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A Multifaceted Approach at Discerning Redditors Feelings Towards ChatGPT
Generative AI platforms like ChatGPT have leapfrogged in terms of technological advancements. Traditional methods of scrutiny are not enough for assessing their technological efficacy. Understanding public sentiment and feelings towards ChatGPT is crucial for pre-empting the technologys longevity and impact while also providing a silhouette of human psychology. Social media platforms have seen tremendous growth in recent years, resulting in a surge of user-generated content. Among these platforms, Reddit stands out as a forum for users to engage in discussions on various topics, including Generative Artificial Intelligence (GAI) and chatbots. Traditional pedagogy for social media sentiment analysis and opinion mining are time consuming and resource heavy, while lacking representation. This paper provides a novice multifrontal approach that utilises and integrates various techniques for better results. The data collection and preparation are done through the Reddit API in tandem with multi-stage weighted and stratified sampling. NLP (Natural Language processing) techniques encompassing LDA (Latent Dirichlet Allocation), Topic modelling, STM (Structured Topic Modelling), sentiment analysis and emotional analysis using RoBERTa are deployed for opinion mining. To verify, substantiate and scrutinise all variables in the dataset, multiple hypothesises are tested using ANOVA, T-tests, KruskalWallis test, Chi-Square Test and MannWhitney U test. The study provides a novel contribution to the growing literature on social media sentiment analysis and has significant new implications for discerning user experience and engagement with AI chatbots like ChatGPT. 2024 Padarha et al., licensed to EAI. -
A multi-Threshold triggering and QoS aware vertical handover management in heterogeneous wireless networks
Vertical handover management provides seamless connectivity in heterogeneous wireless networks. But still there are different challenges that need to be addressed. These challenges include the inappropriate network selection, wrong cell handover, etc. Therefore, in this article, we proposed a handover management scheme based on the data rate and QoS of available networks. The handover triggering is performed on the data rate requires by different applications. Similarly, the network selection is performed by considering the cost, data rate of available networks and energy consumption by the mobile interface. The proposed scheme is simulated in different mobility scenarios with a random number of applications running on various numbers of mobile nodes. The simulation results show that the proposed scheme requires less energy during the scanning and selection of available networks. 2015 IEEE. -
A Multi-Stimuli responsive organic luminogen with aggregation induced emission for the selective detection of Zn2+ ions in solution and solid state
Organic luminogens capable of excited state intramolecular electron transfer (ESIPT) have drawn prodigious attraction due to their enhanced emission in solid-state. A novel Schiff base molecule, 3,5-dibromo-2-hydroxybenzylidenenicotinohydrazide (DHN) exhibited stimuli-induced reversible fluorescence switching and selective binding propensity towards zinc in aqueous media, and the concentration-dependent studies showed a limit of detection of 9.135 nM. DHN was found to be weakly fluorescent in polar solvents with a quantum yield ranging between 0.0365 and 0.0789 but exhibited a very strong fluorescence in solid state (?exc = 370 nm) due to aggregation induced emission (AIE). The ESIPT fluorophore renders significant reversible halochromic properties in solution and solid-state. In addition, utilizing the solid-state fluorescence, we have prepared PVA-probe green-emitting composite films, which can be used for the on-site detection of Zn2+ in aqueous media. The practical applicability of DHN was proven by detecting Zn2+ in real drug samples. Finally, the ESIPT fluorophore was used for fluorescent imaging of intracellular zinc in the cells acquired from the nervous tissue of rats (N2a). The investigations carried out highlight the versatility of ESIPT Schiff bases used for the development of multi-responsive fluorescent materials for selective sensing of metal ions in both solid and solution states. 2022 Elsevier B.V. -
A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification
In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK +), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and ManMachine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
A multi-preference integrated algorithm for deep learning based recommender framework
Nowadays, the online recommender systems based collaborative filtering methods are widely employed to model long term user preferences (LTUP). The deep learning methods, like recurrent neural networks (RNN) have the potential to model short-term user preferences (STUP). There is no dynamic integration of these two models in the existing recommender systems. Therefore, in this article, a multi-preference integrated algorithm (MPIA) for deep learning based recommender framework (DLRF) is proposed to perform the dynamic integration of these two models. Moreover, the MPIA addresses improper data and to improve the performance for creating recommendations. This algorithm is depending on an enhanced long short term memory (LSTM) with additional controllers to consider relative information. Here, experiments are carried out by Amazon benchmark datasets, then obtained outcomes are compared with other existing recommender systems. From the comparison, the experimental outcomes show that the proposed MPIA outperforms existing systems under performance metrics, like area under curve, F1-score. Consequently, the MPIA can be integrated with real time recommender systems. 2022 John Wiley & Sons, Ltd. -
