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Some New Results on Non-zero Component Graphs of Vector Spaces Over Finite Fields
The non-zero component graph of a vector space with finite dimension over a finite field F is the graph G=(V,E), where vertices of G are the non-zero vectors in V, two of which are adjacent if they have at least one basis vector with non-zero coefficient common in their basic representation. In this paper, we discuss certain properties of the non-zero component graphs of vector spaces with finite dimension over finite fields and their graph invariants. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Some results on b-chromatic topological indices of some graphs
Graph coloring is assigning weights, integers, or colors to edges, vertices, or both in a graph subject to certain conditions. Proper coloring C of graph G refers to assigning weights, integers, or colors to the vertices, edges, or both so that adjacent vertices or adjacent edges get a different color. A b-coloring follows proper vertex coloring with subject to an additional property that each color class should have at least one vertex with a neighbor in all the other color classes. The notion of Chromatic Zagreb index and irregularity index was introduced recently. This paper introduces the concept of b-Chromatic Zagreb indices and b-Chromatic irregularity indices. Also, we compute these indices for certain standard classes of graphs. 2023 Author(s). -
Some Variations of Domination in Order Sum Graphs
An order sum graph of a group G, denoted by ? os(G), is a graph with vertex set consisting of elements of G and two vertices say a, b? ? os(G) are adjacent if o(a) + o(b) > o(G). In this paper, we extend the study of order sum graphs of groups to domination. We determine different types of domination such as connected, global, strong, secure, restrained domination and so on for order sum graphs, their complement and line graphs of order sum graphs. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Spatio - Temporal Analysis of Temperature in Indian States
Data, the oil of the century, is available in multiple formats for various applications. It is collected, stored, and distributed across different use cases in various forms. Researchers study, analyse and use data for numerous analyses and predictions. There is an increase in demand and consideration of spatiotemporal data analysis. Analysing and obtaining insights from the spatiotemporal data are carried out by various researchers. Many investigations have started investigating the strategies for spatial-transient examination and applying spatial-transient information investigation procedures to different areas. Analysing spatiotemporal data has been an advanced task; with the help of various Python libraries, Spatio Temporal dataset about the temperature of states of India is analysed to support the harsh climate near the region of tropic of cancer. Across the decade, there has been a cyclic trend in the temperature, which keeps toggling yet increases over time. It remains a question of worry and genuine concern to predict climatic conditions. Spatio-temporal analysis of temperature in Indian states involves analysing the spatial and temporal variations in temperature across different states in India. The study can use various statistical and geographic information systems (GIS) tools. Spatio-temporal analysis of temperature in Indian states can provide valuable insights into the changing climate patterns in different regions of the country, which can be helpful for policymakers, researchers, and other stakeholders to make informed decisions related to climate change mitigation and adaptation. 2023 American Institute of Physics Inc.. All rights reserved. -
Specialized CNN Architectures for Enhanced Image Classification Performance
Image classification is one of the important tasks in computer vision, with a greater number of applications from facial recognition, medical imaging, object recognition and many more. Convolutional Neural Networks (CNNs) have developed as the foundation for image all classification tasks, showcasing the capacity to learn the hierarchical features automatically. In this study proposed three custom CNN models and its comprehensive analysis for the image classification tasks. The models are evaluated using CIFAR-10 dataset to assess the performance and efficiency. The experimental results shows that the proposed custom CNN Model-3 performance is better than the other two models. Our findings demonstrate that Model 3, featuring with the global average pooling, achieves the highest overall accuracy of 94 % with competitive computational efficiency. This suggests that global average pooling is the valuable technique for balanced and accurate image classification. 2024 IEEE. -
Spectroscopic analysis of lead borate systems
Oxide glass systems are interesting because of their bonding like bridging and non-bridging oxygens. Depending on the modifier, the B2O3 glass system can have various Boron-Oxygen network. It is found that, PbO modifies the borate network and increases the formation of penta and diborate groups. In this work, we investigated optical properties of Lead Borate glass systems (x PbO: (1-x) B2O3) with x varying from 30-85 mol % using UV-VIS Spectra and the corresponding band gap was estimated using Tauc relation and these systems behave like direct allowed band gap systems. These results show that, Eg decreases with the addition of lead content. Further the refractive index measurements also have been carried out at various wavelengths. Many correlation is found between the band gap and refractive index for different compositions. Using different theoretical models a best fit has been tried and Ravindra's relation is found to match with our experimental results. 2018 Author(s). -
