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Does environmental reporting ofbanks affect their financial performance? Evidence from India
Purpose: The present study aims to investigate the effect of environmental reporting on the financial performance of banks in India. Design/methodology/approach: The study is based on the secondary data. The sample includes the banks listed in the NSE Nifty Bank Index from 20162017 to 20202021. The environmental reporting data was obtained through the content analysis technique. The financial data was collected from the CMIE Prowess database. Panel regression analysis was used to analyse the data. Findings: The findings indicate a negative significant influence of environmental reporting on the ROA and ROE of banks. On the other hand, environmental reporting does not significantly influence the EPS of banking institutions. Originality/value: To the best of the authors knowledge, this study is the first to contribute to the scarce literature on the influence of environmental reporting on financial performance, pertinently in the context of a developing nation's banking sector. 2023, Emerald Publishing Limited. -
Growth Of ZnSnN2 Semiconductor Films For Gas Sensor Applications
ZnSnN2 is a member of class of nitride semiconductors which have the additional benefits of earth abundance and non-toxicity. For device applications, NH3 gas detecting senor, which finds use in chemical, pharmaceutical, and food process industries, are fabricated with zinc-tin-nitride (ZnSnN2) thin-films on glass substrate by making use of metal as contacts. The ZnSnN2 sensor is found extremely selective to ammonia (NH3) amongst other gases like ethanol, NO2, H2S and exhibited good detecting responses at room temperature. There are many ways to develop thin films of ZnSnN2, and hence in this work we are trying to find a cost effective, feasible and easier method of synthesis, i.e., chemical vapor deposition method. The first step was the optimization of process parameters to grow Zinc-Tin (ZnSn) thin-films. Later, optimization of the process parameters for the growth of compound ZnSnN2 was completed. The grown films are characterized by material quality using X-Ray Diffraction and UV- Vis spectroscopy. 2022 American Institute of Physics Inc.. All rights reserved. -
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. -
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. -
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. -
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. -
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. -
Popularity of food blogs: Exploring the rationale /
Food blogs have been introduced to the world long years back. But it gained popularity only in recent years. It was considered as a leisure medium or as a hobby. The need for food blogs came into existence when people started to write about the food they ate or which they cooked. Most of the people who follow food blogs get attracted to the facts like the content, photographs, visual appeal etc. -
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 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. -
Unstructured data extraction system using multi head attention and a novel language model /
Patent Number: 202141056398, Applicant: K. P. Kavitha.
A system 100 for Offline handwritten text recognition (HTR) of a scanned handwritten text input image leveraging Modern Deep Recurrent Neural Network (RNN). System 100 comprises (RNN) is proposed with the help of the present's embodiments disclosure (RNN). A cursive eliminated handwritten text image is mapped to a multi-head attention-based sequence-to-sequence learning applying the beam search technique and employing an RNN-based variable-length encoder-decoder architecture. -
A system for human face detection and recognition using feature fusion and a method thereof /
Patent Number: 202141031566, Applicant: Manjunatha Hiremath.
Biometric systems have become a vital role in the process of authenticating an individual based on physical or behavioral features/ traits of human beings. Biometric systems are categorized into two types namely Physiological and Behavioral systems. Face recognition, Fingerprint, Iris recognition, Hand geometry, and DNA fingerprint traits are considered as physiological biometrics which are essentially fixed and are relatively stable whereas voice recognition, signature and keystroke recognition are considered behavioral biometrics that can vary over a period of time due to some factors like aging, mood and behavior of the person. -
Secure and analytical agile framework for software continuous delivery /
Patent Number: 201741040903, Applicant: Anwin Varghese.
IT world thrives on quality software products that helps business sustain and grow. These software products help reduce the effort in time & bring in better economic efficiency making these products highly dependable. The challenge of having quality product releases frequently is not quite an easy task as it sounds. To meet this challenge of producing reliable software products, the IT managers & leads need a team of dedicated developers, system programmers, testers along with a highly efficient process in place. -
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. -
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. -
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. -
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. -
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 -
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. -
