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Multi-layer Stacking-based Emotion Recognition using Data Fusion Strategy
Electroencephalography (EEG), or brain waves, is a commonly utilized bio signal in emotion detection because it has been discovered that the data recorded from the brain seems to have a connection between motions and physiological effects. This paper is based on the feature selection strategy by using the data fusion technique from the same source of EEG Brainwave Dataset for Classification. The multi-layer Stacking Classifier with two different layers of machine learning techniques was introduced in this approach to concurrently learn the feature and distinguish the emotion of pure EEG signals states in positive, neutral and negative states. First layer of stacking includes the support vector classifier and Random Forest, and the second layer of stacking includes multilayer perceptron and Nu-support vector classifiers. Features are selected based on a Linear Regression based correlation coefficient (LR-CC) score with a different range like n1, n2,n3,n4 a, for d1 used n1 and n2 dataset,for d2 dataset, combined dataset of n3 and n4 are used and developed a new dataset d3 which is the combination of d1 and d2 by using the feature selection strategy which results in 997 features out of 2548 features of the EEG Brainwave dataset with a classification accuracy of emotion recognition 98.75%, which is comparable to many state-of-the-art techniques. It has been established some scientific groundwork for using data fusion strategy in emotion recognition. 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved. -
Multi-level Prediction of Financial Distress of Indian Companies Using Machine Learning
Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Multi-lingual Spam SMS detection using a hybrid deep learning technique
Nowadays, the incremental usage of mobile phones has made spam SMS messages a big concern. Sending malicious links through spam messages harms our mobile devices physically, and the attacker might have a chance to steal sensitive information from our devices. Various state-of-the-art research works have been proposed for SMS spam detection using feature-based, machine, and deep learning techniques. These approaches have specific limitations, such as extracting and selecting signifi-cant and quality features for efficient classification. Very few deep learning techniques are only used for classifying spam detection. Moreover, the benchmark spam datasets written in English are mostly used for evaluation. Very few papers have detected spam messages for other languages. Hence, this paper creates a multilingual SMS spam dataset and proposes a hybrid deep learning technique that combines the Convolutional Neural Network and Long Short-Term Memory (LSTM) model to classify the message dataset. The performance of this proposed hybrid model has been compared with the baseline deep learning models using accuracy, precision, recall, and F1-score metrics. 2022 IEEE. -
Multi-Model Traffic Forecasting in Smart Cities using Graph Neural Networks and Transformer-based Multi-Source Visual Fusion for Intelligent Transportation Management
In the intelligent transportation management of smart cities, traffic forecasting is crucial. The optimization of traffic flow, reduction of congestion, and improvement of theoverall transportation systemefficiency all depend on accurate traffic pattern projections. In order to overcome the difficulties causedby the complexity and diversity of urban traffic dynamics, this research suggests a unique method for multi-modal traffic forecasting combining Graph Neural Networks (GNNs) and Transformer-based multi-source visual fusion. GNNs are employed in this method to capture the spatial connections betweenvarious road segments and to properly reflect the basic structure of the road network. The model's ability to effectively analyse traffic dynamics and relationships between nearby locations is enhanced by graphsrepresenting the road layout, which also increases theoutcome of traffic predictions. Recursive Feature Elimination (RFE) is employed to improve the model's feature selection process and choose the most pertinent features for traffic prediction, producing forecasts that are more effective and precise. Utilizing real-time data, the performance of the suggested strategywasassessed, enabling it to adjust to shifting traffic patterns and deliver precise projections for intelligent transportation management. The empirical outcomes show exceptional results ofperformance metrics for the proposed approach, achieving anamazing accuracy of 99%. The resultsshow that the suggested techniques findings have the ability to anticipate traffic and exhibit a superior level of reliability whichsupports efficient transportation management in smart cities. The Author(s), under exclusive licence to Intelligent Transportation Systems Japan 2024. -
Multi-objective ANT lion optimization algorithm based mutant test case selection for regression testing
The regression testing is principally carried out on modified parts of the programs. The quality of programs is the only concern of regression testing in the case of produced software. Main challenges to select mutant test cases are related to the affected classes. In software regression testing, the identification of optimal mutant test case is another challenge. In this research work, an evolutionary approach multi objective ant-lion optimization (MOALO) is proposed to identify optimal mutant test cases. The selection of mutant test cases is processed as multi objective enhancement problem and these will solve through MOALO algorithm. Optimal identification of mutant test cases is carried out by using the above algorithm which also enhances the regression testing efficiency. The proposed MOALO methods are implemented and tested using the Mat Lab software platform. On considering the populace size of 100, at that point the fitness estimation of the proposed framework, NSGA, MPSO, and GA are 3, 2.4, 1, and 0.3 respectively. The benefits and efficiencies of proposed methods are compared with random testing and existing works utilizing NSGA-II, MPSO, genetic algorithms in considerations of test effort, mutation score, fitness value, and time of execution. It is found that the execution times of MOALO, NSGA, MPSO, and GA are 2.8, 5, 6.5, and 7.8 respectively. Finally, it is observed that MOALO has higher fitness estimation with least execution time which indicates that MOALO methods provide better results in regression testing. 2021 Scientific Publishers. All rights reserved. -
Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans
Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of liver surgery. This study proposed a novel dynamic approach to improve the fuzzy c-means (FCM) clustering algorithm for automatic localization and segmentation of liver and hepatic lesions from CT scans. More specifically, we developed a powerful optimization approach in terms of accuracy, speed, and optimal convergence based on fast-FCM, chaos theory, and bio-inspired ant lion optimizer (ALO), named (CALOFCM), for automatic liver and hepatic lesion segmentation. We employed ALO to guide the FCM to determine the optimal cluster centroids for segmentation processes. We used chaos theory to improve the performance of ALO in terms of convergence speed and local minima avoidance. In addition, chaos theory-based ALO prevented the FCM from getting stuck in local minima and increased computational performance, thus increasing stability, reducing sensitivity in the iterative process, and allowing the best centroids to be used by FCM. We validated the proposed approach on a group of patients with abdominal liver CT images, and the results showed good detection and segmentation performance compared with other popular techniques. This new hybrid approach allowed for the clinical diagnosis of hepatic lesions earlier and more systematically, thereby helping medical experts in their decision-making. 2020 Elsevier B.V. -
Multi-view video summarization
Video summarization is the most important video content service which gives us a short and condensed representation of the whole video content. It also ensures the browsing, mining, and storage of the original videos. The multi- view video summaries will produce only the most vital events with more detailed information than those of less salient ones. As such, it allows the interface user to get only the important information or the video from different perspectives of the multi-view videos without watching the whole video. In our research paper, we are focusing on a series of approaches to summarize the video content and to get a compact and succinct visual summary that encapsulates the key components of the video. Its main advantage is that the video summarization can turn numbers of hours long video into a short summary that an individual viewer can see in just few seconds. Springer India 2016. -
Multicomponent Synthesis Strategies, Catalytic Activities, and Potential Therapeutic Applications of Pyranocoumarins: A Comprehensive Review
Fused coumarins, because of their remarkable biological and therapeutic properties, particularly pyranocoumarins, have caught the interest of synthetic organic chemists, leading to the development of more efficient and environmentally friendly protocols for synthesizing pyranocoumarin derivatives. These compounds are the most promising heterocycles discovered in both natural and synthetic sources, with anti-inflammatory, anti-HIV, antitubercular, antihyperglycemic, and antibacterial properties. This review employed the leading scientific databases Scopus, Web of Science, Google Scholar, and PubMed up to the end of 2022, as well as the combining terms pyranocoumarins, synthesis, isolation, structural elucidation, and biological activity. Among the catalysts employed, acidic magnetic nanocatalysts, transition metal catalysts, and carbon-based catalysts have all demonstrated improved reaction yields and facilitated reactions under milder conditions. Herein, the present review discusses the various multicomponent synthetic strategies for pyranocoumarins catalyzed by transition metal-based catalysts, transition metal-based nanocatalysts, transition metal-free catalysts, carbon-based nanocatalysts, and their potential pharmacological activities. 2023 The Authors. Chemistry & Biodiversity published by Wiley-VHCA AG, Zurich, Switzerland. -
Multidrug Resistant Bacteria: The Fatal Menace in Healthcare
Mapana Journal of Sciences, Vol-11 (1), pp. 31-47. ISSN-0975-3303 -
Multifaceted Anticancer Potential of Trigonella foenum-graecum
The past decade saw a revolution in the discovery of genetic and epigenetic factors paving way for various types of cancers. With a better understanding of the causes, comes a chance of wider possibility of targeting the root causes of cancer. Nature is a storehouse of natural anticancer molecules, many yet to be explored. Trigonella foenumgraecum (fenugreek) is one such plant, having a huge potential for modulating prophylactic and therapeutic aspects of cancer. Cuisines world over make uses of this legume in multiple ways. This small herb has been found to be loaded with many secondary metabolites like diosgenin, coumarin, trigonelline and so on that reduce inflammation, promote apoptosis, act as antioxidants, regulates cell proliferation, etc., thereby reducing the effects of various hallmarks of cancer. Components of T. foenum-graecum extracts have been found to be effective in alleviating both solid tumours and blood cancers. The milieu of phytochemicals present in T. foenum-graecum has already been shown to have antimicrobial, antioxidant and neuroprotective properties by several studies done in different parts of the world. The current chapter attempts to have a comprehensive look at the potential of various bioactive principles in Trigonella foenum-graecum to be used for the prevention and treatment of cancer. In today's global scenario, where cancer incidences are alarmingly rising, such natural remedies would indeed go a long way in preventing various types of cancer and imparting a better quality of life. 2021 Nova Science Publishers, Inc. -
