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EFFECT OF MAGNETIC FIELD ON THE ONSET OF RAYLEIGH-B??NARD CONVECTION IN A MICROPOLAR FLUID WITH INTERNAL HEAT GENERATION
The effects of through flow, internal heat generation and magnetic field on the onset of Rayleigh-B??nard convection in electrically conducting Micropolar fluid are studied using the Galerkin technique. The eigenvalue is obtained for rigid-free velocity boundary combinations with isothermal and adiabatic on the spin-vanishing boundaries. A linear stability analysis is performed. The influence of various parameters on the onset of convection has been analyzed. The microrotation is assumed to vanish at the boundaries. A linear stability analysis is performed. The influence of various parameters on the onset of convection has been analyzed and their comparative influence on onset is discussed. The problem suggests an elegant method of external control of internal convection. -
Automatic Diagnosis of Autism Spectrum Disorder Detection Using a Hybrid Feature Selection Model with Graph Convolution Network
A neurodevelopmental disorder is called an autism spectrum disorder (ASD) that influences a persons assertion, interaction, and learning abilities. The consequences and severity of symptoms of ASD will vary from person to person; the disorder is mainly diagnosed in children aged 15years and older, and its symptoms may include unusual behaviors, interests, and social challenges. If it is not resolved at this stage, it will become severe in the coming days. So, in this manuscript, we propose a way to automatically tell if someone has ASD that works well by using a combination of feature selection and deep learning. Four phases comprise the proposed model: preprocessing, feature extraction, feature selection, and prediction. At first, the collected images are given to the preprocessing stage to remove the noise. Then, for each image, three types of features are extracted: the shape feature, texture feature, and histogram feature. Then, optimal features are selected to minimize computational complexity and time consumption using a new technique based on a combination of adaptive bacterial foraging optimization (ABFO), support vector machines-recursive feature elimination (SVM-RFE), minimum redundancy and maximum relevance (mRMR). Then, the graph convolutional network (GCN) classifier uses the selected features to identify an image as normal or autistic. According to the research observations, our models accuracy is enhanced to 97.512%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Multi-atlas Graph Convolutional Networks and Convolutional Recurrent Neural Networks-Based Ensemble Learning for Classification of Autism Spectrum Disorders
Autism spectrum disorder (ASD) has an influence on social conversation and interaction, as well as encouraging people to engage in repetitive behaviors. The complication begins in childhood and persists through adolescence and maturity. Autism spectrum disorder has become the most common kind of childhood development worldwide. ASD hinders the capacity to interact, socialize, and build connections with individuals of all ages, and thus its early intervention is critical. This paper discusses some of the most recent approaches to diagnostics using convolutional networks and multi-atlas graphs for autism spectrum disorders. Also, several pre-processing approaches are elaborated. Graph convolutional neural networks (GCNs) to diagnose autism spectrum disorder (ASD) because of their remarkable effectiveness in illness prediction using multi-site data. Convolutional neural network (CNN) and recurrent neural networks (RNN) infrastructure studies functional connection patterns between various brain regions to find particular patterns to diagnose ASD. In our research, we implemented the GCN + CRNN ensemble method and achieved 89.01% accuracy based on resting-state data from the fMRI (ABIDE-II), a novel framework for detecting early signs of autism spectrum disorders is presented and discussed. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An Efficient Security-Enabled Routing Protocol for Data Transmission in VANET Using Blockchain Ripple Protocol Consensus Algorithm
The security quality in Vehicular Ad-hoc NETworks (VANET) has improved as a result of recent developments in Intelligent Transportation Systems (ITS). However, within the current VANET system, providing a cheap computational cost with a high serving capability is a significant necessity. When a vehicle user goes between one Roadside Unit (RSU) to another RSU region in the current scenario, the current RSU periodically needs re-authentication of the vehicle user. This increases the computational complexity of the system. The gathering and broadcast of existing traffic event information by automobiles are critical in Vehicular Ad-hoc Networks (VANET). Traditional VANETs, on the other hand, have several security concerns. This work develops a blockchain-based authentication protocol to address the aforementioned difficulty. To address critical message propagation issues in the VANET, we invent a novel type of blockchain. We develop a local blockchain for exchanging real-world event messages among cars within a countrys borders, which is a novel sort of blockchain ideal for the VANET. We describe a public blockchain RPCA that records the trustworthiness of nodes and messages in such a distributed ledger suitable for secure message distribution. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
A Review on Deep Learning Algorithms in the Detection of Autism Spectrum Disorder
Autism spectrum disorder (ASD) is a neurodisorder that has an impact on how people interact and communicate with each other for the rest of their lives. Most autistic symptoms appear throughout the first two years of a child's life. This is why autism is called a behavioral disease. If you have a child with ASD, the problem starts in childhood and keeps going through adolescence and adulthood. Deep learning techniques are becoming more common in research on medical diagnosis. In this paper, there is an effort to see if convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), and a fusion technique known as convolutional recurrent neural network (CRNN) can be used to detect ASD problems in a child, adolescents, and adults. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Early Detection of Autism Spectrum Disorder Using PDNN and ABIDE I&II Dataset
