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Analyzing the synchronization and causality between Indias financial and business cycle: empirical evidence and policy insights
Purpose This research aims to develop an aggregate financial cycle for India and understand its interrelationship with the business cycle. To study this relationship, the research focuses on examining the level of synchronization, comovement and leadlag relationship between the aggregate financial cycle and the business cycle of India. Design/methodology/approach The study uses principal component analysis and wavelet transform analysis to develop the aggregate financial cycle and understand its time-frequency characteristics, respectively. Then the study undertakes a three-step econometric analysis to measure the various aspects of the relationship between the financial and business cycles. Findings The study found that the aggregate financial cycle and the Indian business cycle have long-term equilibrating relationships. The comovement and the degree of synchronization between the two cycles are moderate, which shows that the relationship between them is relatively dynamic. Further, the leadlag relationship indicated that the financial cycle often leads the business cycle and not vice versa. Originality/value The research stands out as one of the few works to capture multiple dimensions of the financial market into a single aggregate financial cycle to present a broader picture of an emerging market setting, such as India. This study adds to the literature by systematically investigating the relationship between financial and business cycles over the short-, medium- and long-term horizons. Emerald Publishing Limited -
Constructing an aggregate financial cycle for India: an analysis of key financial indicators
Purpose This paper aims to develop a financial cycle for India by aggregating key financial indicators from different sectors of the financial system and then comparing it with the business cycle to understand the macroeconomic implications. Design/methodology/approach The research uses various econometric tests to analyze the relationship among the financial indicators before applying the principal component analysis and filtering technique to extract the aggregate financial cycle. After this, the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is applied to understand how it interacts with the economy. Findings The result of this study creates an aggregate financial cycle that effectively reflects the most influential components of the financial system, empowering policymakers to measure and safeguard the stability of the financial system and economy. Originality/value The authors contribution lies in the systematic integration of key indicators into a comprehensive aggregate financial cycle for India, which has not been thoroughly explored in existing research. This study also emphasizes the significance of banking sectors and other financial intermediaries that were undermined in the existing financial cycle studies. 2025 Emerald Publishing Limited -
Removal of Artifacts from Electroenchaphalography Signal using Multiwavelet Transform
The signal from the brain can be recorded using Electroenchaphalography (EEG). The proposed work summarizes a unique method which is used for the removal of mixed artifacts presented in the electroencephalography signal during the acquisition process. Artifacts comprises of various bio-potential unit such as electrooculogram, electrocardiogram, and electromyogram. These artifacts are referred as a noise sources which is responsible for the complexity of the EEG signal. The artifacts obtained from the EEG signal leads towards improper diagnosis of pathological conditions. The EEG signal which is obtained from the brain is the multi-dimensional signal with the various statistical properties. Time consumption of the EEG signal is not reproducible due to the biological properties of the signal. The information of the EEG signal consists of the data of the neuron levels which is collected for every millisecond with the temporal resolution scale. In account of special cases, EEG signal contains noise and artifacts where information is collected using the extraction of signals. To obtain the information of the artifacts the proposed technique is used to maintain higher accuracy in the extraction process. The proposed technique consists of multiwavelet transform to remove the artifacts from the input EEG signal. In the proposed multiwavelet transform, the signal which consists of noisy features can be decomposed using GHM and thresholding technique. This experimental analysis shows the removal of artifacts from the EEG signals. The pathological conditions are removed which leads to the increase in the accuracy of the system. Also, this research findings shows that the proposed multiwavelet transform based approach outperforms significantly with respect to conventional approaches. The reconstructed EEG signal has the lesser reliability range which is measured in-terms of signal to noise ratio and power spectral density. Published under licence by IOP Publishing Ltd. -
Proficient technique for satellite image enhancement using hybrid transformation with FPGA
Visual quality of images is improved by digital techniques for the improvement of photographs. The main purpose of image improvements is to process an image to make the output more desirable for a particular use than the original image. This paper proposes a new approach, which improves the picture of the satellite by the use of the SVD DWT concept, the Gaussian transformation DWT and multiwavelet transformation. This suggested approach would convert and approximate the single-colour value matrix of the low-flowing sub-band into one low-frequency and 15 high-frequency sub-bands, and then recreate the improved picture using the inverse transformation. In terms of technical criteria as PSNR, RMSE and CC, this approach can have higher quality and quantitative performance. This paper introduces strategies for improving hardware images using a programmable door array in real-time (FPGA). The suggested algorithm is implemented successfully with Xilinx ISE, MATLAB and ModelSim on different scale satellite images in Verilog HDL. In this article, these algorithms should be simulated and implemented using Verilog HDL. The Spartan-3E from Xilinx is the unit chosen here. 2021 IEEE -
