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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Manjunatha, A.S. Venkatramana Bhat, P. Department of Computer and Communication Engineering India Department of Computer Science and Engineering India

ISBN: (纸本)9798331300579

data is collected and forwarded to the cluster head by sensor nodes in the Wireless Sensor Network (WSN). Ensuring the confidentiality and integrity of the data that must be provided to the base station is tough. We recommend using a simple Caesar cipher and Advanced Encryption Standard (AES) technique at the cluster head node level for effective communication from the cluster head to the base station with little power consumption. © Grenze Scientific Society, 2024.

关键词： Sensor nodes

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Enhanced Clickbait Detection through Ensemble Machine Learning Techniques

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Procedia computer science 2025年 258卷 599-608页

作者： Neetu Sardana Deepika Varshney Seema Luthra Department of Computer Science & Engineering-Data Science ABES Engineering College Ghaziabad India

Nowadays online news websites are one of the quickest ways to get information. However, the credibility of news from these sources is sometimes questioned. One common problem with online news is the prevalence of clickbait. Clickbait uses exaggerated headlines to lure people to click the suspected link, but the content often disappoints the reader and degrades user experience it may also hamper public emotions. The proposed work aims to examine diverse set of models for clickbait detection. The models are formed by integration of Machine learning (ML) and Ensemble learning methods (EL) with Term Frequency and Inverse Document Frequency (TF-IDF) & Embedding technique. Five ML and three EL are analysed &compared. Random Forest along with TF-IDF gave the best results of 85%. The resultant model shows significant improvements with a minimal false-positives.

关键词： Clickbait Detection Machine Learning text classification NLP Ensemble learning Stacking classifir

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Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

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computers, Materials & Continua 2023年第11期77卷 1515-1534页

作者： Shabana R.Ziyad Liyakathunisa Eman Aljohani I.A.Saeed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah41411Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia

Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in *** can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five *** research study aims to develop an automated tool for diagnosing autism in *** computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification *** most deterministic features are selected from the self-acquired dataset by novel feature selection methods before *** Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this *** performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research *** experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired *** ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different *** Exploratory data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.

关键词： Autism spectrum disorder feature selection imperialist competitive algorithm LASSO logistic regression random forest

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Interesting Concept Mining with Concept Lattice Convolutional Networks

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IEEE Access 2025年 13卷 99789-99801页

作者： Ibrahim, Mohamed Hamza Missaoui, Rokia Ruas, Pedro Henrique B. American University of Sharjah Computer Science and Engineering Department Sharjah United Arab Emirates University of Quebec in Outaouais Department of Computer Science and Engineering Quebec Gatineau Canada

The extraction of meaningful conceptual structures is often a critical task in many scientific and engineering disciplines, as it enables a comprehensive analysis of complex data in terms of both context and content. In this paper, we introduce the Concept Lattice Convolutional Network (LCN), an efficient semi-supervised learning approach to identify actionable concepts (i.e., interesting conceptual structures) based on a scalable convolutional neural network architecture that operates on concept lattices. The LCN captures diverse levels of global context by employing a message-passing mechanism that incorporates local structural and conceptual information within a lattice. It also employs parameter-sharing convolutional operations as conceptual filters to efficiently discern relevant concepts amidst the irrelevant ones. Moreover, it applies consistent aggregations that maintain local consistency of labeling across concepts in the lattice. Experiments on several datasets show that LCN can accurately identify actionable concepts and is at least three times faster than state-of-the-art exact interestingness indices. © 2013 IEEE.

关键词： Formal concept analysis graph convolutional network interestingness measure message-passing pattern mining semi-supervised learning

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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An Intelligent Analysis of Mobile Evidence Using Sentimental Analysis

An Intelligent Analysis of Mobile Evidence Using Sentimental...

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International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Jones, G. Maria Santhiya, P. Winster, S. Godfrey Sundar, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology and Panimalar Engineering College Chennai India

ISBN: (纸本)9789819950904

Smartphones are compatible and easily accessible compared to computers irrespective of place and time. Smartphones merge with our routine which acts as a medium of communication in several ways such as messaging, voice and video calling, sharing of audio and video contact, and many multimedia contents. We express our emotions through words and these communications can be analyzed by a technique called Natural Language Processing (NLP). Many researchers in the field of text mining and NLP have analyzed the polarity of online product reviews, blogs, social media comments, tweets, and many more. Mobile forensics investigation will be more effective by providing the evidences based on sentimental analysis of the text where the suspected messages contain a negative content of having threats, harassments, text against the community guidelines, and some keywords related to the cases which may be the turning point during the forensics investigation. In this paper, a novel framework called Integrated Mobile Device Forensic Investigation Process (IMDFIP) with inclusion of sentimental analysis techniques in the text which is used to emotionally analyze the texts during the communication process. This framework provides additional information to forensic analysts which will trap the suspect during investigation. The proposed framework of IMDFIP identifies the important terms, emotions, and polarity of the text during the crime. IMDFIP capitulates results compared with the previous models. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA

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Multimedia Tools and Applications 2025年第13期84卷 11475-11501页