The current study's objective was to use deep learning methods to separate valetudinarians amidst autism spectrum disorders (ASDs) from controls employing just the patients brain activation patterns from a dataset of large brain images. We examined brain imaging data from ASD patients from the global, multi-site ABIDE dataset (Autism Brain Imaging Data Exchange). Social impairments and repetitive behaviors are hallmarks of the brain condition known as autism spectrum disorder (ASD). ASD affects one in every 68 kids in the USA, as of the most recent data from the Disease Control Centers. To understand the neurological patterns that arose from the categorization, we looked into functional connectivity patterns that can be used to diagnose ASD participants precisely. The outcomes raised the state of the art by correctly identifying 72.10% of ASD patients in the sample vs. control patients. The classification patterns revealed an anti-correlation between the function of the brain's anterior and posterior regions; this anti-correlation supports the empirical data currently showing achingly ASD impedes communication between the livid brain's anterior and posterior areas. We found and pinpointed brain regions damn frolic, distinguishing ASD among typically developing reign according to our deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
AdvanDNN: Deep Neural Network Analysis of Neuroimaging for Identifying Vulnerable Brain Regions in Autism Spectrum Disorder
Exploring the neurological framework of autism spectrum disorder (ASD) presents a significant challenge due to its diverse manifestations and cognitive impacts. This study introduces an innovative deep learning approach, employing an advanced deep neural network (AdvanDNN) model to identify and analyze brain regions vulnerable to ASD. Utilizing the AAL116 brain atlas for anatomical standardization, our model processes a comprehensive set of neuroimaging data, including structural and functional MRI scans, to discern distinct neural patterns associated with ASD. The AdvanDNN model, with its robust deep learning architecture, was meticulously trained and validated, demonstrating a notable accuracy of 91.17% in distinguishing between ASD-affected individuals and controls. This marks an improvement over the state of the art, contributing a significant advance to the diagnostic processes. Notably, the model identified a pronounced anticorrelation in brain function between anterior and posterior regions, corroborating existing empirical evidence of disrupted connectivity within ASD neurology. The analysis further pinpointed critical regions, such as the prefrontal cortex, amygdala, and temporal lobes, that exhibit significant deviations from typical developmental patterns. These findings illustrate the potential of deep learning in enhancing early detection and providing pathways for intervention. The application of the AdvanDNN model offers a promising direction for personalized treatment strategies and underscores the value of precision medicine in addressing neurodevelopmental disorders. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Autism Spectrum Disorder: Automated Detection based on rs-fMRI images using CNN
Autism spectrum disorder (ASD) impacts approximately 1 in every 160 children globally and is classified as a neurodevelopmental condition. Image classification in neuroscience has advanced primarily due to convolutional neural networks (CNNs) and their capacity to provide better algorithms, more computing resources, and data. This study used a brain scan dataset to test the feasibility of utilizing CNN to detect ASD. Using functional connectivity patterns, the Autism Brain Imaging Exchange (ABIDE) data repository, which includes recordings of rest-state functional magnetic resonance imaging (rs-fMRI), the aim of using it was to distinguish between individuals who have Autism Spectrum Disorder (ASD) and those who are healthy controls. The proposed method effectively classified the two groups. According to the test findings, the suggested model has the ability to accurately detect ASD with a reliability rate of 92.22% when implemented on the ABIDE dataset using the CC200, CC400, and AAL116 brain atlases. The CNN model is computationally more efficient since it uses fewer parameters than other cutting-edge methods. 2023 IEEE. -
Enhanced Lumpy Cattle Skin Disease Prognosis via Deep Learning Methods
Animal illness is growing in importance. Identification of the illness is important since various diseases may affect different animals, and immediate guidance will be provided. Cows with lumpy skin issues are caused by the Neethling infection. The affection of these diseases causes lasting injury to the cattle's skin. Reduced Poor growth, reversal, milk production, gravidity, and, in severe cases, mortality are the most common adverse consequences of the illness. We developed a deep learning-based architecture that can predict or recognize disease. A deep literacy system is required to identify the microorganism causing the lumpy skin disease. This system collects diverse cattle electronic medical records and uses data analysis to create an intelligent diagnosis system for cattle diseases. It involves text preprocessing to enhance data quality, and the ECLAT algorithm correlates disease names with probabilities, providing tailored treatment plans. The system ensures timely disease treatment, reducing herders' losses and promoting scientific intelligence in animal husbandry. 2024 IEEE. -
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. -
Analysis of an Existing Method for Detecting Adversarial Attacks on Deep Neural Networks
