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Seizure Prediction on EEG Signals using Feature Augmentation based Multi Model Ensemble

Recent Patents on engineering 2025年第1期19卷 41-49页

作者： Anandaraj, A. Alphonse, P.J.A. Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

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 epilepsy patients. After analyzing various patents related to seizure prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients is an important task for the early diagnosis of seizures. Objective: The main objective of this paper is to assist epileptic patients to enhance their way of living by predicting the seizure in advance. Methods: This paper builds a feature augmentation-based multi-model ensemble-based architecture for seizure prediction. The proposed technique is divided into 2 broad categories;feature augmentation and ensemble modeling. The feature augmentation process builds temporal features while the multi-model ensemble has been designed to handle the high complexity levels of the EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous classifier models. The second phase is based on the prediction results obtained from the first phase. Experiments were performed using the seizure prediction dataset from the University Hospital of Bonn. Results: Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model. High prediction levels indicate that the model is highly capable of providing accurate seizure predictions, hence ensuring its applicability in real time. Conclusion: The result of this paper has been compared with existing methods of predicting seizures and it indicated that the proposed model has better enhancement in the accuracy levels. © 2025 Bentham science Publishers.

关键词： Electroencephalography

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Robust Image Forgery Localization Using Hybrid CNN-Transformer Synergy Based Framework

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computers, Materials & Continua 2025年第3期82卷 4691-4708页

作者： Sachin Sharma Brajesh Kumar Singh Hitendra Garg Department of Computer Science Abdul Kalam Technical UniversityLucknow226031India Department of Computer Engineering & Applications GLA UniversityMathura281406India Department of Computer Science Raja Balwant Singh Engineering Technical CampusAgra283105India

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).

关键词： Image tampering convolution neural network(CNN) hybrid transformer localization

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Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks

Data Science and Management

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Data science and Management 2025年第2期8卷 137-146页

作者： Khan, Wasim Mohd, Afsaruddin Suaib, Mohammad Ishrat, Mohammad Shaikh, Anwar Ahamed Faisal, Syed Mohd Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram 522502 India Department of Computer Engineering & Applications GLA University Mathura281406 India Department of Computer Science and Engineering Integral University Lucknow226026 India

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 introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data. This is further bolstered by deep residual learning, which is employed to model intricate nonlinear connections directly from input data. A pivotal innovation in our approach is the incorporation of a residual-based attention mechanism. This mechanism dynamically adjusts the importance of nodes based on their residual information, thereby significantly enhancing the sensitivity of the model to subtle anomalies. Furthermore, we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data. This mapping is the key to our model's ability to pinpoint anomalies with greater precision. An extensive experimental setup was used to validate the efficacy of the proposed model. Using attributed social network datasets, we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection. The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks. © 2024 Xi'an Jiaotong University

关键词： Deep learning

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A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

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computers, Materials & Continua 2024年第5期79卷 3111-3138页

作者： Sujithra Sankar S.Sathyalakshmi Department of Computer Applications Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia Department of Computer Engineering Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia

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

关键词： Explainable AI machine learning clinical decision support systems thyroid cancer association-rule based framework SHAP values classification and prediction

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Human Stress Detection in and Through Sleep Patterns Using Machine Learning Algorithms

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Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1691-1713页

作者： Geetha, R. Gunanandhini, S. Srikanth, G. Umarani Sujatha, V. Department of Computer Science and Engineering S.A. Engineering College Chennai600077 India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai600077 India

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 computer science. 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

关键词： Decision trees

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Enhancing energy efficiency: a protocol assessment in multi-hop mesh-based IOUT networks

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Multimedia Tools and applications 2024年第37期83卷 84999-85026页

作者： Sisodia, Ankur Vishnoi, Swati Upadhyay, Sachin Goswami, Jayati Krishna Khenwar, Medha Yadav, Ajay Kumar Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering GL Bajaj Group of Institutions Mathura India Department of Computer Science Banasthali Vidyapith Rajasthan Jaipur India

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

关键词： Energy efficiency

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Hybrid heuristic driving training-rider optimization algorithm for QoS-aware multicast communication system in MANET

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Australian Journal of Electrical and Electronics engineering 2024年第1期21卷 59-78页

作者： Sarangi, Sanjaya Kumar Nanda, Arabinda Lenka, Rasmita Behera, Prafulla Kumar Department of Computer Science and Applications Utkal University Bhubaneswar India Department of Computer Science & Engineering Krupajal Engineering College Bhubaneswar India School of electronics engineering KIIT University Bhubaneswar India

