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A Translator for Indian Sign Boards to English using Tesseract and SEQ2SEQ Model
Language translator for Indian language to English have been developed and it have proven to a challenging domain due to large combination of character in Indic scripts such as Tamil, Kannada and Hindi. In this paper we propose a system which captures Indian printed character and translates it into English, we have discussed the various method and machine learning model that was used to build this system with an accuracy of 87%. 2021 IEEE. -
A Trust Model for Cloud Security and Privacy for Startups in India
The great advantage of cloud computing is elasticity, the ability to add capacity or applications almost at a moments notice. Companies buy exactly the amount of storage, computing power, security and other IT functions that they need from specialists in data-centre computing. On the other side, there are also some disadvantages to using cloud computing that must be considered. In the cloud, the customer may not have the kind of control over their data or the performance of the applications that they need, or the ability to audit or change the processes and policies under which users must work. The security of cloud, and associated privacy concerns, give many organizations pause as they think through their particular cloud computing concerns. Security and privacy concerns include physical security and simple access to facilities and equipment, as well as logical security, industry compliance requirements, audit ability, and more. -
A Two-Pass Hybrid Mean and Median Framework for Eliminating Impulse Noise From a Grayscale Image
In a digital era, Image recuperation plays a vital role in the area of digital image processing. Image instauration offers more visualization on the quality of the image thereby eliminating noise. Elimination of Gaussian and impulse noise is a challenging problem in the area of image restoration. Rigorous research is pursued to restore salt-and-pepper (SAP) noise utilizing spatial filters. Mean and Median are two contributing spatial filters for eliminating impulse noise. This paper applies a two-pass hybrid mean and median framework on a corrupted grayscale image to replace salt and pepper noise. The hybrid framework is effectively restoring the image by abstracting the low, medium, and high-density impulse noise. The efficacy of the recommended strategy is evaluated by quantifying the peak signal to noise ratio and structural similarity index metric. The result obtained when compared with recent recuperation strategies outperforms to remove noise from grayscale images. 2021 IEEE -
A two-stepped feature engineering process for topic modeling using batchwise LDA with stochastic variational inference model
Online ratings and customer feedback on hotel booking websites support the decision-making process of the customer as the reviews provide a deeper understanding about all aspects of a hotel. Consequently, review and rating analyses are of great interest to consumers and hotel owners for the hotel related social media services. The key challenge, however, is to make the wide variety of information accessible in a simple, fast and relevant way and the solution is Topic Modelling and Opinion Mining. Common approaches like Latent Semantic Analysis (LSA) and Hierarchical Dirichlet Process (HDP) have order affects. If the input dataset is shuffled then different topics are generated leading to misleading results. To overcome this, a two-stepped feature engineering process is used: first step is to use a TF-IDF with modified trigrams calculation followed by the second step in removing weak features from the corpus thereby reducing the dimensionality of the Vector Space Model (SVM) for efficient Topic Modeling and sentiment analysis of the considered corpus. Sentiment score is calculated using VADER tool and Topic Modeling is done with Batch Wise Latent Dirichlet Allocation (LDA) using Stochastic Variational Inference (SVI) model. The modified trigrams included calculation of probabilities of words not only in the backward direction but also the probability calculation of the next two words of the target word thereby retaining its context information. The proposed method using Batchwise LDA with SVI along with two-stepped feature engineering process considerably improved its performance when compared to LSA and HDP models due to the fact of identifying hidden and relevant topics in terms of their optimized posterior distribution in hotel reviews dataset. The Batchwise LDA with SVI improved its performance by 3% in terms of its coherence values by using two-stepped feature engineering process and by 9% and 4% increase when compared with LSA and HDP models respectively. 2020, Intelligent Network and Systems Society. -
A uGMRT search for radio emission from planets around evolved stars
In this work, we present the results from a study using the Giant Meterwave Radio Telescope (GMRT) to search for radio emission from planets around three evolved stars namely ? Tau, ? UMi, and ? Gem. Both ? Tau and ? UMi host massive ? 6 MJ mass planets at about ?1.4 au from the central star, while ? Gem is host to a 2.9 MJ mass planet at 1.7 au from the host star. We observe ? Tau and ? UMi at two upgraded GMRT bands: band 3 (250500 MHz) and band 4 (550900 MHz). We also analysed the archival observations from ? Gem at 150 MHz from GMRT. We did not detect any radio signals from these systems. At 400 MHz, the 3? upper limit is 87 ?Jy beam?1 for ? Tau b and 77.4 ?Jy beam?1 for ? UMi b. From our observations at 650 MHz, we place a 3? upper limit of 28.2 ?Jy beam?1 for ? Tau b and 33.6 ?Jy beam?1 for ? UMi b. For ? Gem b, at 150 MHz, we place an upper limit of 2.5 mJy. At 400 and 650 MHz, our observations are the deepest radio images for any exoplanetary system. The Author(s) 2024. -