A MULTI-OBJECTIVE HUNTER-PREY OPTIMIZATION FOR OPTIMAL INTEGRATION OF CAPACITOR BANKS AND PHOTOVOLTAIC DISTRIBUTION GENERATION UNITS IN RADIAL DISTRIBUTION SYSTEMS
This article put forward the determination of the optimal siting and sizing of capacitor banks and PV-DG (Photo-Voltaic Distribution Generation) units in a radial distribution system. A modern population-based optimization algorithm, Hunter-Prey Optimization (HPO), is applied to determine the optimal capacitor bank and PV-DG placement. This algorithm, HPO, got its motivation from the trapping behaviour of the carnivore (predator/hunter) like lions and wolves towards their target animal like deer. The typical IEEE-33 & 69 test bus systems are scrutinized for validating the effectiveness of the suggested algorithm using MATLAB software R2021b version. The acquired results are collated with the existing heuristic algorithms for the active power loss criterion. The nominal or base values for system losses and voltage profile were considered for the comparison, with the results from HPO. The HPO application has an efficient performance in figuring out the most favourable location and capacity of the capacitor banks and PV DGs compared with the other techniques. 2023 by authors and Galileo Institute of Technology and Education of the Amazon (ITEGAM). -
A multi-model unified disease diagnosis framework for cyber healthcare using IoMT-cloud computing networks
The past several decades of research into machine learning have been of great assistance to humanity in the diagnosis of a variety of ailments using various forms of automated diagnostic procedures. Machine learning, combined with smart health devices, has improved health monitoring, timely diagnoses, and treatment. This paper introduces a unified disease diagnosis framework, integrating cloud computing, machine learning, and IoT. The framework has three layers: physical (collects patient data), fog (intermediate layer with a domain identification unit to determine input and diagnosis type), and transmission (cloud server with a disease detection unit). The performance evaluation shows the robustness and efficiency of the model as compared to state-of-art models. 2023, Taru Publications. All rights reserved. -
A Multi-Modal Approach to Digital Document Stream Segmentation for Title Insurance Domain
In the twenty-first century, storing and managing digital documents has become commonplace for all corporate and public sectors around the world. Physical documents are scanned in batches and stored in a digital archive as a heterogeneous document stream, referred to as a digital package. To make Robotic Process Automation (RPA) easier, it's necessary to automatically segment the document stream into a subset of independent, coherent multi-page documents by detecting the appropriate document boundary. It's a common requirement of a TI company's Automated Document Management Systems (ADMS), where business operations are automated using RPA and the goal is to extract information from digital documents with minimal user intervention. The current study proposes, evaluates, and compares a multi-modal binary classification network incorporating text and picture aspects of digital document pages to state-of-the-art baseline methodologies. Image and textual features are extracted simultaneously from the input document image by passing them through Visual Geometry Group 16 - Convolutional Neural Network (VGG16-CNN) and pre-trained Bidirectional Encoder Representations from Transformers (Legal-BERT {}_{base} ) model through transfer learning respectively. Both features are finally fused and passed through a fully connected layer of Multi Layered Perceptron (MLP) to obtain the binary classification of the pages as the First Page (FP) and Other Page (OP). Real-time document image streams from production business process archive were obtained from a reputed Title Insurance (TI) company for the study. The obtained F_{1} score of 97.37% and 97.15% are significantly higher than the accuracies of the considered two baseline models and well above the expected Straight Through Pass (STP) threshold defined by the process admin. 2013 IEEE. -
A multi-layer memory enriched staticmemory system /
Patent Number: 202141039100, Applicant: Debarka Mukhopadhyay.The present invention is configured with the generation of multiprocessor core of each 15 bit CAM cell and a section works at a lower supply area which will drive a part to work at a more prominent induced voltage for the CAM core processor. The present device is configured in such a way so that no level converters are required. The system operates avoiding the power overhead and low supply area as illustrated in Fig1. This process is used assorting voltage with less area interface inside the CAM cells, related by the applying product on D-FF. The match line indicates the search word and stored word are indistinguishable (the match case) or are extraordinary (a confounding case, or miss) of the CAM cell. -
A multi-layer memory enriched staticmemory system /
Patent Number: 202141039100, Applicant: Debarka Mukhopadhyay.
