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Multifunctional biosensor activities in food technology, microbes and toxins A systematic mini review
Biosensors have its significant applications in various fields, its use in food processing, food safety and food technology has helped to enhance the overall health of the society as it can successfully determine the presence and concentration of different microorganisms including Escheichia coli, Vibrio cholera, Clostridium spp. etc., and also determination of various toxins present in food like acrylamides, benzene, ethylbenzene, toluene, xylene, nitrosamines, Benzo[a]pyrene (BaP) which are carcinogenic. The preface of biosensors has assisted food industries for monitoring and verification of raw materials, food processing, and composition of the food and assessment of product freshness. Symbolic biosensors have been developed in recent years and yet there is much immediate need for the development of more reliable, cost-effective, sensitive and novel biosensors for rapid detection and identification of food borne pathogens and toxins. Extensive review recapitulates overall food-pathogen testing research market trends, as well as commercialization of biosensors for the food safety industry as legislation creates novel standards for microbial monitoring. Furthermore, the world's concern about the food safety and human's healthcare, the one and only biosensor's exclusive demand is nothing but an alternative in real time diagnosis of diseases causing pathogens. 2022 Elsevier Ltd -
Multifunctional characteristics of biosynthesized CoFe2O4@Ag nanocomposite by photocatalytic, antibacterial and cytotoxic applications
Carissa carandas, a traditional medicinal herb with a high concentration of antioxidant phytochemicals, has been used for thousands of years in the Ayurveda, Unani, and homoeopathic schools of medicine. By employing Carissa carandas bark extract as a reducing and capping agent in green biosynthesis, we extend this conventional application to produce CoFe2O4 and CoFe2O4@Ag nanocomposite. A variety of techniques have been used to characterize the synthesised nanocomposite, including UVVis, FTIR, XRD, FESEM, EDX, and BET. The CoFe2O4 and CoFe2O4@Ag nanocomposite demonstrated promising antibacterial action against human bacterial pathogens like B. subtilis and S. aureus as gram positive and P. aeruginosa and E. coli as gram negative with inhibition zones of 24.3 0.57, 17.4 0.75 and 20.5 0.5, 19.8 1.6 mm respectively, and the obtained results were superior to the nanocomposite without silver. Moreover, in-vitro cytotoxicity effects of biosynthesized CoFe2O4 and CoFe2O4@Ag were performed on the human breast cancer cell MCF-7. It was found that the MCF-7 cells' 50% inhibitory concentration (IC50) was 60 ?g/mL. Additionally, biosynthesized CoFe2O4 and CoFe2O4@Ag nanocomposite was used to demonstrate the photocatalytic eradication of Rhodamine Blue (RhB). Due to the addition of Ag, which increases surface area, conductivity, and increased charge carrier separation, the CoFe2O4@Ag nanocomposite exhibits a high percentage of photocatalytic degradation of ? 98% within 35 min under UV light irradiation. The photocatalytic performance of as-synthesised nanocomposite was evaluated using dye degradation-adsorption in both natural light and dark condition. Under dark conditions, it was found that 2 mg mL?1 CoFe2O4@Ag in RhB aqueous solution (5 ppm) causes dye adsorption in 30 min with an effectiveness of 72%. Consequently, it is anticipated that the CoFe2O4@Ag nanocomposite will be a promising photocatalyst and possibly a noble material for environmental remediation applications. 2023 Elsevier Ltd -
Multifunctional electrospun membranes incorporated with metal oxide nanoparticles, cellulose acetate, and polyvinylpyrrolidone for wastewater treatment: Oil/water separation, dye adsorption, and dye degradation
Multifunctional membranes have gained considerable attention as useful materials for the treatment of complex wastewater that contains dye and oil substances. Electrospun nanofiber membranes (ENM) have substantial advantages and potential for complex wastewater remediation, owing to their unique properties. In this study, an environmentally compatible ENM is fabricated by incorporating photocatalytic metal oxide nanoparticles of zinc oxide (ZnO) or silver-zinc Oxide (Ag-ZnO) into cellulose acetate (CA)/polyvinylpyrrolidone (PVP) nanofibers using electrospinning. Composite membranes ZnO/CA/PVP, Ag-ZnO/CA/PVP, ZnO/DCA/PVP (DCA: deacetylated cellulose acetate), and Ag-ZnO/DCA/PVP (deacetylated) were employed for oilwater emulsion separation, owing to their superhydrophilic and underwater superoleophobic nature, photocatalytic dye degradation due to the presence of ZnO or Ag-ZnO, and dye adsorption resulting from their high surface area. The composite membranes showed more than 95% efficiency for oil/water separation, malachite green adsorption, and photocatalytic methylene blue degradation. These membranes displayed simultaneous oilwater and dye separation efficiency, as well as antibacterial properties. The membrane we present here provides a simple and effective platform for wastewater remediation with a low energy consumption. 2024 Elsevier B.V. -
Multifunctional SnO?-Chitosan-D-carvone Nanocomposite: A Promising Antimicrobial, Anticancer, and Antioxidant Agent for Biomedical Applications
Nanocomposite made up of inorganic and biocompatible polymer have gained significant attention for biomedical applications due to their enhanced multifunctional properties, offering solutions to serious issues like antimicrobial resistance and cancer treatment. Nanocomposite composed of SnO?, chitosan and D-carvone (SnO2-Cs-Dcar) was prepared to ascertain its efficacy in application for antimicrobial, anticancer activities, and antioxidant effects. The synthesized nanocomposite was characterized by XRD, UV-Vis, FTIR, PL, SEM, TEM, and XPS techniques, confirming successful integration. XRD results confirmed the tetragonal rutile phase of SnO2. The band gap energy was calculated as 4.32eV for SnO2 nanoparticles and 3.11eV for SnO2-Cs-Dcar nanocomposite as observed from UV-Visible spectra. PL emission results showed that SnO2-Cs-Dcar nanocomposite exhibited green emission at 507nm corresponds to number oxygen vacancy site. SEM and TEM results showed that the SnO2-Cs-Dcar nanocomposite entities appear more compact, and the single SnO2 particles are less differentiated, possibly because they have been covered by chitosan and D-carvone. Antimicrobial activity against the pathogens Klebsiella pneumoniae, Candida albicans, Shigella dysenteriae, Bacillus subtilis, and Staphylococcus aureus demonstrated that SnO2-Cs-Dcar exhibited enhanced bacteriostatic effect when compared to bare SnO2. MTT assay on MOLT-4 cancer cells revealed that SnO2-Cs-Dcar nanocomposite exhibited enhanced anticancer activity upon compared to SnO? nanoparticles. The IC50 values were calculated as 13.6 for SnO2 and 12.1 for SnO2-Cs-Dcar nanocomposite. SnO?-Cs-Dcar nanocomposites exhibits high antioxidant activity evidenced by improved free radical scavenging action in comparison with a bare SnO?. Experimental result indicates that the SnO?-Cs-Dcar nanocomposites can be used as biocidal agent for antimicrobial and anticancer therapies. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
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 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.