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Designing a novel network anomaly detection framework using multi-serial stacked network with optimal feature selection procedures over DDOS attacks

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International Journal of Intelligent Networks 2025年第1期6卷 1-13页

作者： Pradeep, K. Jeevan Shukla, Prashanth Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

- Distributed denial-of-service (DDoS) attacks are the major threat that disrupts the services in the computer system and networks using traffic and targeted sources. So, real-world attack detection techniques are considered an important element in executing cybersecurity tasks. The present DDoS techniques are prone to False Positive Rates (FPR) and also it didn't acquire the complicated patterns presented in the attack traffic. Internet of Things (IoT) is a complicated network with resource-constrained devices and networks that are prone to different security threats like DDoS attacks. Later, the Software Defined Networking (SDN) with IoT models is used to enhance the access control techniques and security models. DDoS attacks are considered as an important threat in the IoT networks. Hence, it is important to construct a novel network anomaly detection model with a deep learning mechanism to resolve the limitations of the existing techniques. Initially, essential data required for the validation are gathered from the IDS ISCX 2012 dataset. The optimal features are selected from input data using the Predefined-Mud Ring Algorithm (P-MRA). The optimally selected features are provided to the Multi-Serial Stacked Networks (Multi-SSN), which is the fusion of Convolutional Autoencoder (CAE), Gated Recurrent Unit (GRU), and Bayesian Learning (BL) networks. Here, the essential features for the validation are acquired from the CAE and GRU. Then, these features are stacked and given to the BL mechanism for detecting the anomalies in the network. Further, several experimental validations are performed in the developed framework over traditional network anomaly detection mechanism. © 2024 The Authors

关键词： Internet of things

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Optimised hybrid classification approach for rice leaf disease prediction with proposed texture features

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Journal of Control and Decision 2024年第1期11卷 84-97页

作者： Sakhamuri Sridevi K.Kiran Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramIndia

This paper aims to frame a new rice disease prediction model that included three major ***,median filtering(MF)is deployed during pre-processing and then‘proposed Fuzzy Means Clustering(FCM)based segmentation’is *** that,‘Discrete Wavelet Transform(DWT),Scale-Invariant Feature Transform(SIFT)and low-level features(colour and shape),Proposed local Binary Pattern(LBP)based features’are extracted that are classified via‘MultiLayer Perceptron(MLP)and Long Short Term Memory(LSTM)’and predicted outcomes are *** exact prediction,this work intends to optimise the weights of LSTM using Inertia Weighted Salp Swarm Optimisation(IW-SSO)***,the development of IW-SSO method is established on varied metrics.

关键词： Rice disease improved fuzzy hybrid classifiers optimised LSTM IW-SSO algorithm

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Speech based emotion recognition by using a faster region-based convolutional neural network

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Multimedia Tools and Applications 2025年第8期84卷 5205-5237页

作者： Suneetha, Chappidi Anitha, Raju Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Automatic emotion identification from speech is a difficult problem that significantly depends on the accuracy of the speech characteristics employed for categorization. The display of emotions seen in human speech is inherently integrated with hidden representations of several dimensions and the fundamentals of human behaviour. This illustrates the significance of using auditory data gathered from discussions between people to determine people's emotions. In order to engage with people more closely, next-generation artificial intelligence will need to be able to recognize and express emotional states. Even though recovery of emotions from verbal descriptions of human interactions has shown promising outcomes, the accuracy of auditory feature-based emotion recognition from speech is still lacking. This paper suggests a unique method for Speech-based Emotion Recognition (SER) that makes use of Improved and a Faster Region-based Convolutional Neural Network (IFR-CNN). IFR-CNN employs Improved Intersection over Unification (IIOU) in the positioning stage with better loss function for improving Regions of Interest (RoI). With the help of a Recurrent Neural Network (RNN)-based model that considers both the dialogue structure and the unique emotional states;modern categorical emotion forecasts may be created quickly. In particular, IFR-CNN was developed to learn and store affective states, as well as track and recover speech properties. The effectiveness of the proposed method is evaluated with the help of real-time prediction capabilities, empirical evaluation, and benchmark datasets. From the speech dataset, we have extracted the Mel frequency cepstral coefficients (MFCC), as well as spectral characteristics and temporal features. Emotion recognition using retrieved information is the goal of the IFR-development. Quantitative analysis on two datasets, the Berlin Database of Emotional Speech (EMODB) and the Serbian Emotional Speech Database (GEES), revealed encouraging r

关键词： Emotion Recognition

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TWISH: an enhanced activation function for optimizing mental health and comorbidity risk assessment during COVID-19 using deep learning

