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A Deep Ensemble Framework for DDoS Attack Recognition and Mitigation in Cloud SDN Environment
Much research has been done in the recent past on the absolute shift of Internet infrastructure in order to make it more significantly programmable, configurable and make it more conveniently feasible. Software Defined Networking (SDN) forms the basis for this absolute shift in Internet infrastructure. When you look at the benefits of an SDN-based cloud environment they are monumental. Namely, network traffic control and elastic resource management. The SDN-based cloud environment becomes susceptible to cyber threats, especially like that of Distributed Denial of Service (DDoS) attacks and other cyber-attacks that perturb the SDN-based cloud environment. Hence, automated Machine Learning (ML) models are an efficient way to protect against these cyber-attacks. This research will develop a deep learning-based ensemble model for DDoS attack detection and classification (DLEM-DDoS) in a cloud environment. Long Short-Term Memory (LSTM), 1-D Convolutional Neural Networks (1D-CNN) and Gated Recurrent Unit (GRU) are the three DL models integrated into an ensemble model that classifies the incoming packet by majority voting classifiers. Network traffic data including source and destination IP addresses, packet and byte counts, packet and byte rates, flow duration, protocol types and port numbers are fed into the DLEM-DDoS model. This model preprocesses this data by converting categorical values (like protocol types) into numerical values and removing any missing values. Once collected and preprocessed, the data is fed into deep learning models (LSTM, 1D-CNN, GRU) within the framework for analysis. Finally, in this research using the DLEM-DDoS technique an efficient DDoS attack mitigation scheme in an SDN-based cloud environment is demonstrated. The report shows comprehensive stimulations as well as a superiority into the current approaches in terms of several measures. 2024 S. Annie Christila and R. Sivakumar. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
A deep learning approach in early prediction of lungs cancer from the 2d image scan with gini index
Digital Imaging and Communication in Medicine (DiCoM) is one of the key protocols for medical imaging and related data. It is implemented in various healthcare facilities. Lung cancer is one of the leading causes of death because of air pollution. Early detection of lung cancer can save many lives. In the last 5years, the overall survival rate of lung cancer patients has increased, due to early detection. In this paper, we have proposed Zero-phase Component Analysis (ZCA) whitening and Local Binary Pattern (LBP) to enhance the quality of lung images which will be easy to detect cancer cells. Local Energy based Shape Histogram (LESH) technique is used to detect lung cancer. LESH feature extracts a suitable diagnosis of cancer from the CT scans. The Gini coefficient is used for characterizing lung nodules which will be helpful in Computed Tomography (CT) scan. We propose a Convolutional Neural Network (CNN) algorithm to integrate multilayer perceptron for image segmentation. In this process, we combined both traditional feature extraction and high-level feature extraction to classify lung images. The convolutional neural network for feature extraction will identify lung cancer cells with traditional feature extraction and high-level feature extraction to classify lung images. The experiment showed a final accuracy of about 93.27%. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A Deep Learning Approach to Clinical Decision Support in Heart Disease Diagnosis
Heart disease is the dominant cause of extinction worldwide, emphasizing the importance of early diagnosis and treatment planning. In this article, the authors developed a Clinical Decision Support System (CDSS) for heart disease prediction using deep learning techniques. This system will suggest a neural network architecture with Leaky ReLU as the activation function in the hidden layers and Sigmoid as the activation function in the output layer for binary classification. The configuration neural network is enhanced across three to nine hidden layers. The proposed approach is evaluated using accuracy as the measurable value on five multivariate datasets. By integrating advanced deep learning with clinical expertise, this study aims to enhance predictive accuracy, contributing to reduced heart disease mortality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Deep Learning Approach to Phishing Detection Using BiLSTM with an Attention Mechanism
Phishing sites are a serious cyber threat as they trick users into revealing sensitive personal data. Conventional detection techniques, including rule-based systems and blocklists, cannot cope with changing phishing tactics. In this paper, a new approach to phishing detection is introduced using a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism. The suggested model learns and examines URL-based features and identifies forward and backward relationships in data, enhancing classification accuracy. A 30,000 URL-tagged dataset is utilized to train the model, which is then optimized with the help of methods like sequence tokenization, embedding layers, dropout regularization, and class weight balancing to counter data imbalance issues. The BiLSTM layer processes sequential information about URLs in a bidirectional manner, whereas the attention mechanism applies weights to important features differently to ensure the model pays attention to the most critical elements of phishing URLs. The model was tested based on standard performance metrics and has attained an astounding accuracy of 99.22%, precision of 99.1%, recall of 99.3%, and an F1-score of 99.2%, surpassing the traditional approach like Logistic Regression. The model indicates good generalization ability and is possible to be applied in real-time in web security systems. In the future, the use of dynamic data analysis and large datasets will be applied to improve further the detection efficiency and responsiveness against the new emerging phishing attacks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Deep Learning Method for Autism Spectrum Disorder