A Unified Approach to Predict and Understand Acute Myeloid Leukemia Diagnosis
Acute myeloid leukemia (AML) is a rapidly progressing disease that affects myeloid cells in blood and bone marrow. These abnormal cancerous cells called blast cells are non-functional cells that increase rapidly in bone marrow and are released into blood stream which crowd out the healthy functional cells leading to weak immune system. This life-threatening disease needs to be diagnosed at early stage and hence requires fully automated system for early detection of leukemia to aid pathologists and doctors. Most of the automated machine learning and AI models are not transparent and require techniques to explain model prediction. This paper presents methods to classify blood microscopic images into healthy or acute myeloid leukemia. Among all the methods implemented, Gradient Boosting outperforms with an accuracy of 96.67%. This paper also focuses on explainable AI to interpret model prediction and feature importance which further helps in understanding decision-making process of classification model and optimize it. 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Unified Approach to Two-Dimensional Brinkman-Bard Convection of Newtonian Liquids in Cylindrical and Rectangular Enclosures
A unified model for the analysis of two-dimensional BrinkmanBard/RayleighBard/ DarcyBard convection in cylindrical and rectangular enclosures ((Formula presented.)) saturated by a Newtonian liquid is presented by adopting the local thermal non-equilibrium ((Formula presented.)) model for the heat transfer between fluid and solid phases. The actual thermophysical properties of water and porous media are used. The range of permissible values for all the parameters is calculated and used in the analysis. The result of the local thermal equilibrium ((Formula presented.)) model is obtained as a particular case of the (Formula presented.) model through the use of asymptotic analyses. The critical value of the Rayleigh number at which the entropy generates in the system is reported in the study. The analytical expression for the number of Bard cells formed in the system at the onset of convection as a function of the aspect ratio, (Formula presented.), and parameters appearing in the problem is obtained. For a given value of (Formula presented.) it was found that in comparison with the case of (Formula presented.), more number of cells manifest in the case of (Formula presented.). Likewise, smaller cells form in the (Formula presented.) problem when compared with the corresponding problem of (Formula presented.). In the case of (Formula presented.), fewer cells form when compared to that in the case of (Formula presented.) and (Formula presented.). The above findings are true in both (Formula presented.) and (Formula presented.). In other words, the presence of a porous medium results in the production of less entropy in the system, or a more significant number of cells represents the case of less entropy production in the system. For small and finite (Formula presented.), the appearance of the first cell differs in the (Formula presented.) and (Formula presented.) problems. 2023 by the authors. -
A unifying computational framework for fractional Gross-Pitaevskii equations
This paper concerns investigating the complex behaviour of the special case of Schringer equation called Gross-Pitaevskii (GP) equations using -homotopy analysis transform method (-HATM) with fractional order. Based on denticity function and different initial conditions, we consider three different examples to demonstrate the proficiency of -HATM. We consider different initial conditions for the hired system and the projected method is elegant unification of -homotopy analysis algorithm and Laplace transform. Further, the physical natures of the achieved results have been captured for change in space, time, homotopy parameter and fractional order in terms of contour and surface plots, and the accuracy is presented with the numerical study. The obtained results conclude that, the hired technique is highly methodical, easy to implement and accurate to examine the behaviour of the nonlinear equations of both fractional and integer order describing allied areas of science. 2021 IOP Publishing Ltd. -
A unique adventure - unity based 3D game
The number of gamers are increasing day by day and as a result the gaming industry has seen a huge growth. There was a curiosity to get the in-depth detail so as to how a game is developed. The final year project was a great opportunity to explore this field and to make something that would be fun as well as useful. The proposed work gives the detailed description about the entire process of game development. A game is created with three different levels. Each level comes with a particular set of objectives. The objectives of each level need to be attained in order to proceed to the next level. The environment in the game resembles the Christ Kengeri campus. For that, 3D model of Christ Kengeri campus is designed. 3D modeling is done in Unity and Blender software platform. A* is the search algorithm that has been used for pathfinding. The languages that Unity uses to operate with are objectoriented scripting languages. Scripting languages have its own syntax and the primary parts are called functions, variables and classes. Also each level has its own coding and is not linked with any other. In each level a new character is introduced. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
A unique model for detection of health insurance fraud while improving UX using UI based on emotion cue /
Patent Number: 201941039391, Applicant: Dr. Ilango Velchamy.
