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Facial Emotion Detection Using Deep Learning: A Survey
The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE. -
Real-Time Traffic Sign Detection Under Foggy Condition
Traffic congestion becomes high in urban areas and using public and private transportation services. The image of traffic signs gets affected by fog, and the detection of traffic signs has become difficult. To solve this issue, the machine learning technique has been used. Convolution neural network helps to solve real-time problems; hence, it can be used in the study for detecting traffic signs under foggy condition. The study results revealed that the model network has accuracy of 99.8%, and the proposed algorithm detects a traffic sign under foggy conditions in 2s per frame. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
C-cordial labeling of line signed graphs-I
Let S=(G, ?) be a signed graph. S admits C-cordial labeling if the difference between the number of negative and positive edges (vertices) differ by at most one under canonical marking of S. In this paper, we characterize signed paths and cycles having given number of negative sections where the line signed graphs admit C-cordial labeling. 2020 Author(s). -
Unveiling the Landscape: A Comparative Study of U-Net Models for Geographical Features Segmentation
Geographical features segmentation is a critical task in remote sensing and earth observation applications, enabling the extraction of valuable information from satellite imagery and aiding in environmental analysis, urban planning, and disaster management. The U-Net model, a deep learning architecture, has proven its efficacy in image segmentation tasks, including geographical feature analysis. In this research paper, a comparative study of various U-Net models customized explicitly for geographical features segmentation is presented. The study aimed to evaluate the performance of these U-Net variants under diverse geographical contexts and datasets. Their strengths and limitations were assessed, considering factors such as accuracy, robustness, and generalization capabilities. The efficacy of integrated components, such as skip connections, attention mechanisms, and multi-scale features, in enhancing the models performance was analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Classification and analysis of Alzheimer's Disease using Deep Learning methods on MRI and PET
Alzheimer's disease (AD) falls in the category of neurodegenerative illness in which an individual loses his or her power to remember things and behaviors. It affects memory in younger patients and as it progresses causes diffuse cortical functions. However, a major issue with the diagnosis and treatment of AD symptoms is that it has complex pathogenesis because of which there is no clinical intervention for its treatment. There is no disease-modifying treatment to cure AD symptoms that increases co-morbidities among the patients. The present research identified this gap and focuses on using Deep Learning methods on MRI and PET data so that there is early diagnosis of AD by healthcare experts and they could propose a better treatment process for reducing AD symptoms. The present research identified that by using deep learning-based approaches particularly ResNet50 architecture, there is the execution of quantitative assessment of brain MRI and PET to acquire insights about the internal abnormalities through self-learning features. It will help in initiating proper treatment and avoiding damage to the brain further. 2022 IEEE. -
Classification on Alzheimers Disease MRI Images with VGG-16 and VGG-19
Balancing thoughts and memories of our life is indeed the most critical part of the human brain.Thus, its stability and sustenance are also important for smooth functioning.The changes in the structure can lead to disorders such as dementia and one such type of condition is known as Alzheimers disease.Multi modal neuroimaging like magnetic resonance imaging (MRI) and positron emission tomography (PET) is used for the early diagnosis of Alzheimers disease (AD) by providing complementary information.Different modalities like PET and MRI data were acquired from the same subject, there exists markable materiality between MRI and PET data.Mild cognitive impairment (MCI) is the initial stage with few symptoms of AD.To recognise the subjects which are capable of converting from MCI to AD is to be analysed for further treatments.In this research, specific convolutional neural networks (CNN) which are designed for classifications like VGG-16 and VGG-19 deep learning architectures were used to check the accuracy of cognitively normal (CN) versus MCI, CN versus AD and MCI to AD conversion using MRI data.The proposed research is analysed and tested using MRI data from Alzheimers disease neuroimaging initiative (ADNI). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Artificial Intelligence Involvement in Graphic Game Development