The present study uses deep learning methods to detect autism spectrum disorder (ASD) in patients from global multi-site database Autism Brain Imaging Data Exchange (ABIDE) based on brain activity patterns. ASD is a neurological condition marked by repetitive behaviours and social difficulties. A deep learning-based approach using transfer learning for automatic detection of ASD is proposed in this study, which uses characteristics retrieved from the intracranial brain volume and corpus callosum from the ABIDE data set. T1-weighted MRI scans provide information on the intracranial brain volume and corpus callosum. ASD is detected using VGG-16 based on transfer learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Deep Learning Method for Classification in Brain-Computer Interface
Neural activity is the controlling signal used in enabling BCI to have direct communication with a computer. An array of EEG signals aid in the selection of the neural signal. The feature extractors and classifiers have a specific pattern of EEG control for a given BCI protocol, which is tailor-made and limited to that specific signal. Although a single protocol is applied in the deep neural networks used in EEG-based brain-computer interfaces, which are being used in the feature extraction and classification of speech recognition and computer vision, it is unclear how these architectures find themselves generalized in other area and prototypes. The deep learning approach used in transferring knowledge acquired from the source tasks to the target tasks is called transfer learning. Conventional machine learning algorithms have been surpassed by deep neural networks while solving problems concerning the real world. However, the best deep neural networks were identified by considering the knowledge of the problem domain. A significant amount of time and computational resources have to be spent to validate this approach. This work presents a deep learning neural network architecture based on Visual Geometry Group Network (VGGNet), Residual Network (ResNet), and inception network methods. Experimental results show that the proposed method achieves better performance than other methods. 2023 IEEE. -
A Deep Learning Methodology CNN-ADAM for the Prediction of PCOS from Text Report
Text categorization is a popular piece of work in natural language processing (NLP) and machine learning, and Convolutional Neural Networks (CNNs) can be used effectively for this purpose. Although CNNs are traditionally associated with computer vision tasks, they have been adapted and applied successfully to text classification problems. In the proposed study Convolutional Neural Networks (CNNs) with adam optimization algorithm plays a crucial role in detecting PCOS words from sonographic text reports. 2023 IEEE. -
A Deep Learning Model for Information Loss Prevention from Multi-Page Digital Documents
World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers' sensitive information of a reputed TI company securing it's one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization's information security monitoring team. 2013 IEEE. -
A Deep Learning-Based BCI System for Emotion Classification Using EEG Signals
Electroencephalography-based Brain-Computer Interfacing (EEG-BCI) technologies allow for effortless interaction between external hardware and the human brain through monitoring its electric signals. These systems rely on EEG recordings, which provide non-invasive and real-time neural information through electrodes placed on the scalp. To advance emotion-recognizing efficiency and accuracy, this study proposes a deep learning-based method that can extract valuable temporal and spatial information from EEG signals. The proposed model includes the use of a Graph Convolution Network (GCN) for learning spatial relationships between different EEG channels to model the data in graph form and gain features through that modelling. A Convolutional Autoencoder (CAE) is then used to compress data to low dimensions and to reconstruct it so that major features are not ignored. Furthermore, the model uses an Attention-based Bidirectional Gated Recurrent Unit (ABiGRU) for temporal classification, which can emphasize the most important time steps in both backwards and forward directions. Two standard datasets are employed to test the developed approach. The DEAP dataset is used for emotion recognition with a binary response, and SEED is used with multi-class classification. The model attains great results of 98.12% accuracy on DEAP and 97.58% on SEED datasets. The very high performances show the efficacy of the model for decoding emotional states from EEG signals and very strong potential for real-time emotion recognition in affective computing and BCI. 2026 Seventh Sense Research Group. -
A Deep Learning-Enhanced Adaptive Intrusion Detection Framework for Real-Time Cyber Security Threat Analysis
The rapidly evolving nature of cyber-attacks significantly reduces the effectiveness of conventional intrusion detection systems (IDS) that rely on static rules and signatures. This work presents an adaptive deep learning- based intrusion detection framework designed to maintain reliable performance in real-time environments affected by concept drift. The proposed approach integrates one-dimensional convolutional neural networks (1D-CNN) for local feature interaction learning with a bidirectional long short-term memory (BiLSTM) network to model sequential network traffic behavior. To address evolving attack patterns, a sliding-window-based incremental learning mechanism is employed, enabling continuous model adaptation to recent traffic characteristics. The model is trained using cross-entropy loss optimized with the Adam optimizer, while dropout regularization is applied to reduce overfitting and ensure fast convergence. To enhance transparency and analyst trust, explainable artificial intelligence techniques are incorporated, including SHAP-based feature attribution and an attention mechanism for interpreting temporal dependencies. Experimental evaluation on labeled network traffic data demonstrates stable convergence, consistent detection accuracy under changing traffic conditions, and improved robustness compared to non-adaptive baseline models. These results confirm the effectiveness and practical applicability of the proposed framework for real-time and interpretable cybersecurity intrusion detection. 2026 IEEE. -
