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Multigene Genetic Programming Based Prediction of Concrete Fracture Parameters of Unnotched Specimens
This study explores the fracture energy of notched and unnotched concrete specimens subjected to the classical three-point bend test, instantiating a gradational step in the continued development of concrete fracture mechanics. An experimental campaign involving 18 notched test specimens and nine unnotched specimens of three different grades of concrete, an examination of the existing literature models for unnotched specimens, and a novel Multigene Genetic programming (MGGP) based concrete fracture energy model for unnotched specimens are integral to this study. As a salient result, the multiple approaches to quasi-brittle materials adopted in the study, highlighted the criticality of the determination of fracture energy, tensile strength and characteristic length for the crack width study. The failure modes of notched and unnotched specimens were found to be similar. The reported literature has mainly focused on a limited number of fracture energy influencing parameters. Therefore, six impact parameters have been chosen and incorporated into the present study to provide a more acceptable explanation of concrete fracture behaviour. A sensitivity analysis of the parameters and an error analysis of the model undertaken have established the accuracy and robustness of the developed MGGP model. 2023 by the authors. Licensee C.E.J, Tehran, Iran. -
Multilayer classification based Alzheimer's disease detection
Hippocampus, a small brain region plays a role in the initiation of the neurodegenerative pathways that leadto Alzheimer's. Humans with MCI are probable to develop Alzheimer's disorder. Hippocampal volume has been proven to indicate which patients with MCI will later develop Alzheimer's. Brain degeneration in MCI progresses over time and varies from person - to - person, making early detection difficult. Magnetic resonance imaging is a tool in diagnosing clinically suspected Alzheimer's disease. Information about the historical development of structural changes as the disease progresses from preclinical to overt stages is shaping understanding of the disease, and also guides diagnosis and treatment decisions in the future. In this study, we developed a new multilayer classification method to identify Alzheimer's disease from brain MRI using contour model and multilayer classifier. This method is evaluated on 436 samples of OASIS dataset and achieved accuracy of method is 93.75 %. 2024 Author(s). -
Multilayer flow and heat transport of nanoliquids with nonlinear Boussinesq approximation and viscous heating using differential transform method
Multilayer fluid flow models are significant in various applications, namely, cooling electronic systems, solar thermal systems, and nuclear reactors. The density of a fluid fluctuates nonlinearly due to large temperature difference circumstances in thermal systems. Thus, the linear Boussinesq approximation is no longer relevant. Therefore, this article describes a multilayer flow of nanoliquids in the presence of nonlinear Boussinesq approximation. The hybrid nanoliquid layer is sandwiched between two nanoliquid layers. The single-phase khanafer-vafai-lightstone model is implemented to simulate the nanoliquids. The quadratic density temperature fluctuation and viscous heating are taken into account. The temperature and velocity across the interface are assumed to be continuous. The equations that govern the problem are solved analytically by using the differential transformation method. The results show that the presence of a hybrid nanoliquid layer affects the velocity and heat transfer properties of the nanofluid flow. Hybrid nanofluid can be used to achieve the desired multilayer flow properties of a nanofluid and its heat transfer properties. Further, the quadratic convection aspect increases the velocity distributions. 2021 Wiley Periodicals LLC -
Multilevel CNN Based Hybrid Framework for Adaptive Credit Card Fraud Detection
Credit card fraud presents a substantial problem to financial organizations, as fast changing fraudulent activities necessitate advanced detection techniques. Conventional machine learning methods frequently encounter challenges with adaptability and precision in imbalanced datasets. This study presents a multilevel CNN-based hybrid architecture that combines deep convolutional networks with traditional ensemble classifiers for adaptive credit card fraud detection. The platform includes an adaptive learning module that facilitates ongoing model upgrades, guaranteeing responsiveness to emerging fraud trends. The system, evaluated using a benchmark Kaggle dataset, attained an accuracy of 99.48%, precision of 98.76%, recall of 99.05%, F1-score of 98.90%, and AUC-ROC of 99.91%, outperforming established baseline models such as Logistic Regression, Random Forest, and XGBoost. The suggested system's capacity to integrate deep feature extraction with hybrid classifiers yields enhanced detection efficiency, reduced false positives, and improved generalization. This research enhances fraud detection by overcoming the constraints of static models, rendering it applicable for real-time financial applications and adaptable to emerging threats. 2025 IEEE. -