Analyzes the existing method of detecting adversarial attacks on deep neural networks, proposed by researchers from Carnegie Mellon University and the Korean Institute of Advanced Technologies (KAIST) Ko, G. and Lim, G in 2021. Examines adversarial attacks, as well as the history of research on the topic. The paper considers the concepts of interpreted and not interpreted neural networks and features of methods of protection of the types of neural networks considered. The method for protecting against adversarial attacks is also considered to be applicable to both types of neural networks. An example of an attack simulation is given, which makes it possible to identify a sign showing that an attack has been committed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Gamification and Game-Based Learning: A Systematic Review and Comparative Analysis
In the modern world, characterized by the rapid development of technology and digitalization of almost all spheres of life, it is necessary to keep up with the times and gradually introduce information technology into our lives. This will allow us to remain competitive in a changing world, take advantage of new opportunities and improve our quality of life. It is important to understand that information technology is not just a fashion trend, but a necessary tool for successful development and progress. The paper examines the very concept of gamification, the main methods of introducing gamification into education, highlights the advantages of learning with the addition of gamification, and also works on comparing learning with and without gamification elements. The introduction of game elements into the educational process helps to improve the perception of educational material, as well as increase the level of motivation of the students themselves. It is worth noting that the learning process with the addition of game elements helps to improve attention, develop logical thinking, as well as analyze various situations. Gamification can be viewed from several angles. For a teacher, this teaching method will help to capture the attention of children, which will help create a working atmosphere in the classroom. And for students, gamification is a great opportunity to explore really important topics in game mode. They will have an increased interest in learning, which will have a beneficial effect on their further academic performance and learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Growth, survival and haemato-biochemical profiles of the freshwater catfish, Pangasius sutchi (Fowler, 1937) fingerlings fed with Tinospora cordifolia leaf extract supplemented diet
The present study documents the possible effects of Tinospora cordifolia leaf extract supplemented diets on growth, survival and haemato-biochemical profiles of the catfish, Pangasius sutchi fingerlings. P. sutchi fingerlings were fed with formulated diets, supplemented with four different concentrations of T. cordifolia leaf extract (viz. 100, 200, 400 and 800 mg kg-1 of feed) for 45 days. Fingerlings fed with basal diet served as control. Various parameters of serum biochemical and haematology such as serum total protein content, albumin content, globulin content, albumin globulin ratio, glucose, erythrocytes count, leucocytes count were evaluated along with growth parameters. The results indicated that Specific Growth Rate (SGR), Feed Conversion Ratio (FCR), Protein Efficiency Ratio (PER), survival and Haemato-biochemical profiles such as total serum protein, albumin, globulin, albumin globulin ratio and serum glucose were high in the fingerlings fed with T. cordifolia leaf extract supplemented diets, irrespective of dosage, compared to control. Among the four concentrations of T. cordifolia leaf extract used, 400 mg/kg of feed group showed increased growth, survival and enhanced the health status of P. sutchi fingerlings. 2020, Egyptian Society for the Development of Fisheries and Human Health. All rights reserved. -
Micro grid Communication Technologies: An Overview
Micro grid is a small-scale power supply network designed to provide electricity to small community with integrated renewable energy sources. A micro grid can be integrated to the utility grid. Due to lack of computerized analysis, mechanical switches causing slow response time, poor visibility and situational awareness blackouts are caused due to cascading of faults. This paper presents a brief survey on communication technologies used in smart grid and its extension to micro grid. By integration of communication network, device control, information collection and remote management an intelligent power management system can be achieved 2022 IEEE. -
Power Line Communication Parameters in Smart Grid for Different Power Transmission Lines
In an electrical power system smart grid is a network that renewable energy sources along with smart devices. Communication capabilities of the conventional grid can be improved by the inclusion of superior sensing and computing abilities. Device control, remote management, information collection, intelligent power management is achievable by using communication networks. Wired communication technology is used because of its advantages like reliable connection, free from interference, and faster speed. In this paper, the data communication parameters have been analyzed using Power Line Communication (PLC) with various lengths of transmission lines. An orthogonal Frequency Modulation scheme is used to obtain the minimum BER.MATLAB Programming has been carried out and the results have been compared with the standards and found to be satisfactory. 2021 IEEE. -
Design of digital filters for multi-standard transceivers