The present invention is configured with the generation of multiprocessor core of each 15 bit CAM cell and a section works at a lower supply area which will drive a part to work at a more prominent induced voltage for the CAM core processor. The present device is configured in such a way so that no level converters are required. The system operates avoiding the power overhead and low supply area as illustrated in Fig1. This process is used assorting voltage with less area interface inside the CAM cells, related by the applying product on D-FF. The match line indicates the search word and stored word are indistinguishable (the match case) or are extraordinary (a confounding case, or miss) of the CAM cell. -
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A Multi Objective Artificial Eco-System Based Optimization Technique Integrating Solar Photovoltaic System In Distribution Network
Agricultural sector contributes 6.4% of total economic generation across the world. Notably, the utilization of technology to improve the yield and economy is rapidly increasing. To provide continuous supply to the residential customers, the agricultural feeder grid-dependency has to be integrated with Solar Photo Voltaic (SPV) systems. In this paper, an Artificial Eco-System based Optimization (AEO) algorithm is proposed for simultaneously identifying the locations and quantifying the sizes of SPV systems. A practical distribution system feeder 'Racheruvu 11kV agricultural feeder' Andhra Pradesh, India is considered for simulation purpose and the performance is compared with the standard IEEE-33 radial distribution system. 2022 IEEE. -
A molecular QCA based UV lamp for water purification /
Patent Number: 201731011405, Applicant: Dr.Paramartha Dutta. -
A molecular docking study of SARS-CoV-2 main protease against phytochemicals of Boerhavia diffusa Linn. for novel COVID-19 drug discovery
SARS-CoV-2, the causative virus of the Corona virus disease that was first recorded in 2019 (COVID-19), has already affected over 110 million people across the world with no clear targeted drug therapy that can be efficiently administered to the wide spread victims. This study tries to discover a novel potential inhibitor to the main protease of the virus, by computer aided drug discovery where various major active phytochemicals of the plant Boerhavia diffusa Linn. namely 2-3-4 beta-Ecdysone, Bioquercetin, Biorobin, Boeravinone J, Boerhavisterol, kaempferol, Liriodendrin, quercetin and trans-caftaric acid were docked to SAR-CoV-2 Main Protease using Molecular docking server. The ligands that showed the least binding energy were Biorobin with ? 8.17kcal/mol, Bioquercetin with ? 7.97kcal/mol and Boerhavisterol with ? 6.77kcal/mol. These binding energies were found to be favorable for an efficient docking and resultant inhibition of the viral main protease. The graphical illustrations and visualizations of the docking were obtained along with inhibition constant, intermolecular energy (total and degenerate), interaction surfaces and HB Plot for all the successfully docked conditions of all the 9 ligands mentioned. Additionally the druglikeness of the top 3 hits namely Bioquercetin, Biorobin and Boeravisterol were tested by ADME studies and Boeravisterol was found to be a suitable candidate obeying the Lipinskys rule. Since the main protease of SARS has been reported to possess structural similarity with the main protease of MERS, comparative docking of these ligands were also carried out on the MERS Mpro, however the binding energies for this target was found to be unfavorable for spontaneous binding. From these results, it was concluded that Boerhavia diffusa possess potential therapeutic properties against COVID-19. 2021, Indian Virological Society. -
A Modified Seven-Level Inverter with Inverted Sine Wave Carrier for PWM Control
The conventional multilevel inverter necessitates more active switching devices and high dc-link voltages. To minimalize the employment of switching devices and dc-link voltages, a novel topology has been proposed. In this paper, a novel minimum switch multilevel inverter is established using six switches and two dc-link voltages in the proportion of 1: 2. In addition, the proposed topology is proficient in making seven-level voltages by appropriate gate signals. The PWM signals were produced using several inverted sine carriers and a single trapezoidal reference. When compared to other existing inverters, this configuration needs fewer components, as well as fewer gate drives. Furthermore, this module can generate a negative level without the use of a supplementary circuit such as an H-Bridge. As a result, overall cost and complexity are greatly reduced. The proposed minimum switch multilevel inverter operation is validated through simulations followed by experimental results of a prototype. 2022 Arun Vijayakumar et al. -
