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A Comparative Study of Sentiment and Emotional Analysis of Parallel Corpora (Manipuri-English)
(National Institute of Technology, Silchar, 2026) Devi, Thoudam Siyabeti
A Control Scheme to Mitigate Voltage Unbalance in an Islanded Microgrid using Thermostatically Controlled Loads
(Department of Electrical Engineering, 2022) Pandey, Rupesh Kumar
With increase in requirement of electrical energy in day to day there is an obvious increase in generation but as per the fact that India is very much reliant on thermal power plants for its electricity generation and the raw input to plant is coal which is available but it is limited in amount. The balance between generation and demand needs to be maintained at all time. So, the solution to this problem is use of renewable source of energy like solar, wind etc. There are various advantages of using renewable energy for generating electric energy some of which are: green energy, low cost of installation, etc. The incorporation of renewable energy into conventional grid is possible with distributed generation and microgrid. Microgrid is basically a small-scale power system which is designed to meet power demand for small area. Another probable solution for marinating the proper balance between demand and generation is by providing the control over number of loads connected. This control of load is possible with the idea of Demand side management (DSM). There are various advantages of microgrid like flexibility, reliability is increased. Apart from various advantages of microgrid there are few challenges also that comes with microgrid installation. Some of which are: cyber-attacks, maintain good power quality. Maintaining good power quality in power system is very important, since they become one of the main reasons for proper operation of power system. A grid operating normally should have voltage and frequency well within defined standard limits. This problem of voltage unbalance is more pronounced in islanded microgrid (stand-alone microgrid). In this work a control and mitigation scheme is used which only activates during peak loading hours and by using the use of thermostatically controlled to implement this control scheme the problem of voltage unbalance in the system can be reduced. The used model of thermostatically controlled load is different from conventional model of thermostatically controlled load model as in conventional model reactive power consumption is ignored. During the implementation of this control scheme to plan demand side management and to reduce voltage unbalance in the system, the customer’s thermal comfort is maintained. The source of standalone microgrid is PV array operating to deliver maximum power using P&O algorithm. The system under testing is designed and tested in MATLAB simulink environment.
A Hybrid Model for Analysis and Detection of Covid 19 on X Ray Images using Machine Learning and Deep learning Techniques
(Department of Computer Science & Engineering, NITS, 2022) Varma, Vivek
Covid-19 has presented itself as a deadly infection that can affect all sorts of living beings. The day-to-day life of humans, in all of the countries are getting hindered due to this rapidly spreading viral problem. A clinically based examination of COVID-19 affected persons revealed that most of these people had a respiratory based infection after coming into touch with this disease. Chest based x-ray and chest Computer Tomography are better effective imaging techniques for finding respiratory problems. Nonetheless, producing a x-ray is a lower cost procedure than a CT scan. Deep learning is a very successful machine learning procedure, providing valuable perspective to examine a large quantity of x-ray images, which may provide a significant impact on Coronavirus screening. This work takes x-ray images of chest from corona infected and healthy people. The images are balanced and then fed into convolutional neural network models and their performance is analysed. The accuracy of ResNeXt, Xception and InceptionV3 is checked. 15,135 x-ray scans of chest were taken as the dataset which is collected from multiple certified sources, from this 12,121 are used in training and 3032 are used in validation. The Xception model provides more accuracy (i.e., 97%) on detecting covid in the images outperforming other pre-trained models. Furthermore a concatenated model consisting of all the 3 above stated models was implemented and trained on the same data set which provided the highest overall accuracy of 98%. A second instance of the same concatenated model was created and trained on a new dataset collected from Brixia project consisting of only covid x-rays divided into 18 classes on the basis of their severity. This model was then used to assign a severity score on the covid x-ray scans providing 92% accuracy. Thus the final model being proposed is able to take the x-rays and classify them into three categories (Pneumonia, Normal, Covid) and assign a severity score to it in case of detection of Covid.