Multifaceted Destination Personality Traits: A Short Communication on Understanding from Tourists Perspective
This short communication is an extract from a major research work on destination branding, and this cull out of analysis focused on the multifaceted destination personality traits that the destinations possess and perhaps how such perceptions of tourists differ based on the selected personal factors. Though there are many studies in the destination branding literature, the evidence regarding the personality traits is still at the stage of progression, and approaches referring to multifaceted personality traits are unseen. After the pilot testing, a structured questionnaire was floated to 400 tourists who visited the selected destinations a district in Tamil Nadu, India, between June 2019 and February 2020, where 327 responses were finalized. The questionnaire had statements measuring the destinations personality traits and other questions on tourists characteristics. Combined mean calculation and multivariate results revealed that two personality traits, welcoming and friendly, were emphasized by the tourists and perceived in common. Also, personality traits such as spiritual and charming were found to be commonly perceived. The mean values also indicated the existence of multifaceted destination personality traits some inherent and some perceived. Marketers and others thereof have been recommended on the branding and advertising strategies based on the outcome of this communication. The limitations and scope of this research have been indicated. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Multifarious pigment producing fungi of Western Ghats and their potential
Concerns about the negative impacts of synthetic colorants on both con-sumers and the environment have sparked a surge of interest in natural col-orants. This has boosted the global demand for natural colorants in the food, cosmetics and textile industries. Pigments and colorants derived from plants and microorganisms are currently the principal sources used by mod-ern industry. When compared to the hazardous effects of synthetic dyes on human health, natural colors are quickly degradable and have no negative consequences. In fact, fungal pigments have multidimensional bioactivity spectra too. Western Ghats, a biodiversity hotspot has a lot of unique eco-logical niches known to harbor potential endophytic pigment-producing fungi having enumerable industrial and medical applications. Most of the fungi have coevolved with the plants in a geographical niche and hence the endophytic associations can be thought to bring about many mutually ben-eficial traits. The current review aims to highlight the potential of fungal pigments found in the Western ghats of India depicting various methods of isolation and screening, pigment extraction and uses. There is an urgent need for bioprospecting for the identification and characterization of ex-tremophilic endophytic fungi to meet industry demands and attain sustain-ability and balance in nature, especially from geographic hotspots like the Western Ghats. 2022 Horizon e-Publishing Group. All rights reserved. -
Multifarious Potential of Biopolymer-Producing Bacillus subtilis NJ14 for Plant Growth Promotion and Stress Tolerance in Solanum lycopercicum L. and Cicer arietinum L: A Way Toward Sustainable Agriculture
Diverse practices implementing biopolymer-producing bacteria have been examined in various domains lately. PHAs are among the major biopolymers whose relevance of PHA-producing bacteria in the field of crop improvement is one of the radical unexplored aspects in the field of agriculture. Prolonging shelf life is one serious issue hindering the establishment of biofertilizers. Studies support that PHA can help bacteria survive stressed conditions by providing energy. Therefore, PHA-producing bacteria with Plant Growth-Promoting ability can alter the existing problem of short shelf life in biofertilizers. In the present study, Bacillus subtilis NJ14 was isolated from the soil. It was explored to understand the ability of the strain to produce PHA and augment growth in Solanum lycopersicum and Cicer arietinum. NJ14 strain improved the root and shoot length of both plants significantly. The root and shoot length of S. lycopersicum was increased by 3.49 and 0.41cm, respectively. Similarly, C. arietinum showed a 9.55 and 8.24cm increase in root and shoot length, respectively. The strain also exhibited halotolerant activity (up to 10%), metal tolerance to lead (up to 1000?g/mL) and mercury (up to 100?g/mL), indicating that the NJ14 strain can be an ideal candidate for a potent biofertilizer. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Multifractal analysis of volatility for detection of herding and bubble: Evidence from CNX Nifty HFT
This study delves into the herding and bubble detection in the volatility domain of a capital market underlying. Furthermore, it focuses on creating heuristics, so that common investors find it relatively easy to understand the state of the market volatility. Hence, it can be termed that this study is focused on the specific financial innovation regarding bubble and herding detection coupled with investor awareness. The traces of possible volatility bubble emerge when it is positioned against its own lags (both lag1 and lag2). The volatility trigger indicated clear traces of herding and an embedded parabola function. Continuous and repetitive parabola function hinted at a subtle presence of "fractals". Firstly, the detrended fluctuation analysis has been used with its multifractal variant. Secondly, the regularized form of Hurst calculation and analysis have been used. Both tests reveal the traces of nascent bubble formation owing to prominent herding in CNX Nifty HFT environment. They also indicate a clear link with Hausdorff topological patterns. These patterns would help to create heuristics, enabling investors to be aware of possible bubble and herd situations. Bikramaditya Ghosh, Emira Kozarevic, 2019. -
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).