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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 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. -
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 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 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. -
Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach
Recent years have seen a significant increase in attention in multimodal biometric systems for personal identification especially in unconstrained environments. This paper presents a multimodal recognition system by combining feature level fusion of ear and profile face images. Multimodal biometric systems by combining face and ear can be used in an extensive range of applications because we can capture both the biometrics in a non-intrusive manner. Local texture feature descriptor, BSIF is used to extract discriminative features from biometric templates. Feature level and score level fusion is experimented to improve the performance of the system. Experimental results on different public datasets like GTAV, FEI, etc., show that the proposed method gives better performance in recognition results than individual modality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Multiple Approaches in Retail Analytics to Augment Revenues
Knowledge is power. The retail sector has been revolutionized around the clock by the plentiful product knowledge available to customers. Today, customers can use the knowledge available online at any time to study, compare and purchase products from anywhere. Retail companies can stay ahead of shopper trends by using retail information analytics to discover and analyze online and in-store shopper patterns. A product recommender will suggest products from a wide selection that would otherwise be very difficult to locate for the customer. The algorithm would recommend various products, increase the sales of items that would otherwise be difficult to sell. Market basket analysis is a common use scenario for the search for frequent patterns, which involves analyzing the transactional data of a retail store to decide which items are bought together. To do so data from online resource has been taken, which is analyzed and several conclusions were made. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Multiple Safety Equipment's Detection at Active Construction sites Using Effective Deep Learning Techniques
The safety of human labour is the most important thing in this era no matter where the labour force works. Governments and various NGOs focus on ensuring the delivery of the top safety to the labor class of the country. One such example is the working of the labour force at huge construction sites. For them a lot of work includes a huge amount of risks hence following full safety is the need of the hour for the workers working at construction sites. In order to deal with proper monitoring of the safety being followed at Construction sites. In order to make use of the latest technologies in this field also some of the good object detection models can be used for detecting the safety equipment of the workers which include things like Hard Hats, Masks, Vest, Boots. A lot of research is going on in improving the detection speed and accuracy of objects using state-of-the-art techniques in Computer Vision and this could lead to providing better results. Based on the available research and compute resources future work can be done to improve the results in this specific domain also. 2022 IEEE. -
Multiple slip effects on MHD non-Newtonian nanofluid flow over a nonlinear permeable elongated sheet: Numerical and statistical analysis
Purpose: The purpose of this paper is to examine the interaction effects of a transverse magnetic field and slip effects of Casson fluid with suspended nanoparticles over a nonlinear stretching surface. Mathematical modeling for the law of conservation of mass, momentum, heat and concentration of nanoparticles is executed. Design/methodology/approach: Governing nonlinear partial differential equations are reduced into nonlinear ordinary differential equations and then shooting method is employed for its solution. The slope of the linear regression line of the data points is calculated to measure the rate of increase/decrease in the reduced Nusselt number. Findings: The effects of magnetic parameter (0=M=4), Casson parameter (0.1=?<8), nonlinear stretching parameter (0=n=3) and porosity parameter (0=P=6) on axial velocity are shown graphically. Numerical results were compared with another numerical approach and an excellent agreement was observed. This study reveals the fact that the Brownian motion parameter and boundary layer thickness have a direct relationship with temperature. Also, Brownian motion and thermophoresis contribute to an increase in the thermal boundary layer thickness. Originality/value: Despite the immense significance and repeated employment of non-Newtonian fluids in industry and science, no attempt has been made up till now to inspect the Casson nanofluid flow with a permeable nonlinear stretching surface. 2019, Emerald Publishing Limited. -
Multiple solutions and stability analysis in MHD non-Newtonian nanofluid slip flow with convective and passive boundary condition: Heat transfer optimization using RSM-CCD
This study explores the effect of Williamson nanofluid in the presence of radiation and chemical reaction caused by stretching or shrinking a surface with convective boundary conditions. After implementing two-component model and Lie group theory, the transformed ODEs are solved using the RungeKutta DormandPrince (RKDP) shooting approach technique. The dual solutions are predicted for certain range of physical nanofluid parameters, such as Williamson parameter ((Formula presented.)), stretching/shrinking parameter ((Formula presented.)), and suction parameter ((Formula presented.)) with different slip (Formula presented.) and magnetic M parameters. Contour plots are generated for the stable branch of the Nusselt number ((Formula presented.)) for different combinations, providing insights into the heat transfer characteristics. The eigenvalue problem is solved in order to predict flow stability. The optimization of heat transfer in nanoliquid is conducted by RSM-CCD. The resulting quadratic correlation enables the prediction of the optimal Nusselt number for (Formula presented.), (Formula presented.), and (Formula presented.). This investigation is motivated by various applications including manufacturing processes, thermal management systems, energy conversion devices, and other engineering systems where efficient heat transfer iscrucial. 2023 Wiley-VCH GmbH. -
