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Nifty index: Integrating deep learning models for future predictions and investments
The Indian stock market, led by the NSE and BSE, has witnessed remarkable growth, exemplified by the NIFTY 50 index surpassing INR 176 trillion in market capitalization. Post the transformative New Economic Policy reforms in 1991, the market underwent significant expansion due to increased accessibility. This chapter focuses on predicting Nifty index prices for the upcoming 10-day period, aiming to provide valuable insights for investment decisions. Despite the markets inherent complexity, exacerbated by various factors like economic conditions and investor sentiment, the objective of the research study is clear: to boost profitability, mitigate risk, and safeguard traders capital. Leveraging Long Short-Term Memory (LSTM) and Vector Autoregression (VAR) models, the research study rigorously evaluates prediction accuracy using the Root Mean Square Error (RMSE) metric. The study underscores the potential of deep learning techniques in achieving reasonable accuracy, especially for short-term forecasts, while acknowledging the markets inherent unpredictability. Notably, the findings demonstrate that the LSTM model excels in predicting Nifty Bank prices, with an impressive RMSE score of 242.55 compared to VAR models. Furthermore, optimal data splitting, at an 8:2 ratio, significantly enhances prediction accuracy across all models, emphasizing the critical role of high-quality data in training. In conclusion, this study unequivocally recommends LSTM as the preferred model for Nifty index price prediction, providing practitioners with a robust tool to navigate the complexities of the Indian stock market with enhanced precision and confidence. 2025 selection and editorial matter, Vivek S. Sharma, Shubham Mahajan, Anand Nayyar and Amit Kant Pandit; individual chapters, the contributors. -
Nine Level Quadra Boost Inverter with Modified Level Shifted Pulse Width Modulation Technique
This research initiatives to introduce a switched capacitor based nine level boost inverter (SC-9LBI) powered by modified level shifted pulse width modulation (PWM) technique. The SC-9LBI equipped with single DC source along with three capacitors and eight controlled switches to develop nine level inverter output voltage. The suggested inverter configuration has the ability of boosting the inverter input voltage into 1:4 ratio. Also, this research involves modified level shifted PWM technique to enhance the quality of inverter output voltage. The effectiveness of the NLMLI is assessed through parameters such as harmonic distortion, peak voltage, and output voltage root mean square value (rms). Simulation studies have been conducted using MATLAB/Simulink to evaluate the proposed inverter's performance. 2024 IEEE. -
NIR properties of Be stars in star clusters in the Magellanic Clouds
Magellanic Clouds are the nearby galaxies which are ideal to study the properties of metal poor stellar population. In this study, we explore the near-IR properties of optically identified classical Be stars in 19 star clusters in the Magellanic Clouds. From an optically identified sample of 835 Be stars we obtained the J, H, K magnitudes of 389 stars from the IRSF MCPS catalog. Among these, 247 stars (36.4%) are found in 9 clusters in the Large Magellanic Cloud and 142 stars (55.5%) in 10 clusters in the Small Magellanic Cloud. After correcting for reddening, we studied their NIR properties in the (HK)0 vs (JH)0 diagram. We identified 14 stars with abnormally large near IR excesses, which were removed from the analysis, there by restricting our study to 355 classical Be stars. We propose an extended area in the near-IR (H-K)0 vs (J-H)0 diagram as the diagnostic location of Classical Be stars in the Magellanic Clouds. We identified 14 stars to have near-IR excess, higher than those seen in classical Be stars. From the analysis based on spectral energy distribution and luminosity estimate, we found that 8 candidate Be stars may be Herbig Ae/Be stars. We identified a new sample of 6 sgB[e] stars, which when added to the sparse existing sample of 15 sgB[e] stars in the Magellanic Clouds can provide insight to understand the evolutionary link between sgB[e] stars and Luminous Blue variables. 2017 Elsevier B.V. -
Nirod Mukerji