The patent description consists of an inclusive model to create a better user experience. This patent primarily envisages creation of awareness among the users about the various product offerings of the companies and to detect frauds. It shall include 6 levels such as inclusion of ICT, Customer Experience, Customer journey, Emotion experience, constructing a methodology, optimization and personalization. ICT media will be used to construct a better user interface. -
A unique stimulus-emotion-servicescape model for unsought goods that negates undesirability by capturing emotions and creates desirability through servicescape /
Patent Number: 201941039390, Applicant: Dr. V R Uma.
Unsought goods are those products that are desired when a problem occurs or those that require aggressive selling efforts. The unsought product identified to construct the Stimulus-Servicescape model is health insurance. The clusters identified for testing the model include, the users and the non users. The non-users were further bifurcated as those that were exposed to stimuli and those that were not. Hence, there are three clusters. -
A USB- bluetooth two factor mutual authentication security protocol for wireless sensor networks
Wireless sensor networks are easy to deploy, effective, and can monitor unattended environments. As the data transmitted through these networks is highly sensitive, the security of the networks is important and strong authentication measures must be in place. Authentication is done by means of a security protocol, wherein a user is authenticated through certain factors such as a smartcard or a password, and several mathematical calculations such as hashing, and XOR operations. Several previously proposed authentication protocols and their flaws are discussed in this paper. We propose a new two factor mutual authentication protocol using a USB-Bluetooth token as the second factor, to overcome the security flaws seen in previous schemes. We also provide security analysis as well as Scyther results in support of the proposed protocol. The proposed protocol can be used across various fields such as healthcare, agriculture, traffic monitoring etc. BEIESP. -
A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants
Agriculture is one of the primary occupations in many countries. Tomatoes are grown by many farmers in countries where the water resource is available in abundance. Improper methods of cultivation and failure to identify the diseases when it is in the nascent stage results in the reduction of crop yield thus affecting the outcome of cultivation. This paper proposes a novel method of early identification of diseases in tomato plants by making use of convolutional neural networks (CNN) and image processing. Dataset from an open repository was considered for training and testing and the algorithm was capable of identifying nine different varieties of diseases that affect the tomato plant at its early stages. The images of tomato leaves were fed for identification through processing and classification. An optimum model was developed by analyzing various architectures of CNN including the VGG, ResNet, Inception, Xception, MobileNet and DenseNet. The performance of each of these architectures was compared and various metrics like the accuracy, loss, precision, recall and area under the curve (AUC) were analyzed. 2022 World Scientific Publishing Company. -
A versatile sensor capable of ratiometric fluorescence detection of trace water and turn-on detection of Cu2+ modulating the binding interaction of a Cu(ii) complex with BSA and DNA complemented by docking studies
A fluorescent molecule, pyridine-coupled bis-anthracene (PBA), has been developed for the selective fluorescence turn-on detection of Cu2+. Interestingly, the ligand PBA also exhibited a red-shifted ratiometric fluorescence response in the presence of water. Thus, a ratiometric water sensor has been utilized as a selective fluorescence turn-on sensor for Cu2+, achieving a 10-fold enhancement in the fluorescence and quantum yield at 446 nm, with a lower detection limit of 0.358 ?M and a binding constant of 1.3 106 M?1. For practical applications, sensor PBA can be used to detect Cu2+ in various types of soils like clay soil, field soil and sand. The interaction of the PBA-Cu(ii) complex with transport proteins like bovine serum albumin (BSA) and ct-DNA has been investigated through fluorescence titration experiments. Additionally, the structural optimization of PBA and the PBA-Cu(ii) complex has been demonstrated by DFT, and the interaction of the PBA-Cu(ii) complex with BSA and ct-DNA has been analyzed using theoretical docking studies. 2024 The Royal Society of Chemistry. -
A Video Surveillance-based Enhanced Collision Prevention and Safety System
Road traffic crashes that result in fatalities have become a global phenomenon. Therefore, it is imperative to use caution and vigilance while being on the road. Human mistake, going over the speed limit, being preoccupied while driving or walking, disobeying safety precautions, and other factors can also contribute to such unforeseen accidents or injuries, which can result in both bodily and material loss. So, safety is what we seek to achieve. Furthermore, as the number of automobiles has increased, so too have collisions between vehicles and pedestrians. Using computer vision and deep learning approaches, this research seeks to anticipate such encounters. The data often comes from traffic surveillance cameras in video formats. We have therefore concentrated on video sequences of vehicle-pedestrian collisions. We begin with a detection phase that includes the identification of vehicles and pedestrians; for this phase, we employed YOLO v3 (You Only Look Once). YOLO v3 has 80 classes, but we only took six of them: person, car, bike, motorcycle, bus, and truck. Following detection, the Euclidean distance approach is used to determine the interspace between the vehicle and the pedestrian. The closer the distance between a vehicle and a pedestrian, the more likely it is that they will collide. As a result, pedestrians in risk are located, and once we are aware of the pedestrians in danger, we search for nearby safer regions to alert them to head to the nearest location that is secure. Grenze Scientific Society, 2023. -