作者： Alharbi, Meshal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Before a heart attack happens, treating cardiac patients effectively depends on precise heart disease prediction. A heart disease prediction system for the determination of whether the patient has a heart disease condition or not is to be developed. The paper employs a diverse array of models within the heart disease prediction system, each serving a specific purpose to enhance accuracy and efficiency. data pre-processing techniques, including normalization, standardization, and missing value removal, ensure the quality and consistency of the dataset. Feature extraction methods such as mean, median, standard deviations, and higher-order statistical features contribute to extracting informative features crucial for prediction. The Multi-Objective Forest Particle Swarm Optimization (MOFPSO) algorithm (hybrid version of Forest Optimization (FO) and Particle Swarm Optimization (PSO)) is introduced for efficient feature selection, balancing predictive accuracy and model complexity. Finally, the prediction model, Naïve Bayes trained with Meliorated Ant Colony Optimization algorithm (NB-MACO), is implemented for its simplicity and effectiveness in handling medical datasets. This integration fine-tunes the Naïve Bayes classifier’s hyperparameters, optimizing its performance and resulting in an accuracy of 91.974%. The collective utilization of these models and techniques ensures the development of a robust and accurate heart disease prediction system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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An Efficient Approach for EEG Seizure Detection using CNN with Feature Extraction 7

An Efficient Approach for EEG Seizure Detection using CNN wi...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Joseph, Liya Izudheen, Sminu Narayanana, Uma Nair, Abhinav R Ashok Gopal, K.A. Rajagiri School of Engineering & Technology Department of Computer Science and Engineering Ernakulam India CVV Institute of Science and Technology Department of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350372816

Epileptic seizures are a significant agony for those who suffer from them. Epileptic studies primarily focus on understanding the abnormal behavior of brain signals. Detecting seizures in EEG signals manually is a very chaotic and challenging task. Therefore, we propose an automatic seizure detection system that drastically reduces the manual effort required for seizure detection while providing accurate results. The proposed method involves extracting twelve statistical features from segmented data and adjusting labels to facilitate binary classification using a Convolutional Neural Network (CNN). The dataset is normalized and balanced using SMOTE to address class imbalance. Evaluation metrics such as accuracy, precision and recall are computed on the test set with results visualized through accuracy and loss curves. User input allows for predictions on specific test data, determining the presence of seizures based on model predictions. This structured approach ensures reliable seizure detection through the use of advanced deep learning techniques and comprehensive model evaluation. Furthermore, a comparative study was conducted to assess the effectiveness of the proposed model against conventional machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Gradient Boosting and Decision Tree. Our analysis reveals that CNN consistently demonstrates superior performance compared to these methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Salp Swarm and Bare Bones Mayfly Optimization Algorithm-based CH Selection and Sink Node Mobility for improving Network Longevity in WSNs

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Sadhana - Academy Proceedings in engineering sciences 2025年第2期50卷 1-22页

作者： Janakiraman, Sengathir Deva Priya, M. Department of Information Technology CVR College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India

Energy-potent routing protocols are vital for extending lifetime and energy stability in Wireless Sensor Networks (WSNs) as they comprise of numerous tiny sensor nodes with limited battery-powered energy. Clustering is a significant strategy that is commonly used for balancing energy consumption among energy-restricted sensor nodes with minimized overhead and traffic during data transmission. In particular, using a hybrid Swarm Intelligence (SI) metaheuristic algorithm for clustering and Cluster Head (CH) selection are considered to be significant for improving network longevity. In this paper, an Improved Salp Swarm and Bare Bones mayfly Optimization Algorithm (ISSBBMFOA)-based CH selection, along with sink node mobility scheme is proposed for improving network longevity. This algorithm specifically uses Improved Salp Swarm Optimization Algorithm (ISSOA) for identifying potential CH nodes in the network. This selection of CHs completely depends on the evaluation of fitness factors such as load balancing, mean inter and intra-cluster distances, distance from the sink and nodes’ Residual Energy (RE). It adopts Bare Bones Mayfly Optimization Algorithm (BBMFOA) for determining movement trajectory and location of sink corresponding to each cluster, following clustering of network regions involving optimal clusters. It facilitates moving sink to stop at optimal locations and aggregate data from sensor member nodes of associated clusters. The simulation results of the proposed ISSBBMFOA scheme confirm 23.21% improved throughput, 24.84% better sustained alive nodes and 22.62% enhanced network lifetime in contrast to other CH selection schemes considered for investigation. © Indian Academy of sciences 2025.

关键词： Sensor nodes

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Hybrid Underwater Target Tracking with Particle Filters and Deep Learning;Focus on Design Considerations 8

Hybrid Underwater Target Tracking with Particle Filters and ...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Baba, Abdullatif Alothman, Basil Computer Science and Engineering Department Kuwait Computer Engineering Department Ankara Turkey

ISBN: (纸本)9798331540104

This paper explores essential aspects of autonomous underwater vehicle (AUV) design, focusing on hull structure, hydrodynamics, propulsion systems, and sensor integration. It also examines the role of underwater Simultaneous Localization and Mapping (SLAM) in improving navigation and mapping precision for AUVs. In addition, the paper addresses challenges and solutions related to underwater GPS systems. The primary contribution of this study is a novel tracking method that combines the Particle Filter (PF) with deep learning techniques to enhance the tracking capabilities of underwater maneuvering targets. Multiple simulations validate the effectiveness of the proposed approach in diverse trajectory scenarios. © 2024 IEEE.

关键词： Mapping

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