Nirod Mukerji, a notable author in the field of psychology, penned the influential work Psychopharmacology, offering insights into the intersection of psychology and pharmacology. His intellectual contributions extend to Standing at the Crossroads, where he tackles the fundamental challenges of psychosocial integration within the Indian context. Mukerjis scholarly articles, such as Frontiers of Psychopharmacology, Psychology and History, and Modern Science and Technology and Their Impact on Indian Spiritual Values and Traditions, further explore the implications of psychological practices and contemporary advancements on cultural values in India. 2025 selection and editorial matter, Braj Bhushan; individual chapters, the contributors. -
NiSe2@CdO Nanocomposite: A Next-Generation Electrode for Asymmetric Supercapacitors with Gel Electrolyte
This article investigates the electrochemical performance of NiSe2@CdO nanocomposites synthesized by combining melt-diffusion-synthesized NiSe2 and hydrothermally prepared CdO, followed by ball milling to obtain the final NiSe2@CdO composite. Structural, morphological, and electrochemical analyses revealed flake-like NiSe2 nanoparticles decorated with rod-shaped CdO nanostructures, exhibiting exceptional electrochemical performance. The nanocomposite electrode achieved a specific capacitance of 255 F/g at 10 mV/s from the three-electrode setup, and also, was achieved at an energy density of 48 Wh/kg at the power density 2000 W/kg by the NiSe2@CdO||AC asymmetric device. fourier transform infrared analysis confirmed the structural integrity, while transmission electron microscopy images revealed nanostructures with clear lattice fringes, and energy-dispersive X-ray Spectroscopy verified elemental uniformity. The device demonstrated 96.7% capacitance retention even after 5000 cycles and displayed superior energy and power density characteristics in the Ragone plot. These results in turn, highlight the potential of NiSe2@CdO nanocomposites for next-generation energy storage systems. 2025 Wiley-VCH GmbH. -
Nitrogen doped carbon quantum dots@?-Fe2O3/PANI nanocomposite based electrochemical sensor for cadmium ion detection
Cadmium (Cd2+) ions pose significant risks due to their toxic effects even at low concentrations. By integrating nitrogen-doped carbon quantum dots (N-CQDs) with ?-Fe2O3 and polyaniline (PANI), we have engineered a nanocomposite that demonstrates a remarkably high sensitivity and selectivity in detecting Cd2+ ions. The NCQDs@?-Fe2O3/PANI nanocomposite was prepared by using hydrothermal and in-situ polymerization methods. The characterization techniques for an eco-friendly polymer nanocomposite confirm the interactions between N-CQDs, ?-Fe2O3, and PANI. X-ray diffraction analysis shows the nanocomposite possesses a crystal size of 24 0.03 nm. Field-emission scanning electron microscope and high-resolution transmission electron microscope images showed that the spherical N-CQDs are enclosed by irregular ?-Fe2O3 nanoparticles, which are dispersed on the PANI sheets. Further, the particle size distribution analysis indicates an average size of 5.8 nm for the N-CQDs. The electrochemical sensing result suggests that the nanocomposite is effective in sensing the Cd2+ ions with a detection limit of 750 nm. 2025 -
Nitrogen-doped carbonized polymer dots (CPDs) and their optical and antibacterial characteristics: A short review
Substantial advancements in the field of Carbon Dots (CDs) and their derivatives in recent years can be accredited to their tunable properties. Recently Carbonized Polymer Dots (CPDs) are the emerging form in the CDs family, which possesses a typical polymer/Carbon hybrid structure and properties due to its incomplete carbonization. Alteration of various parameters during the synthesis process suggested that the properties of CPDs depend on temperature and pH. It was found that doping of CPDs using nitrogen enhanced its optical properties, thereby being used as biomarkers. CPDs generally hold a strong green and blue emission, while intense red luminescence was observed doping with nitrogen. Photoluminescence Quantum Yield (PLQY) was also found to increase with the increase in doping and temperature. Doped CPDs find several applications, including bio-imaging, LEDs, etc. In this review, we focus on analyzing the increase in efficiency of CPDs with the process of doping considering optical and antibacterial applications. 2021 by the authors. -
Nitrogen-Oxygen Co-Functionalized Waste Cassava Peel-Derived Carbon Dots for White Led