Specular Reflection Removal Techniques in Cervix Image: A Comprehensive Review
Cancer detection through medical image segmentation and classification is possible owing to the advancement in image processing techniques. Segmentation and classification tasks carried out to predict and classify diseases need to be dependable and precise. Specular reflections are the high-intensity and low-saturation areas that reflect the light from the probing devices that capture the picture of the organ surface. These areas sometimes mimic the features that are key identifying factors for cancers like acetowhite lesions. This review article examines the various methods proposed for removing specular reflections from medical images, especially those captured by colposcope. The fundamentals of specular reflection removal and its associated challenges are discussed. The paper reviews several state-of-the-art approaches for specular reflection removal. The comprehensive review can be a strong foundation for researchers looking to decide on appropriate techniques to employ in their respective research approaches. 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Speech disabilities in adults and the suitable speech recognition software tools - A review
Speech impairment, though not a major obstacle, is still a problem for people who suffer from it, while they are making public presentations. This paper describes the different speech disabilities in adults and reviews the available software and other computer based tools that facilitate better communication for people with speech impairment. The motivation for this writing has been the fact that stuttering, one of the types of speech disability has affected about 1 percentage of the people worldwide. This fact was provided by the Stuttering Foundation of America, a Non-profit Organization, functioning since 1947. A solution to stuttering is expected to benefit a considerable population. Speech recognition software tools help people with disabilities use their computers and other hand held devices to satisfy their day-to-day needs which otherwise, require dedicated domestic help and also question the person's ability to be independent. ASR (Automatic Speech Recognition) systems are popular among the common people and people with motor disabilities, while using these techniques for the treatment of speech correction is a current research field and is of interest to SLPs/SLTs (Speech Language Pathologist / Speech Language Therapist). On-going research also includes development of ASR based software to facilitate comfortable oral communication with people suffering from speech dysfunctions, i.e., in the domain of AAC (Augmentative and Alternative Communication). 2015 IEEE. -
Spoken Language Identification using Deep Learning
A crucial problem in natural language processing is language identification, which has applications in speech recognition, translation services, and multilingual content. The five main Indian languages that are the subject of this study are Hindi, Bengali, Tamil, English, and Gujarati. A Deep Neural Network is introduced in the paper which is specifically made to use Mel-Frequency Cepstral Coefficients (MFCCs) for sophisticated language categorization. The suggested architecture of the model, which includes batch normalisation and tightly linked layers, helps it to be adept at identifying complex linguistic patterns. Comparing the research to the source work [18], promising improvements are shown, highlighting the potential of the model in language detection. 2024 IEEE. -
Spray dried nano oxide ceramics for free flowing plasma spray coating powders and battery material processing
Advanced materials are widely used in electronics, aerospace and automobile industry devices and also in substances synthesized for food, medical and pharmaceutical industries. The quality of the base material powder has high influence on the resulting material body (the product) which goes into the manufacture of the device. To name a few (a) flowable ceramic powders from agglomerated nano ceramic powders for plasma spray coatings with the right sprayable powder characteristics (b) advanced graphene encapsulated nano ceramic oxide powders with uniform conductive coating layers as promising electrodes in Li-Ion batteries, (c) advanced bio-ceramic oxides such as hydroxy-apatite ceramic materials with right amounts of moisture, density and composition consistency as bone and dental implants in bio-ceramics research are examples. Among the many processing methods to achieve the base powders from nano ceramic raw materials the most capable and efficient is 'Spray Drying' which results in powders with high purity with well-defined properties. Complex composite by spray drying is achieved where the 'matrix host' material is encapsulated by the 'guest layer' with special properties. This paper illustrates results pertaining to experimentation via spray drying and microscopic investigation by using SEM associated with EDS on (a) Yttria stabilized zirconia plasma sprayable powders for Thermal Barrier Coatings application and (b) nano yttria stabilized zirconia incorporated into microns sized alumina powders for enhanced densification, to understand the significant role of process parameters on uniformity and consistency of the spray dried products. Information based on review on spray dried Li-ion battery materials is also included. Published under licence by IOP Publishing Ltd. -
Stability Analysis of AFTI-16 Aircraft by Using LQR and LQI Algorithms