Multiplier-free Realization of High throughout Transpose Form FIR Filter
This paper presents a multiplier-free realization of the block finite impulse response (FIR) filter in transpose form configuration using binary constant shifts method (BCSM). The proposed architecture is synthesized using Xilinx Vivado and Cadence RTL Encounter compiler for the area and power analysis and is compared with the existing works in the literature. The comparison highlights the advantages of the proposed architecture in terms of power, hardware complexity and throughput for realizing reconfigurable high throughput block FIR filters. 2020 IEEE. -
Multitask EfficientNet affective computing for student engagement detection
In the realm of education, feedback emerges as a pivotal component, serving to foster engagement and interaction while also facilitating the refinement of teaching methods to capture and maintain student attention. Traditional classroom assessment methods often struggle to accurately gauge the degree of comprehension among students during lectures, relying on manual comment collection that inherently carries the risk of inaccuracies. In response to this challenge, a novel system has been proposed, harnessing the power of Facial Emotion Recognition (FER) technology to capture student feedback. Within this framework, students are given a unique avenue to convey their emotions and reactions, employing facial expressions and gestures as the means to communicate. This innovative approach enables the analysis of students emotional responses and thereby provides invaluable insights into their comprehension levels, as well as the overall quality and engagement experienced during lectures. The approach takes shape through the utilization of Computer Vision techniques, with a particular focus on an unobtrusive methodology for assessing students overall engagement. Overcoming limitations of traditional assessment, our approach integrates compound scaling, employing the proposed Multitask EfficientNetB0 model recognized for its proved accuracy in emotion recognition (95.7%) and behavior analysis (96.3%) across diverse datasets (DAiSEE, iSED, iSAFFE). The behavioral classification system categorizes students into Engaged and Disengaged classes within a multi-class framework, providing nuanced insights into comprehension and Student engagement. Assessment metrics, including ROC Curves, Precision, Recall, and F1-Score, ensure a thorough evaluation. Our systems adaptability is demonstrated across varied educational environments, showcasing real-world efficacy in classrooms, laboratories, and seminar halls. The inclusion of MTCNN enhances face detection capabilities, facilitating robust analysis in dynamic scenarios. Expanding its applicability, the model has been put to the test in a range of educational settings, including classrooms, laboratory environments, and seminar halls, offering dual-capability analysis of both emotions and behavior. This comprehensive approach yields nuanced insights into student engagement and interaction, and its performance has been validated through real-world deployment within classrooms and seminars The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Multivariate statistical optimization of phenolics and antioxidants from nutmeg seeds (Myristica fragrans Houtt)
The present study aimed to optimize the phenolic and antioxidant-rich extract from the nutmeg (Myristica fragrans Houtt) by using a two-factor 26-run central composite design-based response surface methodology tool. The selected parameters were extraction period (2 to 5days), solvent-to-water ratio (v/v) (50100%), and type of solvent (acetone or ethanol). The optimized extract at conditions of 3.14days incubation and 68% (v/v) acetone showed total phenolic content (TPC), total flavonoid content (TFC), and DPPH antioxidant assay as 376.38mg GAE/g DW, 34.40mg QUE/g DW and 842.46mg AAE/g DW, respectively. Among the nineteen (19) compounds identified by the LCMS, myristicin (37.74%) was found to be the highest. Nine (9) alkane-fatty acyl compounds were determined by the GCMS analysis, as well. Additionally, SEM and XRD revealed sheet-like anatomy with the presence of Carbon (C), Oxygen (O) and Potassium (K). The study presented a unique approach to optimizing phenolic-rich antioxidant extracts from nutmeg using response surface methodology, offering valuable insights for more efficient extraction of bioactive compounds with minimal resource waste and potentially enhancing the utilization of nutmeg's nutraceutical properties. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Multiwavelength spectral modelling of the candidate neutrino blazar PKS 0735+178
The BL Lac object PKS 0735+178 was in its historic ?-ray brightness state during 2021 December. This period also coincides with the detection of a neutrino event IC 211208A, which was localized close to the vicinity of PKS 0735+178. We carried out detailed ?-ray timing and spectral analysis of the source in three epochs: (a) quiescent state (E1), (b) moderate-activity state (E2), and (c) high-activity state (E3) coincident with the epoch of neutrino detection. During the epoch of neutrino detection (E3), we found the largest variability amplitude of 95 per cent. The ?-ray spectra corresponding to these three epochs are well fit by the power-law model and the source is found to show spectral variations with a softer when brighter trend. In epoch E3, we found the shortest flux doubling/halving time of 5.75 h. Even though the spectral energy distribution in the moderate-activity state and in the high-activity state could be modelled by the one-zone leptonic emission model, the spectral energy distribution in the quiescent state required an additional component of radiation over and above the leptonic component. Here, we show that a photomeson process was needed to explain the excess ?-ray emission in the hundreds of GeV that could not be accounted for by the synchrotron self-Compton process. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs
In todays information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results. 2023 Tech Science Press. All rights reserved.