White light emitting diodes (WLEDs)are most sought after, with the broad spectra ranging from cool to warm white light being skillfully utilized to create various modes of lighting effects. The fabrication of WLEDs is generally sophisticated, involving either multiple components emitting in different regions or single-component phosphors with complex elemental compositions. In the present work, WLEDs utilizing solvent-tuned carbon dots derived from waste cassava peel are reported through a facile one-step microwave-assisted solvothermal method. The carbon dots show evident UV absorption and correspondingly emit broad visible light spectra when dispersed in a dimethylformamide (DMF)-polyvinyl alcohol (PVA)blend, making themselves suitable white lightemitting down conversion materials. The successful transformation of a 400nm UV LED into a WLED with a general colour rendering index (CRI) of 83 and colour correlation temperature (CCT) of 4426 K gives a promising future outlook toward developing eco and economic-friendly WLEDs. 2025 Wiley-VCH GmbH. -
Nitrogen-Oxygen Co-Functionalized Waste Cassava Peel-Derived Carbon Dots for White Led
White light emitting diodes (WLEDs)are most sought after, with the broad spectra ranging from cool to warm white light being skillfully utilized to create various modes of lighting effects. The fabrication of WLEDs is generally sophisticated, involving either multiple components emitting in different regions or single-component phosphors with complex elemental compositions. In the present work, WLEDs utilizing solvent-tuned carbon dots derived from waste cassava peel are reported through a facile one-step microwave-assisted solvothermal method. The carbon dots show evident UV absorption and correspondingly emit broad visible light spectra when dispersed in a dimethylformamide (DMF)-polyvinyl alcohol (PVA)blend, making themselves suitable white lightemitting down conversion materials. The successful transformation of a 400nm UV LED into a WLED with a general colour rendering index (CRI) of 83 and colour correlation temperature (CCT) of 4426 K gives a promising future outlook toward developing eco and economic-friendly WLEDs. 2025 Wiley-VCH GmbH. -
Nitrogen-rich dual linker MOF catalyst for room temperature fixation of CO2 via cyclic carbonate synthesis: DFT assisted mechanistic study
The benign synthesis of a novel Zn based Lewis acid-base bifunctional metal-organic framework (ITH-1) and its room temperature catalytic ability for the chemical fixation of carbon dioxide via cyclic carbonate synthesis is reported herein. ITH-1 is characterized by the presence of mono coordinated pendant imidazole groups throughout the framework inducing Lewis basicity. The synthesized material is crystallized in the monoclinic space group as revealed by the Single Crystal X-ray Diffraction Analysis and possesses a 2 D non-planar interdigitated network wherein the neighbouring sheets are connected via strong hydrogen bonding (1.947 . ITH-1 was characterized thoroughly via various physicochemical analyses such as XRD, FT-IR, Raman, FE-SEM, CHN, ICP, TGA and was found thermally stable up to 300 ?C. The co-existence of accessible and active Lewis acid (Zn) Lewis base (imidazole) moieties rendered ITH-1 the potential to catalyse the cycloaddition of CO2 with propylene oxide under solvent and co-catalyst free conditions (~95% conversion) at moderate temperatures with remarkable reusable performance (over 5 times). ITH-1 manifested excellent CO2 conversion even under room temperature and 1 bar pressure in the presence of a co-catalyst. Density Functional Theory (DFT) calculations utilizing M06 functional were exercised to envisage the mechanism behind the successful CO2 conversion by ITH-1 at room temperature and were found to be in clear agreement with the experimental results. 2022 Elsevier Ltd -
NLP and Topic Modeling in Healthcare: Identifying Diseases from Patient Histories
Topic modeling and Natural Language Processing (NLP) have demonstrated significant prospects in the healthcare industry for extracting insightful information from unstructured patient histories that can help diagnose diseases and enhance clinical decisions. In this study, patient histories are grouped into ten different clusters using advanced K-Means clustering, with the Dunn Index being used to validate the clustering performance. After the clusters are formed, each cluster is subjected to topic modeling approaches. Four topic modeling approaches are examined in this study, Latent Dirichlet Allocation (LDA), Hierarchical Dirichlet Process (HDP), Latent Semantic Indexing (LSI), and Non-negative Matrix Factorization (NMF). These techniques are used to find disease-related terms from patient histories. Coherence scores, which show the semantic significance of the terms produced, and execution times, which show the computational efficiency needed for real-time healthcare applications, are used to evaluate the models. According to experimental findings forthe USMLE Step 2 Clinical Skills exam dataset, NMF and HDP generated the most cohesive terms, with NMFs faster execution time (1.67s) making it appropriate for widespread healthcare applications. Whereas, a reasonable balance between coherence and computational demands is offered by LDA and LSI. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