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Multimedia Tools and Applications 2025年 1-21页

作者： Bhimavarapu, Usharani Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Significant health hazards have been associated with the COVID-19 pandemic, especially for women, whose mental and physical health have suffered greatly. In order to predict the risk of COVID-19 in women, this study investigates the use of stacked artificial neural networks (ANNs) in hidden layers with a proposed Twish activation function. The study incorporates many psychological and health-related measures into the artificial neural network framework, facilitating an all-encompassing evaluation of risk. Important measures include the Beck Anxiety Inventory (BAI), which assesses anxiety specifically related to pandemics (ANN1), the Social Readjustment Rating Scale (SRRS), which assesses stress from significant life events (ANN2), the Centre for Epidemiologic Studies Depression Scale (CES-D), which assesses symptoms of depression (ANN3), the Charlson Comorbidity Index (CCI), which assesses the impact of pre-existing health conditions (ANN4), and the Prenatal Distress Questionnaire (PDQ), which measures stress in pregnant women during the pandemic (ANN5).The ANN model enhances performance in the hidden layers by using the Twish activation function, enabling more precise and effective COVID-19 risk prediction. Predicting risk variables for women is especially helpful because of the intricacy of mental health, co-occurring conditions, and stress associated with pregnancy during the pandemic. All classes reveal that the proposed model is remarkably accurate, and several of its modifications produce outstanding outcomes. With a 97.75% accuracy rate, ANN1 has the strongest predicting capability. With accuracy scores of 97.50%, ANN2 and ANN4 continuously demonstrate good performance, highlighting dependable performance. ANN3, which has an accuracy of 98.00%, draws attention to outstanding performance. Furthermore, with an accuracy rate of 97.50%, ANN5 retains great predictive capabilities, demonstrating good and consistent performance in a variety of circumstances. © The A

关键词： COVID-19

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Cost minimization approach with hybrid optimization based task scheduling in Geo-distributed cloud

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Australian Journal of Electrical and Electronics engineering 2025年第2期22卷 169-181页

作者： Vasantham, Vijay Kumar Donavalli, Haritha Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India

To manage the huge data, cloud computing necessitates a significant number of disc I/O, network bandwidth and CPU cycles. To handle the massive volumes of data, the programming paradigm known as data flow integrates the tasks to a graph structure. Task scheduling is one of the main areas of current study that primarily aims to allocate the tasks to resources. It is essential to organise the tasks in a way which saves time and makes the most use of available resources. In recent years, scholars have offered different job scheduling techniques. This work intends to introduce a novel cost minimisation for big data processing with respect to task allocation or scheduling. Initially, the big data is processed under Map Reduce framework. This allocation processes is taken as the optimisation issue, which is solved under the consideration of minimisation factors like execution cost, makespan, communication cost, and energy consumption and risk. For solving this issue, a new hybrid optimisation algorithm known as Hybrid bald eagle and Archimedes Optimisation algorithm (HBE-ArCA) is developed. The betterment of HBE-ArCA is proven regarding cost, makespan and so on. ©, Engineers Australia.

关键词： Big data

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Machine learning-based early detection of diabetes risk factors for improved health management

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Multimedia Tools and Applications 2024年第42期83卷 89665-89680页

作者： Nuthakki, Praveena Kumar, T. Pavan Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur522302 India

This research requires to improve the accuracy of early diabetic forecasting in a human body or patient by applying diverse machine learning approaches. Approaching to creation of machine learning models by using patient datasets to produce predictions with improved accuracy. This work will use machine learning classification and ensemble approaches, such as Random Forest (RF), Gradient Boosting (GB), Decision Tree (DT), K-nearest neighbour (KNN), Logistic Regression (LR), and Support Vector Machine (SVM), on a dataset to predict diabetes. The accuracy of each model differs in comparison to other models. This work demonstrates the model's capability by providing an accurate or greater accuracy. This research paper reported different performance metrics like precision, recall, accuracy, F1 score, and sensitivity for various machine learning algorithms. Final experimental results indicate that the Random Forest classifier outperforms other methods. © 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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LEViT- Leaf Disease identification and classification using an enhanced Vision transformers(ViT) model

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Multimedia Tools and Applications 2024年 1-32页

作者： Prashanthi, Bh. Krishna, A. V. Praveen Rao, Ch. Mallikarjuna Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram522502 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India