A Low-Power Analog Front End Circuits for Portable Bio-Medical Devices
(National Institute of Technology, Silchar, 2026) Prasad, Ronanki Sai
A mathematical model on EMHD peristaltic flow of UO2 nanofluid with microbes through a porous conduit: Application to flows in esophagus
(Department of Mathematics, NITS, 2025) Mannan, Abdul
An analytical study is presented on the bioconvective peristaltic flow of a Newtonian nanofluid within a porous, symmetric channel featuring flexible walls. The nanofluid behavior is examined under the influence of externally applied electric and magnetic fields. The model incorporates effects from motile microorganisms. The electroosmotic flow is described using the Nernst–Planck and Poisson equations. To simplify the mathematical formulation, assumptions of long wavelength and low Reynolds number are employed. The Homotopy Perturbation Method (HPM), along with the lubrication approximation, is utilized to derive analytical solutions for the reduced, non-dimensional governing equations. Graphical analysis is used to explore the impacts of various physical parameters on axial velocity, temperature, nanoparticle concentration, and microorganism density. It is observed that increasing buoyancy forces promote increased axial velocity. The Hartmann number and Joule heating parameter contribute to temperature elevation by inhibiting convective heat loss and producing internal heat. Furthermore, higher thermophoresis effects lead to greater nanoparticle accumulation, an effect magnified by Brownian motion. Increased Peclet numbers and bioconvection parameters are found to diminish microorganism density due to restricted diffusion and intensified convective disturbances. we believe that the outcomes of this study have potential applications in several areas, including bioengineering, bioreactors, enzyme processing, solar thermal systems, and energy storage technologies.
A mathematical study on the impact of media awareness during the COVID-19 pandemic
(Department of Mathematics, NITS, 2025) Kumar, Sarvesh
This study introduces a novel SEQIR mathematical model in response to the global challenges posed by COVID-19, which is recognized as one of the most contagious and potentially fatal diseases worldwide, with reinfection further exacerbating its severity. The model is designed to examine the transmission dynamics of COVID-19, evaluate behavioural changes influenced by media-induced public awareness through social media platforms, and formulate effective control strategies. We deduce the disease-free and conditional multiple endemic equilibria, and determine the basic reproduction number using the next-generation matrix approach. The local stability of these equilibria is investigated with the help of the Routh–Hurwitz criterion. Furthermore, we establish global asymptotic stability by constructing appropriate Lyapunov function. A sensitivity analysis is conducted to identify the key parameters that significantly influence the basic reproduction number and future dynamics. The model further demonstrates the presence of backward bifurcation under specific parametric conditions. Finally, numerical simulations are presented to validate the effectiveness of different intervention strategies aimed at reducing COVID-19 infection and enhancing public health outcomes.
A Multimodal Biometric Recognition System Based on Palmprint and Dorsal Hand Vein Using DenseNet
(National Institute of Technology, Silchar, 2026) Priyanka
Multimodal biometric systems have emerged as an effective solution to overcome the limitations of unimodal systems, such as susceptibility to noise, spoofing, and limited discriminative capability. Among various biometric traits, palmprint and dorsal hand vein modalities provide high distinctiveness and reliability. In particular, dorsal hand vein patterns, being subcutaneous, offer enhanced resistance to forgery and environmental variations. This thesis is a multimodal biometric recognition system that implement palm print and dorsal hand vein images using deep learning based feature ex- traction techniques. Different fusion Methods such as feature, score, and hybrid fusion, are evaluated to determine the best approach. Experimental results shows that hybrid fusion achieves best performance by effectively combining score and feature fusion. Furthermore, a Siamese DenseNet model with triplet loss and arcFace loss is employed to learn discriminative embeddings and acheived an Area Under Curve (AUC) of 0.991 and an Equal Error Rate (EER) of 0.045%. The results highlight the effectiveness of combining multiple biometric modalities with advanced fusion techniques for robust authentication.
A Novel Automatic Shot Boundary Detection Algorithm Robust to Illumination and Motion Effect
(Department of Computer Science & Engineering, NITS, 2019) Singh, Alok
Apparently, the summarizing, classifying and indexing valuable information from the massive amount of videos is very inefficient and time consuming through manual procedure. In order to make this process effective, it is necessary to segment a video into meaningful shots and locate the boundaries between the shots. Shot is a collection of continuous frames having similar content and visual feature and when the continuity in the similarity of the frames breaks it is termed as transition from one shot to another shot which creates a boundary between the shots. But, the performance of shot boundary detection approaches are yet to be addressed for the videos having sudden flash light and Object/Camera Motion efficiently. In this thesis, a novel approach for abrupt transition detection is proposed which can able to withstand under certain illumination and motion effect. The proposed algorithm can be roughly divided into two stages: firstly, the adaptive Wiener filter is applied to the lightness component of the frame to retain some important information on both frequencies and LBP-HF is extracted to reduce the illumination effect. From the experimentation, it is also confirmed that the motion effect is also reduced in the first stage. Secondly, Canny edge difference is used to further remove the illumination and motion effect which is not handled in the first stage. TRECVid 2001 and 2007 datasets are applied to analyze and validate our proposed algorithm. Experimental results manifest that the proposed system outperform the state-of-art shot boundary detection techniques.