A deep understanding of virtual reality's role in human resource management, with a specific focus on recruitment
In a rapidly evolving recruitment landscape, virtual reality (VR) emerges as a transformative tool for human resource management (HRM). This chapter delves into the profound impact VR can have on attracting, assessing and onboarding talent. Furthermore, VR services have been already included in other areas where it has been shown its essence to be useful for those industries. The current study explores how VR simulations can provide immersive experiences, allowing candidates to virtually step into the workplace and interact with potential colleagues and tasks. This fosters a deeper understanding of the role and company culture, leading to better-matched hires and improved candidate experience. This chapter also examines the potential of VR for skills assessments, offering a more realistic and engaging evaluation process compared to traditional methods. Finally, this study discusses the challenges and considerations for integrating VR into HRM practices, ensuring a successful implementation of this innovative technology in the realm of recruitment. 2025 The Authors. All rights reserved. -
A Design of Agricultural Robotics for the use of Sowing and Planting
Agricultural robots is always getting better to deal with problems like population growth, fast urbanization, fierce competition for high-quality goods, worries about protecting the environment, and a lack of skilled workers. This in-depth study looks at the main uses of farming robotic systems, covering jobs like preparing the land, sowing, planting, treating plants, gathering, estimating yields, and phenotyping. Each robot is judged on how it moves, what it will be used for, whether it has sensors, a robotic arm, or a computer vision program, as well as its development stage and where it came from. The study finds trends, possible problems, and things that stop business growth by looking at these shared traits. It also shows which countries are putting money into studying and developing (R&D) for these products. The study points out four important areas - movement systems as a whole sensor, computer vision computer programs, and communication technologies - that need more research to make smart agriculture better. The results make it clear that spending money on farming robotic systems can pay off in the long run by helping with things like accurate yield estimates and short-term benefits like keeping an eye on the harvest. 2024 IEEE. -
A Deterministic Key-Frame Indexing and Selection for Surveillance Video Summarization
Video data is voluminous and impacts the data storage devices as there are CCTV surveillance videos being created every minute and stored continuously. Due to this increase in data there is a need to create semantic information out of the frames that are being stored. Video Summarization is a process that continuously monitors changes and helps in reducing the number of frames being stored. This work enables summarization to be carried out based on selecting threshold-based system that can select key-frames ideally suit for storage and further analysis. Initially a Global threshold based on Otsus method is carried out for all frames of a surveillance video and based on the set threshold a retrospective comparison is done on each frame based on statistical methods to converge on determining the keyframes. A similarity index is generated based on the iterative comparison of frames based on global and local threshold comparison. The local threshold is indexed based on Analysing Method Patterns to Locate Errors(AMPLE), An-derbergs D(AbD), Cohens Kappa(CK), Tanimoto Similarity(TS), Tversky feature contrast model(TFCM), Pearson coefficient of mean square contingency(Pmsc). The Global threshold is updated each time a keyframe is selected based on the comparison of local and global threshold. The results are compared with five surveillance videos and six methods to identify keyframes Selection Rate is the metric used for calculating the performance. 2019 IEEE. -
A device for caregiver wellbeing assessment and a method thereof /
Patent Number: 202111033343, Applicant: Dr. Ruchi Tyagi.A system and a method for wellbeing assessment to assess psychological and mental needs of caregivers. The method comprising the steps of identifying categories of psychological need, wherein said categories comprises competence, results doubting, self-esteem and fears of failures, criticism, and expectations; plotting category theme on the basis of the identified categories; determining factors affecting the psychological needs of caregivers in COVID 19 situation on the basis of the plotted category theme, where said factors comprise depression, anxiety and/or stress assessment. -
A Diabetes Detection Framework Based on Datadriven Predictive Technologies
Diabetes is a chronic disease spreading worldwide with major health challenges. It is not only caused by medical factors but other factors too such as genetic, demographics and lifestyle factors. With traditional or manual diagnosis methods, timely diagnosis becomes challenging due to complex and fragmented datasets. Recent advancements in machine learning (ML) models have greatly enhanced the efficiency and accuracy in disease diagnosis and risk evaluation. This review synthesizes the findings from the recent studies in the field of diabetes, major contributions and limitations, identifies the directions for the future work. This review has included the articles from three databases: Scopus, IEEE Xplore and PubMed; published between 2017 and 2025. The study has employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model for the review process. The scope of this work includes datadriven predictive technologies for diabetes detection, risk of complications and disease progression. It also sheds light on ongoing challenges such as data imbalance, limited interpretability, and population generalizability, while pointing to future opportunities in explainable AI and more personalized approaches to diabetes care. The review highlights that hybrid or ensemble models performing better than classical single models for risk prediction. 2025 IEEE. -
A diet and workout recommendstion system using improved conditional restricted boltzmann machines (CRBM) and method thereof /
Patent Number: 202141024082, Applicant: Dr. Vaishali M Deshmukh.