A modified invasive weed optimization for MPPT of PV based water pumping system driven by induction motor
A novel approach called Modified Invasive Weed Optimization (MIWO) technique has been developed and combined with the Perturb and Observes (P&O) algorithm to enhance the extraction of maximum power from photovoltaic (PV) panels in the presence of partial shading conditions. The conventional P&O algorithm falls short in extracting the maximum power from PV systems under partial shading conditions due to the existence of multiple maximum points. In such scenarios, optimization techniques can be employed to search for the global maximum point. The proposed MIWO-based P&O algorithm updates the reference voltage to ensure that the PV system operates at the Maximum Power Point (MPP) based on the prevailing weather conditions. This MIWO based PV system is further fed to water pumping system. A PV-based water pumping system is utilized for both irrigation and domestic purposes. Additionally, a sensorless vector control-based induction motor is employed in this study to drive the pump. The objective of this research is to demonstrate the achievement of an efficient PV-based water pumping system without the need for battery storage. Various results based on MIWO are compared with PSO and GWO. The results are presented based on various water pumping applications and the availability of solar irradiance during rapid climate changes. MATLAB/Simulink simulations, along with hardware-based experiments, are provided to validate the effectiveness of the proposed method under both transient and steady-state conditions. 2024 IOP Publishing Ltd. -
A modified fuzzy approach to prioritize project activities
Project management is an important task in business although project is not just confined to business. Due to the uncertainty of the various variables involved in a project, over several past decades research is going on in the search for an efficient project management model. Although numerous crisp models are easily implementable, the potential of fuzzy models are huge. In the case of software development, the variables involved are highly dynamic. In this paper, we propose a ranking based fuzzy model that can prioritize various activities. We use a popular crisp model to test the effectiveness of the fuzzy model proposed. Simulation is done through Java Server Pages (JSP). There is considerable computational and managerial advantage in implementing the fuzzy model. 2018 Authors. -
A modified approach for extraction and association of triplets
In this paper we present an enhanced algorithm with modified approach to extricate various Triplets i.e. subject-predicate-object from Natural language sentences. The Treebank Structure and the Typed Dependencies obtained from Stanford Parser are used to elicit multiple triplets from English Sentences. Typed Dependencies represents grammatical connections among the words of any sentence and represents how triplets are associated. The intended interpretation behind the extraction of Triplets is that the subject is acting on the object in a way described by the predicate. In graphical form it can be considered that subject and object will be acting as nodes i.e. entities and predicate as edges i.e. relationship. The resulting triplets and relations can be useful for building and analysis of a social network graph and for generating communication pattern and Information retrieval. 2015 IEEE. -
A modern approach of swarm intelligence analysis in big data: Methods, tools, and applications