Diagnosis of Retinal Disease Using Retinal Blood Vessel Extraction
The eye is one of the important organs of the human body. In recent times, major parts of the eye are damaged due to various retinal diseases. Major diseases related to the retina are glaucoma, papilledema, retinoblastoma, diabetic retinopathy, and macular degeneration. These diseases can be detected using image processing techniques. These diseases can cause damage to the eye; hence the early diagnosis can prevent the loss of vision. Thus the early stage of rectification may lead to smaller damage than the risky ones. By extracting the blood vessels, various retinal diseases can be identified, and the severity of the disease can also be identified. Some of these diseases in the retina will occur due to hypertension, blood pressure, and diabetics. Thus, the tear in the blood vessels leads to the loss of visuality in human beings. The proposed work consists of image processing techniques such as segmentation, feature extraction, and boundary extraction which lead to the identification of various retinal diseases with a certain level of accuracy, sensitivity, and specificity by using image processing techniques. The training and testing of retinal images are carried out by using the artificial neural network (ANN) classifier for glaucoma detection and support vector machine (SVM) classifier for detecting diabetic retinopathy. 2021, Springer Nature Switzerland AG. -
Equalization of Finite-Alphabet MMSE for All-Digital Massive MU-MIMO mm-Wave Communication
For more than twenty years, growing the performance and efficiency of wireless communications systems using antenna arrays has been an active field of study. Wireless networks with multiple-input multiple-output are also part of the current norms and are implemented around the world. Access points or BSs with comparatively few antennas are used for standard MIMO systems, and the resulting increase in spectral efficiency was relatively modest. A Multiple-Input Multiple-Output platform's capacity is researched where the transmitter outputs are processed and quantified by a set of limit quantizes through an analogue linear combining network. The linear mixing weights and cutoff levels are chosen from with a collection of possible combinations as a function of the transmitted signal. Millimetre-wave networking requires optimum data transmission to various computers on same moment network in combination with large multi-user actually massive. In order to guarantee efficient data transmission, the heavy insertion loss of wave propagation at su ch a faster speed needs proper channel estimation. A new channel estimation algorithm called Beam space Channel Estimation is suggested. From a set of possible configurations, the capacity of a massive stream from which antennas signals are handled by an analog channel as a part of the channel matrix, linear mixture weights and frequency modulation levels are selected. Probable implementations of specific analogue receiver designs for the combined network model, such as smart antenna selection, sign antennas output thresholding or linear output processing. To demonstrate the effectiveness of BEACHES in service and have FPGA implementation results, we are developing VLSI architecture. Our results show that for large MU-MIMOs, mm-wave communications with hundreds of antennas, specially made denoising can be done at maximum bandwidth and in an equipment format. Published under licence by IOP Publishing Ltd. -
An Improvised Mechanism for Optimizing Fault Detection for Big Data Analytics Environment
In the applications of fault detection, the inputs are the data reflected from health state of the observed system. A major challenge to finding errors is the nonlinear relationship between the data. Big data has other drawbacks, and the volume and speed with which it is generated are reflected in the data streams themselves. In this paper, we develop a deep learning model that aims to provide fault detection in big data analytics engine. This investigation develops an approach for fault detection in large datasets using unsupervised learning. In this research, an unsupervised method of learning is developed specifically for the task of classifying large datasets. To discover regular textual patterns in large datasets, this research use data visualization methods. In this virtual environment, we employ an unsupervised learning method of machine learning that does not require human oversight. Instead, the system should be allowed some leeway to work and find things on its own. The unsupervised learning approach utilizes data that has not been tagged. In contrast to supervised learning, this approach can handle complex tasks. 2024, Ismail Saritas. All rights reserved. -
Liquid gold: assessing groundwater quality at the historic Kolar gold fields, Karnataka, India
