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Dominance-based rough set approach for the detection of cyber attack

International Journal of Information technology (Singapore) 2025年第3期17卷 1447-1460页

作者： Majumder, Sandip Kar, Samarjit Sengupta, Shatadru Department of Mathematics National Institute of Technology Durgapur Durgapur India Department of Computer Applications Haldia Institute of Technology Haldia India

Cyber-attack is unauthorized access to a computer-based system to manipulate its information content illegally and often with harmful intent. Nowadays Internet become a growing part of our social lives. Day by day with the rapid progress of technologies, the scope of data transferred on networks is increasing. As a result, the complexity and density of cyber-attacks are also increasing. Due to the ever-growing network density, it is always hard for cyber-security professionals to observe each moment on the network. The irregularities in identification and detection in the network system become more complex due to the frequent and variety of types of cyber-attacks. In this paper, we discuss various techniques for detecting and preventing different types of cyber jamming in wireless networks. We have used dominance-based rough set theory, which gives a novelty to our proposed work, and also developed algorithms for preventing cyber jamming. In these studies, we present different types of cyber jamming in detail. We compared our technique with the other existing methods and the results obtained in tables. Our methods generate new granulation for the dominance-based rough set. The efficiency of our proposed method has been justified by a practical example of the crawdad dataset. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Cyber jamming attack Dominance relation Dominance rough set Rough set Wireless networks

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A Hybrid ANN Random Forest Regression Method of Feature Extraction in Time Series Data

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SN computer Science 2025年第1期6卷 1-8页

作者： Rekha, M.P. Perumal, K. Department of Computer Applications School of Information Technology Madurai Kamaraj University Tamilnadu Madurai India

A time series is a logical grouping of values about time gleaned from numerous applications. The fundamental properties of the time series data (TSD) include their enormous quantity, high complexity, and traits like trend, cycle, seasonality, and irregularity. Data mining has seen a significant amount of research and development efforts due to the cumulative use of TSD. The effectiveness of various methods of data mining used for time series analysis is closely correlated to the dimension of the time series. In this proposed method, the weather forecasting dataset has been used. The dataset has more number of dimensions and it has been reduce dimensionality using Artificial Neural Network (ANN) and extract the feature with accuracy using Random Forest regression algorithm The study aims to utilize the deep learning (DL) method and feature extraction to minimize the dimensionality in time series data. The proposed approach improves the extraction's effectiveness in reaching our research's goal. The proposal seeks to minimize time series data's high dimensionality. The idea also emphasizes the importance of accurately lowering the dimensionality of time series data. The proposed Random Forest regression method with MAPE (13%), RMSE (9%), and MSE (15%) effectively decreases the dimensionality and extract the feature effectively when compared to existing techniques. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data mining Deep learning Dimensionality reduction Random Forest Time series data

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Ebola optimization based spiking neural network for automatic hate speech recognition

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International Journal of Information technology (Singapore) 2025年第3期17卷 1631-1639页

作者： Meenakshi, A. Ruth, J. Anitha Department of Computer Science and Applications SRM Institute of Science and Technology Vadapalani Campus Chennai India

In this paper, efficient machine learning technique is introduced to develop efficient machine learning model for hate speech recognition from the tweet data. Initially, the tweet data is gathered from the open-source system. Hence, to achieve hate speech recognition, different key tasks are considered which is named as pre-processing stage, feature extraction stage and classification stage. In the pre-processing stage, different pre-processing methods are considered for removing unwanted information from the tweet data such as Character Removal, unwanted symbols removal, URL and tag removal, irrelevant content removal, Stemming, tokenization, parts of speech (POS) and negation vector. The extracted features are utilized to identify the hate speech recognition from the tweet data. The proposed classifier is named as Spiking Neural Network- Ebola Optimization Algorithm (SNN-EOA). The proposed classifier is a combination of Spiking Neural Network (SNN) and Ebola Optimization Algorithm (EOA). The performance of proposed approach is analysed based on different metrics. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Ebola optimization algorithm Hate speech recognition Pattern features Semantic features Sentiment features Spiking neural network

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Parkinson’s disease Severity Detection Based on Fused features with tuned Residual Deep Convolutional Neural Network

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Multimedia Tools and applications 2025年第14期84卷 13111-13131页

作者： Vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh Computer Science & Engineering Indian Institute of Information Technology MP Bhopal India Computer Science & Engineering Maulana Azad National Institute of Technology MP Bhopal India Department of Computer Applications Maulana Azad National Institute of Technology MP Bhopal India

Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. During the diagnosis process, clinicians must consider gait abnormalities as one of the key symptoms. However, relying solely on clinical judgment for the diagnosis of PD may have limitations. In this work, we have proposed a Multikernel SVM (MK-SVM) based Parkinson's severity analysis method. Initially, an adaptive median filter is used to reduce the noise in the gait image. Then, to extract the features, we propose a dual-layer method that employs stacked autoencoders (SAEs) for local feature extraction and residual deep convolution neural networks (RDCNN) for global feature extraction. The hyper-parameters from the RDCNN are optimized using the Enhanced walrus optimization algorithm (EWO). In the final step, we use MK-SVM to classify PD according to severity. This model is evaluated using the GAIT-IT and GAIT-IST datasets. With the help of Python, we evaluated the proposed model in terms of precision, accuracy, recall, specificity, and F-scores, and the model achieved a maximum accuracy of 97.3%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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ADASYN: Oversampling Techniques for COVID-19 Prediction

ADASYN: Oversampling Techniques for COVID-19 Prediction

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Mary, L. William Raj, S. Albert Antony SRM Institute of Science and Technology Department of Computer Applications Kattankulathur Chennai India

ISBN: (纸本)9798331512965

In 2019, the highly contagious COVID-19 virus emerged and rapidly spread, causing a global health crisis characterized by inflammatory respiratory symptoms. The primary target of this disease is the respiratory system. Research has identified bats as the reservoir for the virus, but the intermediate host remains unknown. There is ongoing concern about various coronavirus variants, with the Omicron variant previously dominating globally and now attention shifting to a new variant, BA.2.86, informally known as Pirola, which is being closely monitored worldwide. However, the inherent class imbalance in COVID-19 datasets poses a significant obstacle to the effectiveness of these models. This paper proposes a novel approach combining outlier detection and Adaptive Synthetic Minority Oversampling Technique (ADASYNA) to address this issue. The Outlier-ADASYNA method identifies outliers within the minority class (COVID-19 cases) and synthesizes informative samples to rebalance the dataset. The proposed approach is evaluated using data from 5,400 patient records for both training and testing. The study employs ADASYN to effectively mitigate dataset imbalances and outstanding traditional oversampling techniques. This paper presents a comparative analysis of machine learning techniques, evaluating their performance using diverse metrics. The classification algorithm Random Forest attains the highest accuracy percentage of 90%, and RF Oversampling 92% surpassing numerous classification algorithms in predictive performance. © 2025 IEEE.

关键词： COVID-19

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Dual image based secured reversible data hiding scheme exploiting huffman compression tree combining bit-reversal permutation technique

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Multimedia Tools and applications 2025年第9期84卷 5653-5698页

作者： Datta, Kankana Jana, Biswapati Chakraborty, Mamata Dalui Department of Computer Applications Haldia Institute of Technology HIT Campus Hatiberia Haldia Purba Medinipur West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal Midnapore India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

In the area of data hiding and information security, the greater need is to ensure high embedding capacity of the stego media without hampering the visual quality while ensuring the tightest possible security. Now, it has become one of the biggest challenge to meet all these requirements. Embedding secret messages in dual images is one of the latest techniques that aims to cater such needs. However, there are very few data hiding schemes that are currently using dual images, but are suffering from several shortcomings, such as security loophole and poor visual quality with limited hiding capacity. In this paper, a dual image-based reversible data hiding scheme has been proposed where multiple bits can be embedded within a pair of pixels dynamically by Huffman compression where different pseudo codes are generated for different chunk of secret bits which are embedded by Bit-Reversal Permutation (BRP) and X-OR operations. Finally, pixels are interchanged between the dual images based on odd-even properties of the pixel values. The functional efficiency of the proposed scheme is evaluated by comparing the relevant parameters of different state-of-the-art schemes. It is established that the proposed scheme provides more than 64 dB PSNR value on average after embedding a maximum of 3, 27, 680 bits of secret data which ensures good visual quality and high embedding capacity as compared to other state-of-the-art schemes thereby benefitting innumerable applications across public and private sectors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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A Comprehensive Analysis of Knowledge Graph-based Social Recommendation System 1

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Navuluri, Madhavilatha Bhuyan, Hemanta Kumar RGUKT-IIIT Department of Computer Science and Engineering Ongole India Department of Information Technology and Computer Applications Guntur India

ISBN: (纸本)9798331529246

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from Knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. Data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and Knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems. © 2025 IEEE.