The variety of crops, differences in climate, and the multiplicity of disease symptoms make early identification and evaluation of leaf diseases a challenging task. Although deep-learning methods have been created for this very reason, they often need adjustments to suit various regions and types of crops. This study presents a vision transformer-based deep learning model incorporating Explainable Artificial Intelligence (XAI) features to effectively detect and classify leaf diseases. The model is evaluated using the New Plant Disease Dataset, encompassing data from 14 plants with 38 leaf disease classifications. The primary objective is to enhance accuracy in leaf disease classification through the utilization of vision transformer learning. The proposed model, LEViT (Leaf Disease Identification and Classification using an Enhanced Vision Transformers Model), builds upon the ViT_B_32 model. It incorporates additional layers such as dense layers, dropout, and L2 regularization to improve performance and mitigate overfitting. Specialized preprocessing techniques tailored for transformers are employed to refine data quality. Incorporating Grad-CAM (Gradient-weighted Class Activation Mapping) enhances interpretability, enabling the identification of crucial regions influencing the model’s decisions within images. The model exhibits remarkable efficacy, boasting a training accuracy of 95.22%, validation accuracy of 96.19%, and test accuracy of 92.33%. These results underscore its robust ability to accurately identify leaf diseases across diverse crop types. Comparative analysis with state-of-the-art models in the field demonstrates LEViT’s superior performance, establishing it as a leading solution for leaf disease detection and classification. LEViT’s advanced architecture, coupled with fine-tuning, positions it as a highly promising tool for advancing crop management and yield improvement, thereby significantly contributing to the field of precision agriculture. © The

关键词： Crops

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Improved dynamic threshold based fuzzy logic to detect and classify diabetic retinopathy

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Multimedia Tools and Applications 2025年第6期84卷 3117-3135页

作者： Bhimavarapu, Usharani Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India

Diabetic retinopathy (DR) is a leading cause of vision impairment and blindness globally, with its severity classified into non-proliferative and proliferative stages. Effective detection and segmentation of multiple lesions are crucial for accurate diagnosis and management. This study presents a novel fuzzy logic-based model for the segmentation of multi-lesion diabetic retinopathy, which addresses the complexities associated with varying severity levels. The proposed multi-phase approach employs a comprehensive fuzzy inference system to enhance the detection and classification of diverse lesions related to diabetic retinopathy. Evaluated using three benchmark datasets—Messidor-2, Eyepacs, and IDRiD—proposed model achieved an accuracy of 99.57% on the Messidor-2 dataset and consistently high precision, recall, and F1-scores exceeding 99% across all datasets. Comparative analyses reveal that the proposed model significantly outperforms existing approaches, with an accuracy increase of 2.45% in multi-lesion segmentation metrics. Additionally, statistical validation demonstrates the robustness of our findings, with p-values less than 0.001. This study emphasizes the potential of fuzzy logic in enhancing diagnostic capabilities for diabetic retinopathy through precise lesion segmentation, ultimately aiding in improved patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fuzzy inference

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DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 1-13页

作者： S, Balasubramani D, John Aravindhar Renjith, P.N. Ramesh, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment. © 2023

关键词： K-means clustering

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IoMT enabled diabetic retinopathy segmentation and classification using ensemble efficient net model

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Multimedia Tools and Applications 2024年第18期84卷 1-32页

作者： Bhutnal, Vinodkumar Moparthi, Nageswara Rao Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

The most common preventable cause of blindness in working-age adults worldwide is diabetic retinopathy (DR). Accurate detection of DR by machine learning (ML) approaches is generally limited to pre-selected features. However, the feature learning ability and parameter consideration must be efficient to achieve better results. Therefore, this research proposes a novel automation of DR using images in the Internet of Medical Things (IoMT) established on a deep learning (DL) model to improve segmentation and classification performance. In this work, IoMT datasets such as the Diabetic Retinopathy Dataset (DRR) and the Indian Diabetic Retinopathy Image Dataset (IDRiD) are collected from public sources. First, the collected fundus images are pre-processed using the Sequential Bilateral-Rolling Guidance Filter (SeQ-BR) for noise exclusion. The filtered images are subjected to the Improved Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to improve contrast. The pre-processed images are segmented into thin vessels, exudates, orbit, microaneurysms, and optic nerve heads using the Dilatation U-Net model (Di-UNet). From the segmented images, the appropriate features are extracted using the Convolutional Swin Transformer (CST) model. To minimize the dimensionality problems, an Opposition Based Mud Ring Optimization (OMR) algorithm is used to select the features. Finally, disease severity can be classified using the ensemble efficient net-assisted gazelle optimization algorithm model (E-EffGaz). The proposed method results in 97.69% accuracy for the DRR dataset and 97.52% accuracy using the IDRiD dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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