NLP-based Health Care- Hospital Recommendation Systems with Online Text Reviews by Patients Satisfaction
Recent times, these recommendations based on reviews play a vital role in the service industry. The hospital is assessing its quality of service using these surveys or studies posted in online forums. The ongoing pandemic also played a vital role in making the online review more popular. These statistical data and visualization are informative in representing the views of patient satisfaction towards health service. As the size of data is large and it is of varied size and format it is difficult to get consolidated results. The users share their emotions and feelings through this review. So, it is a challenge to assess the emotions of the patients. Sentiment analysis using machine learning makes our work easy in evaluating the scores visually. The reviews are analyzed using natural language processing (NLP), and the sentiment of the studies is analysed as positive, negative, and neutral using polarity ranking, which in turn is converted as the recommendation system based on patient reviews. This paper aims to propose a new method of recommending the hospital based on the sentiment of the previous user review. The thought of the user is collected from the various hospitals. The proposed (Healthcare Recommendation System) HRS system has nearly 0.5 mean absolute error, which states that the proposed HRS system is significantly effective. 2023 IEEE. -
NLP-based personal learning assistant for school education
Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slack-based UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chat-bot's NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future. 2021 Institute of Advanced Engineering and Science. All rights reserved. -
NN-SVM: a hybrid neural networksupport vector machine framework for accurate pneumonia detection from chest X-rays
We present neural network (NN)-support vector machine (SVM), hybrid NN-SVM framework for three-class pneumonia detection (normal, bacterial, and viral) from chest X-rays (CXRs). Pretrained NN backbone is fine-tuned for radiographic textures; global average pooling (GAP) yields embeddings that feed calibrated radial basis function (RBF)-SVM. Standardized preprocessing (resize, normalization) and class-aware augmentation are applied. We report accuracy, precision, recall, F1-score, area under the curve (AUC), confusion matrices, and per-class receiver operating characteristic (ROC). Statistical significance is assessed via DeLong (AUC), McNemar (accuracy), and paired bootstrap (F1-score). Gradient-weighted class activation mapping (grad-CAM) supports interpretability; external validation and domain adaptation (batch normalization re-estimation and temperature scaling) assess robustness. NN-SVM attains 97.46% accuracy with strong macro-F1 and AUC. Compared with SoftMax head, SVM improves margin separation and calibration. We present NN-SVM, hybrid deep learning approach that combines transfer-learned convolutional neural networks (CNNs) with SVM classifier to automatically diagnose pneumonia from CXRs into three clinically relevant categories: viral pneumonia, bacterial pneumonia, and normal. We use pre-trained CNN to extract robust image embeddings after standardized preprocessing (resizing and normalization) and train RBF-kernel SVM on resulting features. Performance is evaluated with accuracy, precision, recall, F1-score, and confusion matrices. On labeled CXR dataset, NN-SVM achieves 97.46% accuracy, demonstrating strong diagnostic capability that can reduce radiologist burden and support timely clinical decision-making. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/ -
No right is absolute: the need for a more responsible use of social media
[No abstract available] -
Node Overlapping Detection for Draggable Node-Based Applications
Node-based interfaces are user interfaces that are based on the concept of nodes, which represent individual units of functionality, and edges, which represent the connections between nodes. In a node-based interface, nodes are connected by edges to form a graph, which represents the data flow and relationships between different parts of the system. The Node overlapping detection technique is only for react flow version 11 and higher. Users having previous versions are not able to use that functionality. To detect the overlapping, based on the output of this library, several user-defined functions can be used to resolve to overlap. It will see the single-pixel overlap. Using this library, users can avoid Node and edge overlapping by creating custom edges. It is a simple JavaScript function currently used for reactjs. In the future, if any other script develops a draggable node-based flowsheet-creating feature, the user can use this library accordingly. 2023 IEEE. -