Swarm intelligence is one of the most modern and less discovered artificial intelligence types. Until now it has been proven that the most comprehensive method to solve complex problems is using behaviours of swarms. Big data analysis plays a beneficial role in decision making, education domain, innovations, and healthcare in this digitally growing world. To synchronize and make decisions by analysing such a big amount of data may not be possible by the traditional methods. Traditional model-based methods may fail because of problem varieties such as volume, dynamic changes, noise, and so forth. Because of the above varieties, the traditional data processing approach will become inefficient. On the basis of the combination of swarm intelligence and data mining techniques, we can have better understanding of big data analytics, so utilizing swarm intelligence to analyse big data will give massive results. By utilizing existing information about this domain, more efficient algorithm can be designed to solve real-life problems. 2023, IGI Global. All rights reserved. -
A Model to Predict the Influence of Inconsistencies in Thermal Barrier Coating (TBC) Thicknesses in Pistons of IC Engines
LHR (Low heat Rejection) engines comprise of components that are modified with ceramic Thermal Barrier Coatings (TBC) to derive improvements in performance, fuel efficiency, combustion characteristics and life. In addition to engine parameters, the ability of TBCs (250 - 300?m thick) to function favorably depends on materials technology related factors such as surface-connected porosity, coating surface roughness, uniformity and consistency in coating thickness [1]. Right since the nineties, emphasis has been placed on the complexity of piston contours from a coating processing standpoint because the piston bowl geometry although appears simple, is actually quite complex. Robotic plasma gun manipulation programs have been developed to obtain uniform coating properties and thicknesses which are highly classified information. Thicker coatings offer better thermal insulation characteristics but in thickness deficient regions, TBCs may be as thin as ?30 microns. Applied via the 'line of sight' process, in the Atmospheric Plasma Spray System the coating thickness does not get developed adequately if the components comprise of contours with shadow regions. Thus the coating quality of a LHR engine heavily depends upon the shape of the engine components. This affects the barrier effects offered by the TBC and is reflected via generation of unwanted thermal gradients in the combustion chamber and on the external piston walls that adversely influence the engine performance. Extensive diesel engine cycle simulation and finite-element analysis of the coatings have been conducted to understand their effects on (a) diesel engine performance and (b) stress state in the coating and underlying metal substructure. Research work presented here involves the need and developmental efforts made via Computational Fluid Dynamics (CFD) to generate a model via ANSYS - Fluent simulation software that predicts the temperature gradient across TBCs of various ceramics and coating thicknesses. The geometric model was developed using the dimensions obtained using a CMM (Coordinate Measuring Machine) in Solidworks and the mesh was developed in Altair Hypermesh. The generated mesh consists of 221938 elements. Interfaces were created between the piston-bond coat-top coat surfaces. The Ansys-FLUENT CFD code solves the energy equation to find out the temperature drop in the piston for different combustion temperatures. Although most of the cavities presented are not rectangular, incompressible and steady laminar flow was assumed. The Semi-Implicit Method for Pressure-linked Equations (SIMPLE) was used to model the interaction between pressure and velocity. The energy variables were solved using the second order upwind scheme. In addition, the CFD program uses the Standard scheme to find the pressure values at the cell faces. Convergence was determined by checking the scaled residuals and ensuring that they were less than 10-6 for all variables. Two cases with combustion temperatures varying between 700 and 800 K were developed in Ansys FLUENT, wherein the thickness was deficient in the 'shadow' region. The model was validated via experimentation involving thermal shock cycle tests in prototype burner rig facility and measuring the temperature drop across the TBC as well. Non uniform coatings, leading to non-uniform drop in temperature across the thickness are most likely to affect the lubrication system of the engine and therefore the performance. Substantial efforts must be directed towards development of consistent and uniformly thick coatings for optimum performance of the LHR engine. 2017 Elsevier Ltd. All rights reserved.