Multilevel Inverter-Fed Closed Loop Control and Analysis of Induction Motor Drive
Multilevel inverters have discovered more extensive extent of utilization in moderate and also in high-power adjustable-speed drives. This chapter introduces a vector control scheme of induction motor drive which includes pulse width modulations for reducing harmonics and total harmonic distortion (THD). For better control of induction motor, indirect vector control has been applied which offers advantages such as removal of flux sensor, more dynamic responses, and minimum torque pulses is applied. The inverter named neutral point clamped inverter is applied for observing dynamic control of the motor drive along with efficiency. The main attention of this chapter is to improve the performance of indirect vector controller. The THD analysis proves the better operation of induction motor as compared to conventional voltage source inverter-fed induction motor drive. By the help of MATLAB simulation, the dynamic performance as well as steady-state of multilevel inverter-based drive are analyzed. 2024 Scrivener Publishing LLC. -
Multilevel Quantum Inspired Fractional Order Ant Colony Optimization for Automatic Clustering of Hyperspectral Images
Hyperspectral images contain a wide variety of information, varying from relatively large regions to smaller manmade buildings, roads and others. Automatic clustering of various regions in such images is a tedious task. A multilevel quantum inspired fractional order ant colony optimization algorithm is proposed in this paper for automatic clustering of hyperspectral images. Application of fractional order pheromone updation technique in the proposed algorithm produces more accurate results. Moreover, the quantum inspired version of the algorithm produces results faster than its classical counterpart. A new band fusion technique, applying principal component analysis and adaptive subspace decomposition, is successfully proposed for the pre-processing of hyperspectral images. Score Function is used as the fitness function and K-Harmonic Means is used to determine the clusters. The proposed algorithm is implemented on the Xuzhou HYSPEX dataset and compared with classical Ant Colony Optimization and fractional order Ant Colony Optimization algorithms. Furthermore, the performance of each method is validated by peak signal-to-noise ratio which clearly indicates better segmentation in the proposed algorithm. The Kruskal-Wallis test is also conducted along with box plot, which establishes that the proposed algorithm performs better when compared with other algorithms. 2020 IEEE. -
Multilevel Security and Dual OTP System for Online Transaction Against Attacks
In the current internet technology, most of the transactions to banking system are effective through online transaction. Predominantly all these e-transactions are done through e-commerce web sites with the help of credit/debit cards, net banking and lot of other payable apps. So, every online transaction is prone to vulnerable attacks by the fraudulent websites and intruders in the network. As there are many security measures incorporated against security vulnerabilities, network thieves are smart enough to retrieve the passwords and break other security mechanisms. At present situation of digital world, we need to design a secured online transaction system for banking using multilevel encryption of blowfish and AES algorithms incorporated with dual OTP technique. The performance of the proposed methodology is analyzed with respect to number of bytes encrypted per unit time and we conclude that the multilevel encryption provides better security system with faster encryption standards than the ones that are currently in use. 2019 IEEE. -
Multilingual Sentiment Analysis of YouTube Live Stream using Machine Translation and Transformer in NLP
YouTube has become one of the all-inclusive video streaming sources on the internet. Today, the news is streamed on YouTube, marketing of a product is done live on YouTube and it has become a platform for one of the biggest PR producers for companies. Various companies have proposed an optimized way of understanding and getting the opinions of the viewers from YouTube live chat and find the best possible way to provide relevant and informative content to boost the business strategy. This study uses Natural Language Processing (NLP) based approach along with NLP transformers to classify and analyses the sentiment. 2022 IEEE. -
Multilingual Sentiment Analytics for India's NEP 2020
