Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of...
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Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing...
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Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing *** manual forgery localization is often reliant on forensic *** recent times,machine learning(ML)and deep learning(DL)have shown promising results in automating image forgery ***,the ML-based method relies on hand-crafted ***,the DL method automatically extracts shallow spatial features to enhance the ***,DL-based methods lack the global co-relation of the features due to this performance degradation noticed in several *** the proposed study,we designed FLTNet(forgery localization transformer network)with a CNN(convolution neural network)encoder and transformer-based *** encoder extracts local high-dimensional features,and the transformer provides the global co-relation of the *** the decoder,we have exclusively utilized a CNN to upsample the features that generate tampered mask ***,we evaluated visual and quantitative performance on three standard datasets and comparison with six state-of-the-art *** IoU values of the proposed method on CASIA V1,CASIA V2,and CoMoFoD datasets are 0.77,0.82,and 0.84,*** addition,the F1-scores of these three datasets are 0.80,0.84,and 0.86,***,the visual results of the proposed method are clean and contain rich information,which can be used for real-time forgery *** code used in the study can be accessed through URL:https://***/ajit2k5/Forgery-Localization(accessed on 21 January 2025).
In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study...
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In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable *** predictivemodels for thyroid cancer enhan...
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In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable *** predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce ***,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and *** paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present *** study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction *** the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the *** original dataset is used in trainingmachine learning models,and further used in generating SHAP values *** the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based *** new integrated dataset is used in re-training the machine learning *** new SHAP values generated from these models help in validating the contributions of feature sets in predicting *** conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making *** this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the *** study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of *** proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area un
Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, a...
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Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computerscience. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress. The medical field processes vast amounts of medical data;the machine learning algorithms sift through patterns that might escape the human eye. The machine learning algorithms act as detectives, able to spot correlations and bring out a sense of complex information. The machine learning algorithms reveal fine correlations and patterns, aiding in more precise and prompt diagnoses particularly to focus fundamental mental health issues in individuals of all ages. This research work deploys an enhanced Multilayer Perceptron (MLP), exhibiting an extensive feature analysis for processing medical datasets, resulting in improved effectiveness in predicting stress levels. This helps us to diagnose issues more accurately and swiftly which improves the patient outcomes. The proposed and enhanced MLP model undergoes stri
IOUT (Internet of Underwater Things) relies on underwater acoustic sensors, which have limited resources such as battery power and bandwidth. The exchange of data among these sensors faces challenges like propagation ...
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IOUT (Internet of Underwater Things) relies on underwater acoustic sensors, which have limited resources such as battery power and bandwidth. The exchange of data among these sensors faces challenges like propagation delay, node displacement, and environmental errors, making network maintenance difficult. The objective of this study is to address the energy efficiency and performance issues in IOUT networks by proposing and evaluating an energy-efficient routing protocol called Efficient Cost Wakeup Routing Protocol (ECWRP). To achieve the objective, the study focuses on two key parameters: Cost and Duty Cycle. The Duty Cycle parameter helps in reducing undesirable impacts during underwater communications, improving the performance of the routing protocol. The Cost parameter is utilized to select the most efficient path for data transmission, considering factors such as transmitting power levels. The protocol is applied to a multi-hop mesh-based network. The proposed ECWRP routing protocol is assessed through simulations, demonstrating its superior efficiency compared to the Ride algorithm. By eliminating unnecessary handshaking and optimizing route selection, ECWRP significantly enhances energy efficiency and overall performance within the IoUT network. The study's findings on the enhanced energy efficiency and performance improvements achieved by the ECWRP protocol hold promising implications for the design and optimization of IoUT networks, paving the way for more sustainable and effective communication systems in underwater environments. In conclusion, the study demonstrates the effectiveness of the Efficient Cost Wakeup Routing Protocol (ECWRP) in enhancing energy efficiency and performance in multi-hop mesh-based IoUT networks. The protocol's utilization of the Duty Cycle parameter reduces undesirable impacts, while the Cost parameter enables the selection of the most efficient path for data transmission. The results confirm the superiority of the ECWRP protoc
Mobile Ad hoc Network (MANET) is broadly applicable in various sectors within a short amount of time, which is connected to mobile developments. However, the communication in the MANET faces several issues like synchr...
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In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generati...
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In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the *** that,there are several methods to improve the retrieving process...
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In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the *** that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching ***,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation *** improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data *** this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire *** overall work was implemented for the application of the data recommendation *** are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching ***,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query *** was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature *** training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the *** are formed as clusters and paged with proper indexing based on the MPS parameter of similarity *** overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision
Algorithms are central objects of every nontrivial computer application but their analysis and design are a great challenge. While traditional methods involve mathematical and empirical approaches, there exists a thir...
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