Noise removal feature enhancement and speech recognition techniques for artificial larynx transducer speech
Speech impediments are the state of difficulty for a person to speak comfortably. These impediments make the spoken speech distorted and they are generally categorized as disordered speech. The quality of disordered speech is poor as clarity, intelligibility and naturalness is missing. In most type of disordered speech the voice is natural and produced by the vocal system of the human being. The vocal system includes the organ called as Larynx placed in the upper part of the neck. This organ has the vocal folds that contribute for pitch variation and volume of the speech. This organ will be malfunctioning some time or will be removed because of cancer. In both the case in order to restore speech, an external device called Artificial Larynx Transducer (ALT) is used to produce the sound. It is a small handheld battery operated device and is used for decades to obtain the audible speech for people who lost their speech because of removal of larynx. The quality of speech and its intelligibility of AL speakers have not improved for decades. The reason for poor quality is constant vibration of ALT, direct sound from ALT and pressure offered to produce the vibration. newlineSo in this research the nature of the speech produced from ALT is analyzed, a possible enhancement of the parameter is done and a recognition technique of the spoken word with the help of trained data is done. Here the approach followed to tackle the problem of poor quality in AL speech involves both speech enhancement and recognizer technique development. When it is looked as enhancement problem noise region localization, noise estimation and noise suppression methods were adopted. In the process of parameter enhancement, pitch frequency estimation and improvement is implemented. When it is looked as recognition problem the parameters pitch frequency, formant frequency, glottal excitation, spectral tilt, coefficients are extracted. As formant frequency is a sensitive parameter, its estimation was done using Recurrent Neural network. -
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Non invasive methods of blood glucose measurement: Survey, challenges, scope
Noninvasive body parameters monitoring and disease detection is one of the emerging research area now a days. In this paper a review on Non-invasive methods of blood glucose measurement has been made. A comparative study has been made which describes the methodology incorporated in the published literatures, research challenges and the used tools. This paper also describes about the factors which highly impacts the non-invasive measurement. Finally, a deep learning based noninvasive measurement method compatible with IOT is mentioned. This paper serves as a proper reference for future researchers working in non-invasive blood glucose measurement domain in selecting appropriate non-invasive method algorithm for glucose monitoring non-invasively. 2019 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth. -
Non linear thermal radiation effect on Williamson fluid with particle-liquid suspension past a stretching surface
A mathematical analysis of two-phase boundary layer flow and heat transfer of a Williamson fluid with fluid particle suspension over a stretching sheet has been carried out in this paper. The region of temperature jump and nonlinear thermal radiation is considered in the energy transfer process. The principal equations of boundary layer flow and temperature transmission are reformed to a set of non-linear ordinary differential equations under suitable similarity transformations. The transfigured equalities are solved numerically with the help of RKF-45 order method. The effect of influencing parameters on velocity and temperature transfer of fluid is examined and deliberated by plotted graphs and tabulated values. Significances of the mass concentration of dust particle parameter play a key role in controlling flow and thermal behavior of non-Newtonian fluids. Further, the temperature and concern boundary layer girth are declines for increasing values of Williamson parameter. 2017 The Authors