Games have always been a popular form of entertainment and with the advancements in technology, the integration of Artificial Intelligence (AI) in gaming has revolutionized the gaming industry. This research article aims to explore the various applications of AI in gaming and its impact on the industry and player experience. Unlike the typical straightforward nature of AI, this research paper takes a more human approach to discussing the topic. It delves into the evolution of AI in games and the various types of AI used in game development. These include rule-based AI, learning- based AI, and evolutionary AI, which have all contributed to the development of increasingly immersive gaming experiences. The benefits and challenges of using AI in games are also explored, considering the impact on player experience. While AI-powered opponents can provide a greater challenge, balancing the difficulty level is critical to ensuring the game remains enjoyable. The potential ethical concerns of using AI in games are also discussed, such as data privacy, bias, and fairness. Furthermore, this research paper looks into the future of AI in games and how it may shape the gaming industry and player experience in the years to come. With the continued development of AI techniques such as reinforcement learning and GANs, the possibilities for more immersive and engaging gaming experiences are endless. 2023 IEEE. -
Impact of Meltdown and Spectre Threats in Parallel Processing
[No abstract available] -
Optimal locations for PMUs maintaining observability in power systems
Population of Phasor Measurement Units (PMUs) in power systems are increasing day by day as PMUs measure the electrical quantities more accurately with time-stamping. The measurements done by PMU can be used for monitoring, controlling and for state estimation of the power system. Since the installation of PMUs demand high capital cost, their number and location to be chosen optimally is by minimizing investment without losing observability of the system. In this paper Integer Programming techniques used to solve Optimal Placement of PMU (OPP) problem. The OPP problem is solved for normal power system as well as for a few contingency conditions like one PMU outage, considering zero injection bus, outage of single line on various standard IEEE Bus Systems. The work is also trying to place PMUs under planned islanding in certain standard networks. 2016 IEEE. -
On Equitable Chromatic Completion of Some Graph Classes
An edge of a properly vertex-colored graph is said to be a good edge if it has end vertices of different color. The chromatic completion graph of a graph G is a graph obtained by adding all possible good edges to G. The chromatic completion number of G is the maximum number of new good edges added to G. An equitable coloring of a graph G is a proper vertex coloring of G such that the difference of cardinalities of any two color classes is at most 1. In this paper, we discuss the chromatic completion graphs and chromatic completion number of certain graph classes, with respect to their equitable coloring. 2022 American Institute of Physics Inc.. All rights reserved. -
On Circulant Completion of Graphs
A graph G with vertex set as {v0, v1, v2,.., vn-1} corresponding to the elements of Zn, the group of integers under addition modulo n, is said to be a circulant graph if the edge set of G consists of all edges of the form {vi, vj} where (i-j)(modn)?S?{1,2,,n-1}, that is, closed under inverses. The set S is known as the connection set. In this paper, we present some techniques and characterisations which enable us to obtain a circulant completion graph of a given graph and thereby evaluate the circulant completion number. The obtained results provide the basic eligibilities for a graph to have a particular circulant completion graph. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Predicting of Credit Risk Using Machine Learning Algorithms
Credit risk management is one of the key processes for banks and is crucial to ensuring the banks stability and success. However, due to the need for more rigid forecasting models with strong mapping abilities, credit risk prediction has become challenging for the banking industry. Therefore, this paper attempts to predict commercial banks credit risk (CR) by using various machine learning algorithms. Machine learning algorithms, namely linear regression, KNN, SVR, DT, RF, XGB, and MLP, are compared with and without feature selection and feature extraction techniques to examine their prediction capabilities. Various determinants of credit risk (features) have been extracted to predict credit risk, and these features have been used to train machine learning models. Findings revealed that the decision tree algorithm had the highest performance, with the lowest mean absolute error (MSE) value of 0.1637 and the lowest root mean squared error (RMSE) value of 0.2158. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
COVID-19 Pandemic: Review on Emerging Technology Involvement with Cloud Computing