This study presents a multilingual sentiment analysis framework for evaluating public sentiments on India's National Education Policy (NEP) 2020. The authors developed a dataset related to NEP 2020 using web scraping from open sources. The curated dataset comprises 50,000 social media posts (English: 30,000, Hindi: 12,000, Tamil: 8,000) processed through a confidence-gated hybrid annotation pipeline. Sentiment labels were created using Transformer models (BERT, mBERT, XLMR) and validated by native-speaker with F1-scores of 87.6%, 81.2% and 78.0% for English, Hindi and Tamil respectively: outperforming baselines (SVM, Naive Bayes, BiLSTM) by 12-18% (p<0.001). We use computational efficiency measures to illustrate that training takes 3.2-5.3 hours and inference lasts between 118 and 187 posts per second. Topic modeling revealed sentiment divergences: positive for linguistic inclusivity and teacher training, negative for affordability and infrastructure. Cross-linguistic analysis showed English-Hindi convergence (similarity: 0.61) versus Tamil divergence (0.46), reflecting regional priorities. Tamil emphasized linguistic identity while English prioritized implementation critiques. Quantitative policy impact analysis shows very strong correlation (r=0.68, p<0.01) between regional sentiment scores and state adoption rates. This open-sourced contribution is filling the crucial gap of inclusive policy analytics in multilingual society informing evidence-based policy. 2025 IEEE. -
Multilingual Voice-Assisted for Traffic Sign Detection and Classification in Adverse Weather Conditions
In a world where millions of people are wounded in auto accidents each year due to negligence, a lack of understanding of traffic laws, and bad weather, there is an urgent need for greater road safety. This is particularly the case in India, where a disproportionately high number of traffic accidents lead to numerous fatalities. Ignoring traffic signs raises these risks and endangers not only vehicles but also passengers and pedestrians. This project addresses the significant issue of traffic sign recognition in bad weather and offers voice-based instruction in many languages to increase road safety. Using a mix of state-of-the-art technologies, including YOLOv8 for real-time sign detection and the Google Translate API, which supports NLP tasks, this research offers a full solution. The model's remarkable precision and efficacy underscore its capacity to revolutionize traffic safety and furnish a more secure and expedient driving encounter. With the world moving towards more autonomous mobility, this study is laying the groundwork for safer and more effective driving in the future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Multimedia Enhanced Teaching and Learning with Special Reference to Developing Cognitive Skills
Indian Streams Research Journal, Vol-3 (7), pp. 25-28. ISSN-2230-7850 -
Multimodal artificial intelligence for early cancer detection via liquid biopsy, imaging, and clinical records
Tumours are diverse and multiscale, making it difficult for modern medicine to diagnose early cancer. Using structured clinical data, radiologic imaging features, and liquid samples, this research presents a multimodal AI framework for the early and reliable detection of cancer. The proposed approach surpasses single-modality approaches by integrating signals from various domains, including cancer genetic, anatomical, and physiological data. Using attention-based fusion, representation learning, and better preprocessing, we developed a prediction model that fine-tuned the weights of different modes. The results of the experiments demonstrated that it outperformed unimodal models on all datasets in terms of sensitivity, specificity, and generalisation. The framework has potential for screening purposes because of its ability to detect cancer at an early stage. Clinical confidence and interpretability were both boosted by the results of explainability tests, which revealed substantial feature contributions. The suggested multimodal framework outperformed unimodal baselines across all assessment cohorts with an AUC of 0.94, sensitivity of 0.91, and specificity of 0.88. Experimental results confirm multimodal fusion's clinically interpretable early cancer detection and precision oncology decision assistance. Copyright 2026. Published by Elsevier B.V. -
Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey
Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information. The Electrochemical Society -
Multimodal data analytics for climate and water resources management
The incorporation of multimodal data analytics into climate and water resource management has become a groundbreaking strategy for tackling intricate environmental issues. This chapter examines the importance of integrating various data sourcesincluding satellite imagery, weather sensors, textual reports, and social media feedsto develop a comprehensive perspective on climate and water systems. It addresses key challenges such as data heterogeneity, computational demands, and potential biases while showcasing the significant benefits of multimodal data in enhancing predictive modeling and decision-making. The discussion extends to advanced methodologies for data acquisition, integration, and feature extraction, with a focus on machine learning and deep learning techniques. Additionally, real-world applications in climate prediction, drought and flood forecasting, and water quality assessment are explored. The chapter also considers ethical concerns and future advancements in multimodal analytics, emphasizing the importance of responsible data utilization and innovative research to strengthen climate adaptation and water resource management efforts. 2026 Elsevier Inc. All rights reserved. -