The system includes, but not limited to, one or more processor provided in a computer network; and a memory disposed in communication with each of the processor and storing processor executable instructions, the instructions comprising instructions to: process varied datasets of Food items and various nutrient parameters of Food items with respect to their ratings by various food takers and while recommending for a patient or a target user. -
A diet and workout recommendstion system using improved conditional restricted boltzmann machines (CRBM) and method thereof /
Patent Number: 202141024082, Applicant: Dr. Vaishali M Deshmukh.
The system includes, but not limited to, one or more processor provided in a computer network; and a memory disposed in communication with each of the processor and storing processor executable instructions, the instructions comprising instructions to: process varied datasets of Food items and various nutrient parameters of Food items with respect to their ratings by various food takers and while recommending for a patient or a target user. -
A Different Humour: A Quantitative and Qualitative Analysis of the Nature of Participation of Select Indian Female Stand-Up Comedians on YouTube
In recent times, Stand-up Comedy space in India has been registering itself as an alternative public sphere. As comedy has always been understood to be a masculine domain in any given society, it becomes imperative to examine if this aspect of the ????implied??? public sphere of the Stand-up Comedy space changes the dimension of comedy. This dissertation studied the nature of participation of both the male and female stand-up comics on YouTube using descriptive surveys which reported the default nature of the stand-up comedy space in India. Furthermore, the thesis studied the implications of the performances by select few female comics with specific reference to the audience reception of their comedic routines. In addition, this dissertation studied how female stand-up comics negotiate their citizenship and gender in a stand-up comedy space in India. Thematic and Critical Discourse Analysis were used to examine the theme, style, and nature of humour pervasive throughout their comedic routines. The style of humour presentation by the select female comics included subversion which was articulated in varied ways, often by marking it explicitly through themes, narratives and humour, and in other times, using it covertly. The thesis explored the dimensions of ???unladylike??? or ???unfunny??? which were used as markers to identify their routines by both themselves and their YouTube audience/commentators, the thesis also attempted to explain how these comedians found a balance between ????doing gender??? and ????undoing gender??? in these comedy spaces. The thesis concluded with an argument that the stand-up comedy space as negotiated by the select comics provides us a glimpse of an emergent feminist public sphere. -
A Dimensionality Reduction Model: A Retrospective Approach on Dementia Triggering Parameters and Feature Ranking
The medical sector has advanced in an imposing way, and are coming up with lifesaving models and wearable devices for disease predictions and patient monitoring. The prediction models and wearable devices will lead to immense amount of data collection leading to the dimensionality issues, overfitting and inaccurate results. From the pool of data that we use for our prediction model, we should be able to identify the required information and parameters which gives a positive contribution to the decision making model. Every dataset with higher number of parameters and high dimensionality will tend to the problems of overfitting. Here, we have a dataset of demented and non-demented patients with five conventional features and other physical parameters. Along with these parameters, we are adding three new prediction parameters like glyhb, BMI and Cholesterol, for proving the association of Diabetics and Dementia. After the addition of these parameters, the dataset will have thirty parameters, and dimensionality reduction is done to avoid the condition of overfitting. The work uses Principal Component Analysis(PCA)for reducing the dimensionality, t-SNE for visualization and K means clustering is used to cluster the target variable. The cluster mean of each variable is used to understand the performance of each variable in each cluster. Later, a basic feature ranking method is also implemented which can be further used for the prediction model. The performance metric used in this research work is Silhouette score, Inertia and Inter-Cluster Distance map. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A discourse analysis on spiritual text of the weekly supplement "The Speaking Tree" by the Times of India /
This research looks at the creative ways by the Times Group in reviving the dying medium ‘print’ through its weekly supplement ‘the speaking tree’. The goal is to show the relevance of spirituality in the modern society and to educate them on necessity of spirituality in the contemporary times. The researcher has analysed some articles by the spirituality experts published in the supplement vis-a-vis discourse analysis and symbols vis-a-vis semiotics.