To assess ecological sustainability and resilience, it is necessary to periodically examine various ecological properties in areas with high pollution and contaminant risks. Kolar Gold Fields (KGF) in Kolar, Karnataka, showcases one of India's most contaminated zones because of the extensive gold mining and its lingering effects. In KGF, the quality of groundwater has been severely reduced as there exist extensive mining tailings, locally referred to as cyanide dumps, which have been neglected for several preceding years without proper disposal strategies. The current approach focuses on the water pollution caused by heavy metal deposits in the KGF region. Groundwater samples were sampled from Oorgam, an abandoned region in KGF, and subsequently filtered for water quality examinations. The investigation documented concentrations of several metals, including cadmium (0.068 0.0024 ppm), lead (0.288 0.0016 ppm), nickel (0.058 0.0047 ppm), and chromium (0.23 0.0235 ppm) and have met the standard specifications in accordance with World Health Organization (WHO). Prominent pH disparity was documented amongst the experimental samples, with a detectable pH drop in the aqua-purified water in comparison to the positive control. The test results imply that the water samples collected from KGF remain unpotable for consumption or irrigation due to the persistence of high levels of heavy metal concentration. This study underscores the urgent requirement for a remedial approach to ensure water safety for drinking and irrigation in the area. 2025 Brawijaya University. All rights reserved. -
Liquid gold: assessing groundwater quality at the historic Kolar gold fields, Karnataka, India
To assess ecological sustainability and resilience, it is necessary to periodically examine various ecological properties in areas with high pollution and contaminant risks. Kolar Gold Fields (KGF) in Kolar, Karnataka, showcases one of India's most contaminated zones because of the extensive gold mining and its lingering effects. In KGF, the quality of groundwater has been severely reduced as there exist extensive mining tailings, locally referred to as cyanide dumps, which have been neglected for several preceding years without proper disposal strategies. The current approach focuses on the water pollution caused by heavy metal deposits in the KGF region. Groundwater samples were sampled from Oorgam, an abandoned region in KGF, and subsequently filtered for water quality examinations. The investigation documented concentrations of several metals, including cadmium (0.068 0.0024 ppm), lead (0.288 0.0016 ppm), nickel (0.058 0.0047 ppm), and chromium (0.23 0.0235 ppm) and have met the standard specifications in accordance with World Health Organization (WHO). Prominent pH disparity was documented amongst the experimental samples, with a detectable pH drop in the aqua-purified water in comparison to the positive control. The test results imply that the water samples collected from KGF remain unpotable for consumption or irrigation due to the persistence of high levels of heavy metal concentration. This study underscores the urgent requirement for a remedial approach to ensure water safety for drinking and irrigation in the area. 2025 Brawijaya University. All rights reserved. -
A Relative Reference Responsive LRU based Page Replacement Algorithm for NAND Flash Memory
The acceptance of NAND flash memories in the electronic world, due to its non-volatility, high density, low power consumption, small size and fast access speed is hopeful. Due to the limitations in life span and wear levelling, this memory needs special attention in its management techniques compared to the conventional techniques used in hard disks. In this paper, an efficient page replacement algorithm is proposed for NAND flash based memory systems. The proposed algorithm focuses on decision making policies based on the relative reference ratio of pages in memory. The size adjustable eviction window and the relative reference based shadow list management technique proposed by the algorithm contribute much to the efficiency in page replacement procedure. The simulation tool based experiments conducted shows that the proposed algorithm performs superior to the well-known flash based page replacement algorithms with regard to page hit ratio and memory read/write operations. 2021, Webology. All Rights Reserved. -
Review on developments in nand flash page replacement algorithms
The non volatility, low power consumption and high density of NAND flash memories, made them an inevitable part of electronic industry. Due to the high wear out nature exhibited by flash systems, the algorithms used for page replacement in traditional memory systems are not suitable for flash page replacement. Along with the objective to maintain high hit rate, flash page replacement algorithms should aim at decreasing the page write count and maintain wear levelling. This paper presents major algorithms proposed for flash memory page replacement. The major contribution of this work is a relative study on various strategies, performance matrices and evaluation tools used for flash page replacement algorithm. The study shall help the researchers to identify the pros and cons of various flash page replacement algorithms, to identify the major gaps in between and to identify some commendable tools that can be used for flash page replacement algorithm evaluation. The gaps identified need to be addressed seriously in the near future. 2020, Engg Journals Publications. All rights reserved. -
A Relative Analysis on the Spotting of Cardiovascular Disease Employing Machine Learning Techniques
Heart is one of the significant segments in the human body since it powers blood to the all the pieces of the body. Blood courses through the vein. Cardiovascular sickness is corresponded with the blockage of vein. The sign of heart sickness depends whereupon condition is impacting an individual. The term coronary illness is ordinarily utilized instead of cardiovascular infection. Dilated cardiomyopathy, Heart failure, Arrhythmia, Pulmonary stenosis, Mitral regurgitation, Coronary artery disease, Myocardial infraction, Mitral valve prolapse, Hypertrophic cardiomyopathy are the sorts of coronary illness. The several machine learning techniques are analyzed to spot heart disease. This paper gives relative investigation of coronary illness expectation utilizing machine learning. 2021 IEEE. -
Implementation of hybrid machine learning approach for intrusion detection system