The stability analysis of the dynamical system of linearized plant model of Advanced Fighter Technology Integration (AFTI)-16 aircraft was proposed along with the optimal control methods by applying linear quadratic regulator (LQR) and linear quadratic algorithm (LQI) algorithms. The LQR and LQI algorithms results were compared with state-space model analysis results. The state-space methods like pole placement method, without using the LQR algorithm the negative feedback system were found to be unstable. By the application of LQR and LQI algorithms to the linearized plant AFTI-16 aircraft open-loop system having negative feedback found to be stable. The stability parameters were verified by using MATLAB programming software. The eigenvalues play a key role in finding closed-loop system stability analysis. MIMO dynamical system with state feedback gain matrices is calculated by using MATLAB programming software. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Stability Analysis ofSalt Fingers forDifferent Non-uniform Temperature Profiles inaMicropolar Liquid
This paper describes the linear stability analysis of salt finger convection for different non-uniform temperature profiles by keeping the solutal concentration uniform throughout the system. The system consists of two parallel plates separated by a thin layer of micropolar liquid with infinite length, in which the system is heated and soluted from above the plate. Normal mode techniques are used to convert the system of partial differential equations into ordinary differential equations; further, Galerkian method is introduced to get the eigenvalue for isothermal, permeable with no-spin boundary conditions. The study also explains the effect of different micropolar parameters on the onset of convection. The phase of temperature flow for different boundary conditions explains the graphical solution of the energy equation and its gradients. It is shown that non-uniform temperature profiles, diffusivity ratio, coupling parameter, and solutal Rayleigh number influence the stability of the system. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Stacked LSTM a Deep Learning model to predict Stock market
The goal of Stock Market Prediction is to forecast the future value of a company's financial stocks. The use of machine learning and deep learning technologies in stock market prediction technologies is a recent trend. Machine learning makes predictions based on the values of current stock market indices by training on their previous values in sequential timely order using the artificial neural network, while deep learning makes predictions based on the values of current stock market indices by training on their previous values in sequential timely order using the artificial neural network. 2022 IEEE. -
Stalling angle predictions of symmetric aero foil by flow analysis
The computational examination of the 2D subsonic stream over a National Advisory Committee for Aeronautics (NACA) 0012 airfoil at different approaches is done in an ANSYS R14.5 software. The stream elements are acquired by computing the fluid stream conditions of coherence and energy, momentum conservation with the K- model. The present investigation is fundamentally significant, ascribed to the way that the considered aero foil creates lift by moving rapidly through the air, and the examination of the flow over aero foil helps in execution assessment of the flight of the aero foil. In the current work, for a given stream speed with the aero foil set at an approach to the airstream, a pressure gradient among upper and lower wing surfaces exists, which involves the generation of lift, and the lift generated increases up to a certain angle of attack, beyond which the lift decreases, this particular point gives rise to stalling. The distinction in these pressing factor is very important to understand the effect of angle of attack on the lift of the wing. Consequently lift is a significant aspect to be evaluated for stalling angle predictions. The steps associated with assessment of the lift includes forecast of stalling angle at different flow conditions over a symmetric aero foil by modeling the NACA0012 aero foil using the co-ordinates provided by the NACA database and henceforth bringing in it to the ANSYS work bench for meshing, preprocessing followed by the interaction of post processing for different angles of attack. 2022 Author(s). -
State-of-art Techniques for Classification of Breast Cancer: A Review
Cancer is an unexpected and unclear disease that puts many people at risk. Breast cancer has surpassed prostate cancer as the most common cancer in women, as well as the main cause of cancer-related mortality in women. Breast cancer rates have been rising in India for several years, with 100,000 new cases recorded each year. In India, there are up to one million breast cancer patients at any given moment. The survival rate of breast cancer has increased in recent years as a result of advances in technology, effective treatment, and medical care delivery. It extends the lives of the sufferers and improves their quality of life. Breast cancer can be detected using a variety of imaging methods. Radiologists can utilize a computer-aided diagnostic technique to discover and diagnose irregularities earlier and more quickly. Many Computer-Aided Diagnosis methods have been developed to identify breast cancer in its early stages using mammography images. The computer aided diagnostics systems mostly focus on identifying and detecting breast nodules. Staging breast cancer at its detection needs to be focused on, as the treatment is based on the stage of cancer. As a result, this study focuses on producing evaluations on computer aided diagnostics approaches for segmenting nodules and identifying different stages of breast cancer, thereby assisting radiologists in assessing the illness. 2022 IEEE. -
Statistical Analysis of Ecological Mathematical Model Based on Data Warehouse