Multimodal data analytics for social media and user behavior
The introduction of social media has prompted an explosion of diverse data types, such as textual content, pix, videos, and audio. Traditional unimodal analysis techniques do not effectively depict the difficult interactions between exceptional fact sorts and consumer sports. Multimodal data analytics addresses this difficulty through fusing one-of-a-kind modalities to unlock deeper insights, improving the accuracy and scope of social media analysis. This bankruptcy looks into the significance of multimodal facts in understanding consumer behavior, sentiment analysis, content material engagement assessment, and trend prediction. The bankruptcy starts off with the exploration of various sources of statistics in social media analytics, together with textual content posts, visual content, and consumer interactions. It then explores preprocessing and function extraction strategies utilized to prepare raw multimodal data for the usage of gadget gaining knowledge of. In-intensity methodologies, inclusive of natural language processing for text evaluation, computer vision for photo and video interpretation, and speech recognition for audio processing, are expounded in extraordinary detail. Integration of these modalities via fusion techniquesearly fusion, past due fusion, and hybrid modelsis also explored. 2026 Elsevier Inc. All rights reserved. -
Multimodal data generation and synthesis
Multimodal data generation and synthesis have become new promising directions in artificial intelligence research, making possible the combination and transformation of the different data modalities: text, images, audio, and video. In this chapter a look will be made about the principles, methodologies, applications, and challenges linked with multimodal data, bringing attention to the current trends and needs regarding multimodal systems and systems approaches to tackle complex real-world challenges across the medical and health care, autonomous systems, entertainment, and extended reality (XR) fields. The chapter introduces multimodal data and discusses how the approach differs from unimodal methods, considering the merits of working with multiple data forms. Multimodal systems present richer and more comprehensive representations that lead to better decision-making and provide a better interaction with users. The complexity due to alignment, synchronization, and representation of diverse modes is inherently difficult. This section further discusses state-of-the-art techniques in multimodal synthesis, especially focusing on generative approaches like generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. These methods are shown to facilitate cross-modal transformations, such as text-to-image or audio-to-video synthesis, driving innovation in artificial intelligence and beyond. Applications of multimodal data synthesis are discussed in detail, underscoring its transformative impact. In health care, for instance, synthesizing medical images paired with textual annotations enhances diagnostic accuracy and medical training. Autonomous vehicles benefit from the integration of LiDAR, visual, and auditory data, enabling robust decision-making in real-time environments. Similarly, in entertainment and XR, multimodal synthesis is redefining content creation, making immersive experiences more personalized and dynamic. The chapter also delves into novel applications such as multimodal translation, exemplified by systems that translate sign language into spoken text, fostering inclusivity and accessibility. Despite its potential, multimodal synthesis faces critical challenges, including bias in data and models, privacy concerns, and the ethical implications of creating hyperrealistic synthetic data, such as deepfakes. All these raise pressing concerns, and addressing these requires robust privacy-preserving techniques, bias-mitigation strategies, and stringent ethical guidelines. 2026 Elsevier Inc. All rights reserved. -
Multimodal Early Fusion Strategy Based on Deep Learning Methods for Cervical Cancer Identification
