Browse Items (16481 total)
Sort by:
-
A thorough investigation of various goals and responses for mobile software-defined networks
Cloud computing has caused some companies to modify their IT infrastructure and maintenance procedures and may eliminate their current hardware altogether. Conventional methods of setting up a switch or router may be error-prone and unable to make full use of the capabilities of current network architectures. As many intelligent networking designs as possible must be developed for intellectualization, activation, and customization in future networks. Due to software-defined networking (SDN) technology, it's possible to control, secure, and optimize network resources, eliminating the rigid coupling between the control plane and the data plane in traditional network architectures. Here, the chapter explores the problems, difficulties, and potential solutions associated with software-defined networks (SDN), a novel concept in computer networking. Through SDN, the network gains the ability to be programmable, quick, and adaptable thanks to its separation of data and its ability to control traffic. 2023, IGI Global. All rights reserved. -
A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis
Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelop-mental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitiv-ity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, at-lases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodolo-gy. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an im-pressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse da-tasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support. 2024 Bentham Science Publishers. -
A Three-Species Model With Predator-Taxis Sensitivity: Hopf Bifurcation and Active Control Stabilization
This study presents an analysis of a novel fractional two preyone predator model incorporating predator-taxis sensitivity. We conduct a comprehensive stability analysis, explore the model's chaotic nature through period-doubling bifurcations, and also show the existence of limit cycles through fractional Hopf bifurcation. It is observed that the fractional-order parameter brings in a stabilizing effect and, simultaneously, a shift of the Hopf bifurcation point. At the Hopf bifurcation point, the system moves from stable equilibria to sustained oscillations. In addition, regardless of initial conditions, the system approaches a stable limit cycle, showing the robustness of the method. We also demonstrate the effectiveness of the active control method to eliminate the periodicity of the fractional system and also unravel the decelerating influence of the fractional-order parameter on the convergence time to equilibrium. These results provide valuable insights into the stabilization of ecosystem dynamics and contribute more broadly to our understanding of population dynamics in ecological systems. 2025 John Wiley & Sons Ltd. -
A top-down approach for studying the in-silico effect of the novel phytocompound tribulusamide B on the inhibition of Nipah virus transmission through targeting fusion glycoprotein and matrix protein
The proteins of Nipah virus ascribe to its lifecycle and are crucial to infections caused by the virus. In the absence of approved therapeutics, these proteins can be considered as drug targets. This study examined the potential of fifty-three (53) natural compounds to inhibit Nipah virus fusion glycoprotein (NiV F) and matrix protein (NiV M) in silico. The molecular docking experiment, supported by the principal component analysis (PCA), showed that out of all the phytochemicals considered, Tribulusamide B had the highest inhibitory potential against the target proteins NiV F and NiV M (-9.21 and ?8.66 kcal mol?1, respectively), when compared to the control drug, Ribavirin (-7.01 and ?6.52 kcal mol?1, respectively). Furthermore, it was found that Tribulusamide B pharmacophores, namely, hydrogen donors, acceptors, aromatic and hydrophobic groups, contributed towards the effective residual interactions with the target proteins. The molecular dynamic simulation further validated the results of the docking studies and concluded that Tribulusamide B formed a stable complex with the target proteins. The data obtained from MM-PBSA study further explained that the phytochemical could strongly bind with NiV F (-31.26 kJ mol?1) and NiV M (-40.26 kJ mol?1) proteins in comparison with the control drug Ribavirin (-13.12 and ?13.94 kJ mol?1, respectively). Finally, the results indicated that Tribulusamide B, a common inhibitor effective against multiple proteins, can be considered a potential therapeutic entity in treating the Nipah virus infection. 2024 Elsevier Ltd -
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 Travellers Melancholy
On possibly the most humid day in the month of June, I lay on a bed in a backpacking hostel, journalling my melancholy. 2025, The Assosiation FormAkademisk. All rights reserved. -
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 turn-on bis-hydrazone fluorescent chemosensor for selective Cd2+ detection: synthesis, structural insights, and theoretical validation
Heavy metal pollution, particularly from cadmium(ii) ions (Cd2+), causes severe environmental and health risks due to its acute toxicity, carcinogenicity, and bioaccumulation, leading to kidney damage, neurological disorders, and other physiological issues. Herein, we report the one-pot synthesis of a bis-hydrazone-based fluorescent probe L2H2O (1) for selective detection of Cd2+. Probe 1 was derived from isophthalaldehyde and 3-pyridylcarbonyl hydrazine and single-crystal X-ray diffraction discloses a well-defined binding pocket with pyridyl, imine, and carbonyl donor sites suitable for Cd2+ coordination. Probe 1 exhibits weak emission in CH3CN/HEPES buffer (9?:?1, v/v, pH 7.4) due to photoinduced electron transfer (PET) and unrestricted intramolecular rotations. Upon selective binding to Cd2+, 1 displays a pronounced turn-on fluorescence response with intensity enhancements of at ?324 nm and at 420 nm, accompanied by bathochromic shifts to 327 nm (?? = 3 nm) and 445 nm (?? = 25 nm) (?ex = 295 nm). The limit of detection (LOD) for probe 1 with metal Cd2+ is 3.39 M, with a binding constant of 5 103 M?1. 1H NMR titration, DFT-optimized geometries (B3LYP/6-31+G(d)/LANL2DZ), and simulated UV-Vis spectra further confirm binding of Cd2+, blocking PET and rigidifying the structure via chelation-enhanced fluorescence (CHEF). This work presents a modular hydrazone scaffold for developing selective Cd2+ sensors with potential application in environmental and biological monitoring. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique, 2026. -
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 Univariate and Multivariate Time Series Analysis for the Prediction of Maize Production in India
This study examines the use of time series analysis for predicting maize production in India. The objective is to analyze the relationship between maize productions, domestic consumption, exports, and to forecast maize production using various time series models. The study employs cointegration techniques such as Johansen's test, Engle-Granger test, and Granger causality test to determine the long-term relationship between the variables. The findings exhibit that there is a bidirectional causal relationship between domestic consumption and export series and between domestic consumption and production series and that all three variables are co-integrated. To forecast maize production, the study employs both multivariate and univariate time series models. The multivariate models used are vector auto regressive and vector error correction models, while the univariate models used are ARIMA (auto regressive integrated moving averages), Holts exponential smoothing, NNAR (neural network auto regression), K-nearest neighbors (KNN), and LSTM (long short-term memory). The best forecast model is selected on the basis of a comparison of three evaluation metrics: mean absolute square error, mean absolute percentage error, and root mean square log error. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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 Verilog-Based Design Framework for Real-Time Edge Detection in Image Processing
This article details the design process of a real-time image processing system developed in Verilog. The design has proven highly effective in real-time image acquisition, buffering, and processing, with a focus on hardware and performance optimization. The principal modules are a line buffer for image frame storage, a convolution engine featuring edge detection filters such as Sobel and Prewitt, and a control unit responsible for data flow and synchronization. The architecture facilitates the transmission of image data from a camera, with processed images transmitted via VGA/HDMI interfaces. Focus is placed on attaining low latency, high throughput, and optimal utilization of FPGA platform resources. The technology is particularly relevant for autonomous systems, medical imaging, industrial automation, and surveillance, where real-time edge detection is crucial for decision-making. 2025 IEEE.