Persistence of ecosystems, existence and stability of periodic and almost periodic solutions, and global attractiveness are important research contents in ecological mathematical theory. This article takes the ocean as an example to illustrate. The marine ecological model management system integrates marine technology, Internet technology and database technology. The purpose is to collect, organize and analyze mathematical models related to marine ecosystems, integrate them according to certain classification principles, and store them in the form of text. In the database, the query of the database according to the important parameters in the mathematical model or the classification of the mathematical model is provided on the Internet, and the queried mathematical model is displayed on the screen through the browser. This paper adopts the method of data warehouse. How to effectively use resources is an important aspect of whether to take the initiative in competition. Data warehouse can play the characteristics of information processing and has broad application prospects in the face of competition in the field of telecommunications. 2023 IEEE. -
Steganography using Improved LSB Approach and Asymmetric Cryptography
Steganography deals with the craft of obscuring private data inside a spread media. In confidential data communication security is a vital issue. In this paper, we use a two-layer security. At first, data encryption is achieved by the method of RSA algorithm of asymmetric cryptography, and later the ciphered data is hidden into host image by an innovative embedding technique. To hide our ciphered data into host image, we modify the existing LSB technique and use a mapping function that ensures a secure and confidential image steganography resulting in a stego image. Here cryptography is blended with steganography and provides two level security in the confidential data transmission over the internet. 2020 IEEE. -
Stir Speed and Reinforcement Effects on Tensile Strength in Al-Based Composites
This study focuses on the preparation of Al-based hybrid composites using AA7475 as the main alloy reinforced with two materials, ZrO2 and SiC. The combination of stir-squeeze processing techniques was employed to create various specimens by varying four parameters: Stir-speed, Stir-time, reinforcements, and squeeze pressure. Taguchi design was utilized to generate specimens for analyzing their mechanical properties, specifically tensile strength, hardness, and porosity.The results indicated that the highest porosity (4.44%) was observed in the L16 test, with a combination of 700rpm stir speed, 25 mins stir time, 2wt% reinforcements, and 80MPa squeeze pressure. On the other hand, the lowest porosity (2.61%) was found in the L7 test, with 800rpm stir speed, 30 mins stir time, 2wt% reinforcements, and 100 MPa squeeze pressure.Regarding tensile strength (UTS), the maximum value (285.23MPa) was achieved in the L13 experiment, while the minimum value (187.58 MPa) was observed in the L1 experiment. This variation in UTS can be attributed to the applied load, the strengthening effect of the reinforcements, and the grain size of SiC. 2024 E3S Web of Conferences -
Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review
This paper systematically reviews the literature related to stock price prediction systems. The reviewers collected 6222 research works from 12 databases. The reviewers reviewed the full-text of 10 studies in preliminary search and 70 studies selected based on PRISMA. This paper uses the PRISMA-based Python framework systematic-reviewpy to conduct this systematic review and browser-automationpy to automate downloading of full-texts. The programming code with comprehensive documentation, citation data, input variables, and reviews spreadsheets is provided, making this review replicable, open-source, and free from human errors in selecting studies. The reviewed literature is categorized based on type of prediction systems to demonstrate the evolution of techniques and research gaps. The reviewed literature is 7 % statistical, 9% machine learning, 23% deep learning, 20% hybrid, 25% combination of machine learning and deep learning, and 14% studies explore multiple categories of techniques. This review provides detailed information on prediction techniques, competitor techniques, performance metrics, input variables, data timing, and research gap to enable researchers to create prediction systems per technique category. The review showed that stock trading data is most used and collected from Yahoo! Finance. Studies showed that sentiment data improved stock prediction, and most papers used tweets from Twitter. Most of the reviewed studies showed significant improvements in predictions to previous systems. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Stock market prediction using DQN with DQNReg loss function
There have been many developments in predicting stock market prices usingreinforcement learning. Recently, Google released a paper that designed a new loss function,specifically for meta-learning reinforcement learning. In this paper, implementation is doneusing this loss function to the reinforcement learning model, whose objective is to predict thestock price based on certain parameters. The reinforcement learning used is an encoderdecoderframework that is useful for extracting features from long sequence prices. TheDQNReg loss function is implemented in the encoder-decoder model as it could providestrong adaptation performance in a variety of settings. The model can buy and sell the index, and the reward is the portfolio return after the days trading has concluded. To maximizeyield the model must optimize reward function. The DQNReg loss implemented DQN network and the Huber loss DQN network is compared with the Sharpe ratio considered for return. 2024 The Author(s).