The Intrusion Detection System (IDS) enforces information security and is responsible to identify attacks and vulnerabilities inside a network. It does this by analyzing the packet stream throughout the network. In traditional IDS systems, the analysis is done by looking for signatures of known attacks or deviations of normal activity as described by the rules provided for the IDS system. Machine learning helps in deriving predictive knowledge and this makes it ideal to apply Machine learning in an IDS system to detect attacks. This paper focuses on creating a hybrid model that is best to implement in an IDS system. A hybrid model is implemented which combine multiple machine learning algorithms using Ensemble method. The experiments include evaluating machine learning algorithms such as Decision Tree, MLP (Multi-Layer Perceptron), Gradient Boosting etc. The algorithms with the best results are taken to construct Hybrid model. This Hybrid approach will improve the accuracy and efficiency for identifying the attacks by the IDS system. Depending on the type of attack, the IDS system can classify packets as DoS (Denial of Service), Probe, R2L (Root to Local), U2R (User to Root) or Normal. The experiments are carried using NSL-KDD Dataset. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Influence of Sinusoidal and Non-Sinusoidal Two-Frequency Gravity Modulation in Viscoelastic Fluids Driven by Triple Diffusivity
This study focuses on understanding the system's response to gravity modulation with two frequency components, characterized by both sinusoidal (sine wave) and non-sinusoidal (square, triangular, and sawtooth) waveforms, on three-component convection, considering a viscoelastic fluid modelled using an Oldroyd-B fluid. We apply the Venezian approach to evaluate the Rayleigh number, its corrected form, and the wave number by deriving a five-mode Lorenz model to investigate the onset of convection. A nonlinear analysis is conducted to investigate the dynamics of heat and mass transfer by solving an extended eight-mode Lorenz model, capturing higher order interactions. The onset of convection and the transport properties were observed to be influenced by combinations of sinusoidal and non-sinusoidal waveforms. This study optimizes convection-driven systems subjected to external periodic forcing by offering a more comprehensive understanding of convective instabilities in viscoelastic fluids. 2025 Wiley Periodicals LLC. -
Chaos in a triple diffusive system involving a viscoelastic fluid layer
This study investigates the linear and weakly nonlinear stability analysis in a Rayleigh-Bard configuration with a viscoelastic fluid layer influenced by two additional solutal components. The governing equations for both stationary and oscillatory convective regimes, and the critical point at which convection sets in is derived. The comparative analysis is performed for three different viscoelastic fluid models: Oldroyd-B, Maxwell, Rivlin-Ericksen fluid, along with the Newtonian fluid model. In weakly nonlinear stability analysis, a generalized eight-mode Lorenz model is developed that satisfies the general properties of a classical Lorenz model. From this reduced model, the critical points and Hopf-Rayleigh number, representing the initiation of chaos through Hopf bifurcation are determined. The Lyapunov exponents are used to characterize the chaotic, periodic and quasi-periodic motions of the system. The results show that the viscoelastic and triple diffusion parameters affect the initiation of convection and transition to chaos. It is also observed that the Maxwell fluid exhibits the earliest initiation of chaos and the Newtonian fluid the latest, with Oldroyd-B and Rivlin-Ericksen exhibiting intermediate behaviour. The presence of additional solutal concentrations delays the onset of chaotic motion. 2026 The Physical Society of the Republic of China (Taiwan). -
AI and IoT for universal health and well-being across generations
Over the last several years, the confluence of AI and the Internet of Things (IoT) has caused tremendous changes in many areas of our life, including the healthcare industry. Because of this cooperation, new possibilities have emerged with the aim of enhancing the health and welfare of people across all different generations. The ability to efficiently gather, analyze, and derive insights from large volumes of real-time data has revolutionized healthcare, allowing for better patient treatment and community health management. This is made feasible by combining algorithms powered by artificial intelligence with IoT-connected devices. Examining the gamechanging possibilities of AI and the IoT in the healthcare industry is the goal of this introductory piece. The function of AI and the Internet of Things in advancing health equity and wellness across diverse age groups is the primary emphasis of this study. Countless and varied uses of AI and the internet of things may be found in the medical field. Some examples of these uses include remote patient monitoring and the development of predictive analytics tools for use in illness prevention.Health outcomes and quality of life for individuals of all ages can be improved via the development of individualized therapies and treatment programs that cater to each person's specific needs. It is feasible to create these opportunities with the help of these technologies. Healthcare issues may be effectively addressed in a variety of locations, from densely populated cities to more rural places, by implementing solutions that leverage the internet of things and artificial intelligence. Because these solutions are both accessible and scalable, this is the result. It is possible for healthcare systems to overcome barriers to service delivery and access by utilizing these technologies. As a result, people of all ages and from all over the world will be able to live the kind of healthy, fulfilling lives they deserve. 2024, IGI Global. All rights reserved. -