Cloud computing is the latest technology that has a significant influence on everyones life. During the COVID-19 crisis, cloud computing aids cooperation, communication, and vital Internet services. The pandemic situation made the people switch to online mode. The technology helped to bridge the gap between the work space and personal space. A quick evaluation of cloud computing services to health care is conducted through this study in COVID situation. A short overview on how cloud computing technologies are critical for addressing the current predicament has been held. The paper also discusses distant working of cloud computing in health care. Moreover, cloud infrastructure provides a way to connect with different aid personnel. The patient data can be transferred to the cloud for monitoring, surveillance, and diagnosis. Thus, health care is provided instantaneously to all the individuals. Additionally, the study addresses the privacy and security-related issues with appropriate solutions. The paper also briefs on the different kind of services are provided by different CSPs that are cloud service providers to confront this epidemic. This article primarily focuses on cloud computing technology involvement in COVID, and secondary focus is on other technology like blockchain, drones, machine learning and Internet of things in COVID-19. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analytical Methods of Machine Learning Model for E-Commerce Sales Analysis and Prediction
In the commercial market, E-commerce sales show a significant trend and have attracted many consumers. Ecommerce sales forecasting has a significant role in an organization's growth and aids in improved operation. Many studies have been conducted in the past using statistical, fundamental, and data mining techniques for better analysis and prediction of sales. However, the current scenario calls for a better study that combines the available information to propose different machine-learning techniques. The sole motive of the study is to analyze and determine different machine learning models to predict accurate results. The research observed that the Extreme Gradient Boosting model outperformed all other models and brought a good result. It produced an RMSE value of 0.0004 and Explained Variance score of 0.99. Decision Tree algorithm also shows an exemplary result. 2023 IEEE. -
Maximum Decision Support Regression-Based Advance Secure Data Encrypt Transmission for Healthcare Data Sharing in the Cloud Computing
The recent growth of cloud computing has led to most companies storing their data in the cloud and sharing it efficiently with authorized users. Health care is one of the initiatives to adopt cloud computing for services. Both patients and healthcare providers need to have access to patient health information. Healthcare data must be shared and maintained more securely. While transmitting health data from sender to receiver through intermediate nodes, intruders can create falsified data at intermediate nodes. Therefore, security is a primary concern when sharing sensitive medical data. It is thus challenging to share sensitive data in the cloud because of limitations in resource availability and concerns about data privacy. Healthcare records struggle to meet the needs of security, privacy, and other regulatory constraints. To address these difficulties, this novel proposes a machine learning-based Maximum Decision Support Regression (MDSR)-based Advanced Secure Data Encrypt Transmission (ASDET) approach for efficient data communication in cloud storage. Initially, the proposed method analyzed the node's trust, energy, delay, and mobility using Node Efficiency Hit Rate (NEHR) method. Then identify the efficient route using an Efficient Spider Optimization Scheme (ESOS) for healthcare data sharing. After that, MDSR analyzes the malicious node for efficient data transmission in the cloud. The proposed Advanced Secure Data Encrypt Transmission (ASDET) algorithm is used to encrypt the data. ASDET achieved 92% in security performance. The proposed simulation result produces better performance compared with PPDT and FAHP methods. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Markov analysis of unmanned cryogenic nitrogen plant with standby system
The unmanned cryogenic nitrogen plants operated by the industrial gas companies globally have their unique set of Reliability, Availability and Maintainability challenges. A generic reliability model of a typical unmanned cryogenic nitrogen plant is presented in this research work along with standby cryogenic storage System. The standby system is analysed for sensing and switching device as well as for load sharing system. The complexities of unmanned cryogenic nitrogen plant under repair with standby system are analysed using Markov method. A Markovian model has been developed for two different configurations: Configuration-1: Cryogenic nitrogen plant with gas and liquid production, nitrogen plant with only gas production and its dependence on the standby cryogenic storage system and to overall system reliability. Configuration-2: Considering cryogenic nitrogen plant is under repair and the standby system with external supply component under operation without failure and the system reliability of the configurations are solved by solving the set of differential equations and the solutions are presented in this paper. 2020 Author(s). -