关键词： Knowledge graph

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Predicting Autism Spectrum Disorder in Adults Through Facial Image Analysis: A Multi-CNN with BiLSTM Model

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SN computer Science 2025年第3期6卷 1-15页

作者： Kumar, Anil Jaiswal, Umesh Chandra Department of Information Technology & Computer Applications MMMUT Deoria Road U.P. Gorakhpur 273010 India

The immersive experience of watching movies presents a unique opportunity to observe and analyze adults’ facial expressions and emotional responses in a naturalistic environment. The significant growth of the entertainment industry in recent years has expanded the availability and personalization of movies and other media, offering a platform for studying behavioral patterns in diverse populations. This study explores the feasibility of diagnosing Autism Spectrum Disorder (ASD) in adults through the analysis of facial images captured during movie watching sessions. A Kaggle dataset comprises 2653 facial images of individuals diagnosed with ASD and neurotypical adults, focusing on emotion-related expression changes over time to develop a diagnostic model. To address this challenge, we propose the integration of deep learning methodologies to classify ASD in adults aged 18 to 30 years. The data set was pre-processed to standardize and enhance the variability of the training images, including resizing to a fixed target size, horizontal flipping for augmentation and rescaling to normalize pixel values. These pre-processed images are input for a hybrid model that combines multi-convolutional neural networks (multi-CNN) and bidirectional long- and short-term memory (BiLSTM) networks. The multi-CNN component efficiently extracts spatial features from the images, while the BiLSTM leverages temporal dynamics, capturing the sequential nature of changes in facial expressions over time. The proposed model achieved an accuracy of 96.6% in predicting ASD diagnoses, demonstrating the efficacy of the work done. This high level of performance underscores the potential of integrating computer vision and deep learning techniques for non-invasive, scalable diagnostic tools. The findings contribute to the development of efficient methods for the detection of ASD in young adults, emphasizing the importance of emotion-related behavioral analysis in autism research. © The Author(s), under

关键词： Autism Convolution neural network Deep learning Entertainment Machine learning Prediction

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Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) Classification and Prediction of Heart Disease 4

Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chairmadurai, P. Kavitha, P. Kamalakkannan, S. Pallavaram Department of Computer Science Chennai India Pallavaram Department of Computer Applications Chennai India Pallavaram Department of Information Technology Chennai India

ISBN: (纸本)9798331523923

Medical data analysis is increasingly used to plan, improve research techniques, and explain diagnoses. The allocation of medical resources is based on the prevalence of specific pathologies. Artificial intelligence (AI) offers robust software solutions to analyze existing data and provide optimal predictions of medical outcomes. The dataset for heart disease prediction consists missing values in important features like cholesterol, ECG results and lifestyle factors. This research focuses to solve the issues in the traditional machine learning algorithm and designing a system model incorporating multiple data processing algorithms to classify heart disease. The classification process allows for the generation of a predictive model from training and testing datasets. These datasets are processed using an algorithm for classification to create a new model that can analyze detailed data, potentially with these same classes, by combining computational techniques with mathematical tools. This study uses optimization strategies to improve this model's performance and maximize prediction outcomes. This study aims to provide a framework to forecast cardiac problems based on significant risk using various classifier techniques, including the Bayesian Optimized SVM, to variables. The UCI repository is the source of the dataset utilized in this investigation. Linear Discriminant Analysis is used for feature extraction to decrease dimensionality and enhance the model's capacity to identify relevant patterns in the data. To generate the results as a precision, Recall, f1 score and accuracy. © 2025 IEEE.

关键词： Support vector machines

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Emotions: Pyramidal Structured Feature Representation from EEG Signals

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SN computer Science 2025年第4期6卷 1-18页

作者： Thejaswini, M.S. Kumar, G. Hemantha Aradhya, V. N. Manjunath Department of Studies in Computer Science University of Mysore Karnataka Mysore 570006 India Department of Computer Applications JSS Science and Technology University Karnataka Mysuru 570006 India

EEG-based emotion recognition offers significant advantages for human-robot interaction, computer gaming, and mental health monitoring. However, current methods face challenges like high-dimensional data, computational complexity, and unrealistic setups. To address these issues, this research proposes a pyramidal structured feature extraction approach utilizing hierarchical forward interpolation and multirate sampling frequencies. A culturally relevant EEG dataset, featuring Kannada musical clips and commercial advertisements, was created for ecological validity. Data were recorded from 46 participants using a BIOPAC 2-channel EEG device, targeting emotional states such as enjoyment, relaxation, sadness, fear, and being scared. The pyramidal approach reduces dimensionality by scaling EEG features from thousands to a compact set while preserving critical information. Iterative forward difference operations are applied across five hierarchical levels, and multirate sampling enhances temporal resolution for better emotion discrimination. The second stage uses a probabilistic neural network, achieving classification rates of up to 98%, outperforming principal and independent component analysis techniques. This framework is computationally efficient, supports real-time applications, and works flexibly with diverse datasets. By integrating culturally relevant stimuli, it sets a new benchmark for EEG-based emotion recognition. Practical applications include emotion-driven product recommendations, entertainment systems, and advancements in human–computer interaction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： EEG Signals Emotional Intelligence Entertainment Numerical Analysis PNN

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