Visiting Indian Hospitals Before, during and after COVID
The prevailing COVID-19 situation has brought in temporary and permanent changes in the attitude and lifestyle of people. Starting from Hand sanitizers and face masks, it extends to online classrooms and work from home culture. In case of visiting hospitals and medications, people with pre-existing medical conditions and minor health issues tend to delay or avoid visiting hospitals due to fear of infection, which is dangerous. Further, people or patients tend to access several alternatives and precautions. The alternatives include home remedies, ayurvedic medication, yoga and meditation. On the other hand, hospitals are trying to adapt online consulting and telemedicine. Besides, Cancellation or delay of nonemergency surgeries became inevitable in the lockdown phase. This survey conducted among the people of Erode district, Tamilnadu to study the perception of people concerning visiting hospitals for health issues. The results show that fear of infection, financial and transportation difficulties are the major factors which affected people from visiting hospital. Also, changing trends like Telemedicine and home remedies are likely to be permanently opted by people. In Brief, the outcomes reveal the changing attitude of people towards medication and hospital visiting habits. 2022 World Scientific Publishing Company. -
Let there be Light, but not too much: The Need to Legally Address Light Pollution in India
Electricity and artificial lights were synonymous with economic growth and development. Unfortunately, over usage of artificial lights has proven adverse effects. Research shows that excessive light impacts human health and endangers ecological balance, disturbs wildlife, causes decline in insect, moth, reptile pollution and depletes energy resources. Countries around the world have gradually started recognising light pollution as an emerging challenge and have brought in regulations to curb it. However, India is yet to recognise the threat of light pollution. Against this backdrop, the authors have established the need to recognise light pollution as a matter requiring dedicated and concerted focus. This was achieved through the analysis of recent and credible journal articles category with a cite score of over ten. Reliance was also placed on the light pollution map to understand the intensity of the problem, especially in India. The authors next conducted a study of legal regimes governing light pollution and artificial light, in different jurisdictions around the globe. The paper draws upon the best practices from these jurisdictions and suggests that India adopt techno-legal legislation, at the earliest, to combat light pollution. 2023- Kalpana Corporation. -
BCI Radiology Images Converting into Report Using BERT and GPT
The construction of precise radiology reports from medical images is an essential aspect of Contemporary healthcare. Medical images such as X-rays, MRIs, CT scans, or ultrasounds. Also, it can make use of medical reports. Medical report has a bunch of details about each patients medical history, diagnosis, treatment plan, lab results, and more. This paper represents a theoretical examination. The paper mainly focuses on two prominent NLP models. One is BERT (Bidirectional Encoder Representations from Transformers) and the other one is GPT (Generative Pre-trained Transformer). This paper is going to validate their applicability to transforming brain-computer interfaces (BCI). This paper will utilize these radiology images in perfectly framed medical reports. By differentiating these models based on their Architectural properties, Linguistic processing abilities, and capability for clinical integration, this papers goal is to establish the most effective method for automated medical reporting. Merging of these insights from existing studies recommends that when BERT leads in context-based precision and getting an idea of complex medical terminology, GPT offers outstanding text-generation potential. This paper proposes that an intermixture procedure taking advantage of the strengths of both models may offer the most supreme solution for automated medical reporting, balancing precision with readability and clinical applicability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Next Generation AI-Enhanced Intelligent Imaging for Automated Knee X-ray Interpretation in Osteoarthritis: Front office Integration and Employee Training
Osteoarthritis is a long-lasting musculoskeletal ailment in which the degenerative alterations in the cartilage of the articles progressively arise. As the pathological anatomy develops to bone structure degeneration, it results in pain, stiffness and functional impairment. It results in immobility, reduced quality of life, more susceptibility to fractures, hospitalization, and mental health problems in the older adults and postmenopausal women. To address the increasing global prevalence of OA and the necessity to diagnose X-rays of the knee promptly, affordably and accessibly, this paper presents an AI-based imaging system based on the use of Convolutional Neural Networks (CNNs) and compares their results with the existing machine learning models. The framework will be structured to integrate the front-office workflows and streamline the diagnostic process of patient registration to report delivery and establish a well-organized staff training ecosystem aimed at enabling clinical staff to operate the AI-enabled workflow and adapt to it. The study offers a comprehensive, deployment ready, diagnostic platform through advanced automation, user oriented front-office functionality and the consistent upskilling of the workforce, to provide scalable, precise, cost effective OA detection, clinical efficiency, interpretative variability reduction, and workforce transition to scalable intelligent, patient centred care. 2025 IEEE.