This paper addresses on three different architectures of digital decimation filter design of a multi-standard RF transceivers. Instead of using single stage decimation filter network, the filters are implemented in multiple stages using FPGA to optimize the area, delay and dynamic power consumption. The proposed decimation filter architectures reflect the considerable reduction in area and dynamic power consumption without degradation of performance. The filter coefficients are derived from MATLAB, the filter architectures are implemented and tested using Xilinx SPARTAN FPGA.First, the types of decimation filter architectures are tested and implemented using conventional binary number system. Then the two different encoding schemesi.e. Canonic Signed Digit (CSD) and Minimum Signed Digit (MSD) are used for filter coefficients and then the architecture performances are tested.The results of CSD and MSD based architectures show a considerable reduction in the area and power against the conventional number system based filter design implementation. The implementation results reflect that considerable reduction in area of 47.89% and dynamic power reduction of 28.64% are achieved using hybrid architecture. 2015 School of Electrical Engineering and Informatics. All rights reserved. -
Hybrid architecture of digital filter for multi-standard transceivers
This paper addresses on three different architectures of digital decimation filter design of a multi-standard RF transceivers. Instead of using single stage decimation filter network, the filters are implemented in multiple stages using FPGA to optimize the area and power. The proposed decimation filter architectures reflect the considerable reduction in area & power consumption without degradation of performance. First, the types of decimation filter architectures are tested and implemented using conventional binary number system. Then the two different encoding schemes i. e. Canonic Signed Digit (CSD) and Minimum Signed Digit (MSD) are used for filter coefficients and then the architecture performances are tested using FPGA. The results of CSD and MSD based architectures show a considerable reduction in the area & power against the conventional number system based filter design implementation. The implementation results reflect that considerable reduction in area of 25. 64% and power reduction of 16. 45% are achieved using hybrid architecture. Research India Publications. -
Digital filter architectures for multi-standard wireless transceivers
This paper addresses on two different architectures of digital decimation filter design of a multi-standard RF transceivers. Instead of using single stage decimation filter network, the filters are implemented in multiple stages using FPGA to optimize the area and power. The proposed decimation filter architectures reflect the considerable reduction in area & power consumption without degradation of performance. The filter coefficients are derived from MATLAB , the filter architectures are implemented and tested using Xilinx SPARTAN FPGA . The Xilinx ISE 9.2i tool is used for logic synthesis and the Xpower analysis tool is used for estimating the power consumption. First, the types of decimation filter architectures are tested and implemented using conventional binary number system. Then the different encoding scheme i.e. Canonic Signed Digit (CSD) representation is used for filter coefficients and then the architecture performance is tested .The results of CSD based architecture shows a considerable reduction in the area & power against the conventional number system based filter design implementation. -
Impact of lockdown during COVID-19 pandemic on the learning status of undergraduate and postgraduate students of Bangalore
Background: The COVID 19 pandemic has created various impacts on every human's life. COVID 19 lockdown has provoked enormous changes in the education sector which in turn influences the student's life in many aspects. The scope of this study is to understand the impact in both undergraduate and postgraduate students. Aim: This study aims at incisively analyzing the impact of lockdown imposed due to the COVID-19 pandemic on graduate students of Bangalore. Method: It is an online survey that encompasses a structural questionnaire with open-ended questions created using Google Forms, which were sent across the students through social media platforms. Results: A total of 115 students from both undergraduate and postgraduate programs have participated in this survey. Simple percentage distribution was estimated to evaluate the pedagogy, opinion on educational decisions, modes of learning, socio-economic conditions, and problems pertaining to academia because of this pandemic. As per this analysis, 80.9% of students faced difficulty due to lockdown. 67% of students thought that their family's income will be affected by this pandemic. 68.7% of students felt stressed, depressed and 52.3% of students could not find a suitable environment in their home to study during this lockdown. When we see this pandemic in an optimistic light, it has created various opportunities such as Digital learning and adoption of new health habits. 2021. RIGEO. All Rights Reserved. -
A Scoping Review of Formal Care to Children with Special Needs during the Covid-19 Pandemic
The Covid-19 pandemic caused an unprecedented closure of direct service for children with special needs (CSNs), which shifted service to remote mode. This scoping review analyzed the strategies adopted by different formal care services for CSNs, their strengths and weaknesses, and the challenges faced by the formal care providers (FCPs). This study identified relevant articles through academic databases and Google searches using appropriate search strings and keywords. It included ten journal articles (n=10) and eight pieces (n=8) of grey literature through a meticulous selection process and extracted data. This review drew results by collating the descriptive numerical data analysis and qualitative thematic analysis and interpreting them. Reporting incor-porated all the possible items recommended by the PRISMA-ScR guidelines. This review demonstrated that pediatric rehabilitation adopted the telehealth approach and that special education changed to remote learning. When childcare programs in the USA functioned according to specific guidelines, residential care in South Asian countries faced a financial crunch. FCPs faced personal and professional challenges that required systematic training to deal with pandemic situations. This scoping review made suggestions for relevant policy formulations for equitable and effective service delivery to CSNs during pandemic situations, and it exposed new avenues for research. 2022 Authors.