It is essential to enhance the accuracy of automatic cervical cancer diagnosis by combining multiple forms of information obtained from a patients primary examination. However, existing multimodal systems are not very effective in detecting correlations between different types of data, leading to low sensitivity but high specificity. This study introduces a deep learning system for automatic diagnosis of cervical cancer by incorporating multiple sources of data. First, a convolutional neural network (CNN) to transform the image database to a vector that can be combined with non-image datasets is used. Subsequently, an investigation of jointly the nonlinear connections between all image and non-image data in a deep neural network is performed. Proposed deep learning-based method creates a unified system that takes advantage of both image and non-image data. It achieves an impressive 89.32% sensitivity at 91.6% specificity when diagnosing cervical intraepithelial neoplasia on a wide-ranging dataset. This result is far superior to any single-source system or prior multimodal approaches. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Multimodal Emotion Recognition in HumanComputer Interaction Using MFF-CNN
The rise of technology in the digital era has amplified the importance of understanding human emotions in enhancing humancomputer interactions. Traditional interfaces, mainly focused on logical tasks, often miss the nuances of human emotion, creating a gap between human users and technology. Addressing this gap, the development of the HumanComputer Interface for emotional intelligence uses advanced algorithms and deep learning models to accurately recognize emotions from various cues like facial expressions, voice, and written text. This paper presented a significant approach for emotion detection in HCI and the challenges faced in capturing genuine emotional responses. Historically, the emphasis in HCI design was on operational tasks, neglecting emotional nuances. However, the tide is changing toward embedding emotional intelligence into these interfaces, leading to enhanced user experiences. This research introduces the MFF-CNN, a neural network model combining both textual and visual data for accurate emotion detection. Through sophisticated algorithms and the integration of advanced machine learning techniques, this paper presents a refined approach to emotion detection in HCI, supported by a comprehensive review of related works and a detailed methodology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Multimodal Emotion Recognition Using Deep Learning Techniques
Humans have the ability to perceive and depict a wide range of emotions. There are various models that can recognize seven primary emotions from facial expressions (joyful, gloomy, annoyed, dreadful, wonder, antipathy, and impartial). This can be accomplished by observing various activities such as facial muscle movements, speech, hand gestures, and so forth. Automatic emotion recognition is a significant issue that has been a hotly debated research topic in recent years. At the moment, several research people have taken a component in inheriting or extra multimodal for higher understanding. This paper indicates a method for emotion recognition that makes use of 3 modalities: facial images, audio indicators, and text detection from FER and CK+, RAVDESS, and Twitter tweets datasets, respectively. The CNN model achieved 66.67 percent on the FER-2013 dataset of labeled headshots while on the CK+ dataset, 98.4 percent accuracy was obtained. Finally, diverse fusion strategies had been approached, and each of those fusion techniques gave distinctive results. This project is a step towards the sense of interaction between human emotional aspects and the growing technology that is the future of development in today's world. 2022 IEEE. -
Multimodal emotional analysis through hierarchical video summarization and face tracking
The era of video data has fascinated users into creating, processing, and manipulating videos for various applications. Voluminous video data requires higher computation power and processing time. In this work, a model is developed that can precisely acquire keyframes through hierarchical summarization and use the keyframes to detect faces and assess the emotional intent of the user. The key-frames are used to detect faces using recursive Viola-Jones algorithm and an emotional analysis for the faces extracted is conducted using an underlying architecture developed based on Deep Neural Networks (DNN). This work has significantly contributed in improving the accuracy of face detection and emotional analysis in non-redundant frames. The number of frames selected after summarization was less than 30% using the local minima extraction. The recursive routine introduced for face detection reduced false positives in all the video frames to lesser than 2%. The accuracy of emotional prediction on the faces acquired through the summarized frames, on Indian faces achieved a 90%. The computational requirement scaled down to 40% due to the hierarchical summarization that removed redundant frames and recursive face detection removed false localization of faces. The proposed model intends to emphasize the importance of keyframe detection and use them for facial emotional recognition. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.