semantics of the words. The proposed methodology consists of phases like pre-processing, feature extraction, and process to classify the text. The white spaces in the text are removed through normalization in the pre-processing phase, and also remove special characters such as question marks, punctuations, and exclamatory symbols to remove stop words. The features that are pre-processed are then subjected to feature extraction using IPCA. A set of correlated features are made used for identifying more important features in the data set under consideration. Next, the classification is done for identifying the hate text or for any language abuse. MCNN is applied for the classification of the text into HATE and NON-HATE from the text with better accuracy. The experiments prove that the proposed method has a high level of accuracy even for a large dataset. The results show that the proposed method has better performance in terms of precision, recall, and F-measure when compared with other state-of-the-art methods. 2024 Taylor & Francis Group, LLC. -
AI Based Variable Step Size Block Least Mean Square Filter for Noise Cancellation System
Most of the Active Noise Cancellation (ANC) systems working properly in low-frequency noises only. To make it suitable for isolating high-frequency noise, it needs an additional circuit which consumes more energy. This problem is mitigated in this study by designing a Variable Step size Block Least Mean Square (VSBLMS) filter which is suitable for an effective noise cancellation system. VSBLMS filter is designed with RCA to make a design area efficient and it is designed with a novel adder to achieve high speed as well as less energy consumption. The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed VSBLMS filter design mitigates the unwanted noises in various frequency bands. The proposed VSBLMS reduces the energy consumption by 9.32%, 27.63%, 13.53%, 11.80%, 10.71 %, 13.14% and 9.26% when compared with state of the art methods. 2023 IEEE. -
Area and Energy Efficient Method Using AI for Noise Cancellation in Ear Phones
Adaptive filters are suitable for most of the Digital Signal Processing (DSP) applications such as channel equalization, noise cancellation, echo cancellation, channel estimation and system identification. Nowadays due to the advancement in semiconductor technology, the need for Active Noise Cancellation (ANC) headphones in compact devices is increased. The major idea behind this proposed work is to design an area and energy efficient novel adaptive filter suitable for in-ear headphones by combining Normalized Least Mean Square (NLMS) and Block LMS (BLMS). The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed design mitigates the unwanted noises in various frequency bands. 2023 IEEE.