A Voting Enabled Predictive Approach for Hate Speech Detection
In today's digital environment, hate speech, which is defined as disparaging and discriminating communication based on personal characteristics, presents a big difficulty. Hate crimes and the rising amount of such content on social media platforms are two examples of how it is having an impact. Large volumes of textual data require manual analysis and categorization, which is tedious and subject to prejudice. Machine learning (ML) technologies have the ability to automate hate speech identification with increased objectivity and accuracy in order to overcome these constraints. This article intends to give a comparative analysis of various ML models for the identification of hate speech. The proliferation of such content online and its negative repercussions on people and society are explored, as is the necessity for automated hate speech recognition. This paper intends to support the creation of efficient hate speech detection systems by performing a comparative analysis of ML models. Random forest records the best performance with higher accuracy and low response delay period for hate speech detection. The results will help enhance automated text classification algorithms and, in the end, promote a safer and more welcoming online environment by illuminating the benefits and drawbacks of various approaches. 2023 IEEE. -
A Way Towards Next-Gen Networking System for the Development of 6G Communication System
In this talk, the advancements announced by sixth-generation mobile communication (6G) as compared to the earlier fifth-generation (5G) system are carefully examined. The analysis, based in existing academic works, underscores the goal of improving diverse communication aims across various services. This study finds five crucial 6G core services designed to meet distinct goal requirements. To explain these services thoroughly, the framework presents two central features and delineates eight significant performance indices (KPIs). Furthermore, a thorough study of supporting technologies is performed to meet the stated KPIs. A unified 6G design is suggested, imagined as a combination of these supporting technologies. This design plan is then explained by the lens of five prototype application situations. Subsequently, possible challenges contained in the developing track of the 6G network technology are carefully discussed, followed by suggested solutions. The debate ends in an exhaustive examination of possibilities within the 6G world, seeking to provide a strategy plan for future research efforts. 2024 IEEE. -
A weighted-Weibull distribution: Properties and applications
The paper describes a two parameter model and its relationship to the widely used Weibull model. Mathematical properties of the distribution like survival and hazard functions, moments, harmonic and geometric means, Shannon entropy and mean residual life are derived. Different methods of estimation are discussed and a simulation study is performed to verify the efficiency of estimation methods. Applications of our distribution in different scenarios observed in real life areillustrated. 2023 John Wiley & Sons Ltd. -
A Worldwide Test of the Predictive Validity of Ideal Partner Preference Matching
Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference matching (i.e., Do people positively evaluate partners who match vs. mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered reportpartnered with the Psychological Science Acceleratoruses a highly powered design (N = 10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The corrected pattern metric that collapses across 35 traits revealed a zero-order effect of ? =.19 and an effect of ? =.11 when included alongside a normative preference-matching metric. Specific traits in the level metric (interaction) tests revealed very small (average ? =.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, mens and (especially) womens stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, mens stated preferences underestimatedand womens stated preferences overestimatedrevealed preferences. Implications for the literature on human mating are discussed. 2024 American Psychological Association -
Abjection and Intersecting Trans Women Identities: Examining Doing Gender through Malayalam Movies Ardhanaari and Njan Marykutty; [Abjeo e Identidades de Mulheres Trans Interseccionadas: Examinando Fazendo Gero atrav dos Filmes em Malayalam Ardhanaari e Njan Marykutty]; [Examinando hacer gero a trav de las pelulas en malayalam Ardhanaari y Njan Marykutty]
The non-confirmation to vexed societal gender norms places trans identities in an abjected state. Media, mainly cinema, plays an indispensable role in shaping, shunning, and promulgating such ideologies. To understand this discourse, the Malayalam films Ardhanaari (2015) and Njan Marykutty (2018) are taken to examine the question of abjection, a concept by Kristeva, and doing gender, by West and Zimmerman. The study argues that the abjection trans identities face forces them to perform their gender in accordance with cisnormative femininity. The study further argues that trans identities should embrace abjection and employ it as a political tool to disrupt the established hegemonic traditional gender structure and its definitions. 2023 Universidad de Guadalajara. All rights reserved.