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 synchronization, managing power, resources, and routing management. Often, finding the optimal path in the MANET becomes complicated in the existing approaches. In this work, the latest QoS-assisted multiple-cast communication system in MANET is presented using hybridized approaches for better communication over the MANET domain. Especially, an Exploitation-assisted Driving Training-Rider Optimization (EDT-RO) model is suggested for the multicast routing in MANET. The developed EDT-RO algorithm optimizes the paths for communication in MANET, where the network considers more than two receivers. The proposed EDT-RO-based multicast routing algorithm aims to attain the objective function considering the data packet delivery rate, the throughput, the end-to-end latency, and the Quality of Service (QoS). Throughout the result analysis, the offered model shows better performance in terms of 0.2%, 0.3%, 0.5%, and 1.5% than DHOA, Jaya, DTBO, and ROA regarding mean. Simulations are conducted by comparing with other optimization algorithms used for QoS-assisted multiple casts routing to showcase and attain better routing performance of the developed models. ©, Engineers Australia.

关键词： Optimization

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Classifying the state of knowledge-based question answering: patterns, progress, and prospects

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International Journal of computers and applications 2025年第1期47卷 93-105页

作者： Sharma, Sumit Jain, Sarika Tiwari, Mukesh Kumar lal, Sohan Department of Computer Science and Engineering Quantum University Roorkee Uttarakhand Roorkee India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

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 generation of QASs focuses on the types of inquiries that users typically enquire, the features of the data sources that are used, and the formats in which the right responses are produced. Many QASs have been developed since the 1960s when research on the subject first started. There are several cutting-edge frameworks for answering questions that can be used to categorize the QA system according to a number of criteria. The necessity for an extensive survey on QASs comes naturally in order to determine the future focus of research in this field. In this study, QASs are analyzed by the number of criteria used to categorize question-answering (QA) and provide the dimension using a variety of standards to cover all the QAS. We describe the current state of research in each QAS area and make recommendations for its future direction. Based on the dimension criteria outlined in the research methodology, research studies are chosen for additional review and comparison. Constructed some criteria to classify the QASs, we provide the classification and go over the benefits and drawbacks of each class's QAS, as well as the problems with the corresponding study. Results are detailed summarized and compared. As a result of this study, a general architecture of frameworks for QA over knowledge base (KB) is drawn in addition to the comparative analysis of systems, serving as a reference for academics working on related topics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Question answering

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Web Page Recommendation Using Distributional Recurrent Neural Network

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computer Systems science & engineering 2023年第4期45卷 803-817页

作者： Chaithra G.M.Lingaraju S.Jagannatha Department of Computer Science&Engineering Sapthagiri College of EngineeringBangaloreIndia Department of Information Science and Engineering Sahyadri College of Engineering&ManagementMangaluruIndia Department of Computer Applications MSRITBangaloreIndia

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

关键词： Ontology data mining in big data logarithmic directionality texture pattern metaheuristic pattern searching system distributional recurrent neural network query recommendation

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Algoviz—Real-Time Insight of Algorithms 3rd

Algoviz—Real-Time Insight of Algorithms

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3rd International Conference on Emerging Trends in Expert applications and Security, ICE-TEAS 2024

作者： Prasad, S. Ashwini Hanumanthappa, M. Supriyaa, K. Rathore, Vijay Singh Department of Computer Science and Applications Bangalore India Department of Computer Science Engineering Jaipur India

ISBN: (纸本)9789819737444

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 third avenue for studying algorithms, i.e. the algorithm visualizer. Algorithm visualizer is an interactive software tool designed to enhance the understanding and learning of various algorithms through visual representation. This tool provides a dynamic and engaging way to explore complex computational processes, making them more accessible to both novice learners and experienced programmers. The visualization platform offers a diverse range of algorithms, spanning data structures, sorting methods, graph traversal techniques, path finding algorithms, and more. Through real-time interaction, users gain insights into algorithmic operations, fostering a deeper understanding of their logic and behaviour. Users can customize inputs, experiment with different scenarios, and observe, how algorithmic choice impacts outcomes in a visual and interactive manner. The primary goal of algorithm visualizer is to elucidate the inner workings of various known algorithms by presenting them graphically, thereby simplifying their complexities for comprehension. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer debugging

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