A Study of Emotion Classification of Music Lyrics using LSTM Networks
Emotion Recognition is a vital component of human-computer interaction and plays a pivotal role in applications such as sentiment analysis, virtual assistants, and affective computing. Long Short-Term Memory (LSTM) models are a subset of Recurrent Neural Networks (RNNs). It has gained significant popularity for their effectiveness in sequence modeling tasks, including emotion recognition. The study presents a review on the application of Long Short-Term Memory (LSTM) networks for emotion classification using music lyrics. It offers a thorough review of relevant literature and outlines the methodology for implementing LSTM models for emotion recognition. Furthermore, the study emphasizes the significance of hyperparameter tuning in building effective machine-learning models, particularly LSTM-based models. 2024 IEEE. -
Lightweight Anti DDoS Security Tool: Edge Level Filtering in SDN using P4
Software Defined Network (SDN) which has a promising future in satellite communication was first introduced as the solution to solve problems existing in the traditional network architecture. So far in SDN, mitigation strategies employed hardware installation or software solution which is heavily dependent on SDN controllers. The disadvantage of these approaches is the a) cost for implementation, b) intensive resource usage, and 3) costly optimization strategy necessary to enhance SDN performance. This research aims to fill the gap of the previously seen defense mechanism by enabling edge-level filtering without involving the control plane. By implementing filtering functions in edge switches, it can provide an efficient and effective defense layer in SDN network systems so that SDN switch can become the first line of defense against packet injection attacks. The proposed solution, Lightweight Anti-DDoS Software (LADS) focuses on lightweight workloads and provisioning of effective filtering mechanism to allow SDN switches to drop and block malicious packets sent by attackers. It utilizes Programming Protocol-independent Packet Processors (P4) programming language to create custom functionalities in SDN switches. P4 allows SDN switches to conduct host authentication and malicious packet filtering as well as blacklisting to isolate attackers. Simulation result proves that LADS efficiently manages malicious activities and maintains network performance during attacks at the data plane independent of SDN controller. 2023 IEEE. -
AUTONOMOUS IOT MOVEMENT IN HOSTILE AREAS USING ROBOTICS AND DEEP FEDERATED ALGORITHMS
Innovative solutions are required when Internet of Things (IoT) devices are deployed in hostile or difficult locations to ensure dependable and effective operation. In order to enable autonomous IoT mobility in such challenging circumstances, this study suggests a novel approach integrating robotics and deep federated algorithms. Robotics and IoT can work together to create a system that can adapt to dangerous environments, extreme weather conditions, and unexpected terrain. Deep federated algorithms further improve system performance by facilitating dispersed device collaboration for learning while protecting data privacy. The suggested framework covers the issues of communication stability, energy optimization, and real-time decision-making. We illustrate the practicality of this strategy in strengthening the dependability and efficiency of IoT deployments in hostile situations through simulations and tests. 2023 IEEE. -
A Heuristic Approach to Resolve Priority-Driven Unbalanced Transportation Problem (PUTP)
This research addresses the priority-driven unbalanced transportation Problem (PUTP), characterized by a situation where the overall demand surpasses the available supply. We propose the Max-flow Min-cost Priority-driven Unbalanced Transportation Problem (MMPUTP) as a heuristic approach to handle this issue effectively. The strategy of MMPUTP focuses on optimizing resource allocation and reducing costs, making it highly effective in fulfilling high priority needs in a cost-efficient manner. Through a comparison with Vogel's Approximation Method (VAM) over different sets of problems ranging in size from 5?5 to 50?50, the effectiveness of the MMPUTP algorithm is evident. The findings underscore the significance of choosing the right algorithm based on the size and complexity of the problem set in the context of the Priority-driven Unbalanced Transportation Problem, with MMPUTP proving to be a flexible and reliable option in various situations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.