A Sarcasm-aware Cyberbully Detection Framework for Code-Mixed Hinglish Text Using Deep Learning
(National Institute of Technology, Silchar, 2026) Rai, Sweety
Cyberbullying detection in Hinglish social media text is challenging due to code-mixing, informal language, and sarcasm that often masks harmful intent. Traditional text-based approaches struggle to capture such implicit expressions. This research pro-poses a two-phase sarcasm-aware framework for cyberbullying detection. In Phase 1, a MuRIL-based model is trained to learn sarcasm-aware representations from Hinglish cyberbullying-related data. In Phase 2, these representations are integrated with tex-tual and emoji signals for classification. An experimental setup involving model variants indicates the consistent improvement, with the proposed model achieving F1-score of 85.70%, in comparison to the text-only baseline which achieves 83.69%. Stratified analysis further shows improved performance on sarcastic samples, reducing the sarcasm gap from 27.19% to 18.83%. Cross-dataset evaluation on Mendeley and TRAC datasets shows improvements in AUC and overall F1-score. However, the observed F1 score im-provement is mainly due to better detection of non-aggressive samples, highlighting the effect of domain mismatch on aggressive classes. These findings highlight the im-portance of sarcasm-aware modelling while also reveals the limitations in cross-domain generalization.
A Smart Farming Model for Crop Yield Prediction Using Machine Learning Analytics
(National Institute of Technology, Silchar, 2026) Saha, Sukanya
A Study on Edge Wave Propagation in an Incompressible Anisotropic Initially Stressed Plate of Finite Thickness
(Department of Mathematics, NITS, 2025) Prakash, Ved
This study investigates the propagation of edge waves in an incompressible, anisotropic, initially stressed elastic plate of finite thickness and infinite length. Using Biot’s theory of incremental deformations, a closed-form dispersion relation for edge waves is derived. The analysis accounts for the influence of material anisotropy and initial stress components. Special cases are examined to validate the model against known solutions in stress-free and isotropic conditions. Notably, it is observed that edge waves cannot propagate in a thin isotropic plate without initial stress, whereas in the limit of a thick plate, the solution approaches Rayleigh wave propagation in a half-space. Numerical simulations are performed to explore how the normalized wave velocity c β depends on various physical parameters. The key variables include the normalized initial stress components S11 2Q and S22 2Q , as well as the scaled thickness parameter H = kh, where k is the wave number and h is the plate thickness. Initially, an isotropic case is assumed with N = Q. Results show that increasing S22 2Q leads to a significant reduction in wave velocity, consistent with acoustoelastic effects observed in compressively stressed media. In contrast, variation in S11 2Q causes a more modest reduction in velocity, highlighting the directional sensitivity of the wave response. Further simulations introduce anisotropy by allowing N Q = R to vary, representing different levels of directional stiffness. For fixed H, increasing R influences the dispersion behavior, with distinct shifts in the velocity profile depending on whether the medium is stiffer or more compliant in the transverse direction. Additionally, increasing H generally leads to higher wave velocities in the isotropic case, while in anisotropic media, the trend may reverse, indicating a complex interplay between geometry, pre-stress, and material anisotropy. These findings provide new insights into edge wave dynamics under realistic mate rial and stress conditions and have implications for non-destructive evaluation, micro acoustic device design, and the analysis of wave propagation in pre-stressed elastic structures.
A Study On Effect Of Inspection On Deteriorating Inventory Model
(Department of Mathematics, NITS, 2022) Rohit
Inventory management is the mainstream of Operational Research which plays a crucial role in business enterprises and industries. One of the important factors in inventory management is the deterioration of goods. In many inventory deterioration problems, the deterioration rate of goods highly depends on adjacent goods. Accordingly, there is a need to decrease the effect of deteriorated goods on other goods by removing them with the help of inspection. Therefore, the objective of this paper is to develop an inventory model for deteriorating goods, considering the effect of a number of inspections during the replenishment period on the average deterioration rate. Holding cost and demand rate both are a linear function of time which was assumed as constant in most of the deteriorating inventory model and deterioration rate of a single item is constant. Also, prepayments should be made by the retailer to the supplier. In this proposed model, the average deterioration rate depends on the number of inspections in each period. Likewise, to prove the uniqueness of the solution, the convexity of the model is proved. The model can be applied for the optimization of the total inventory cost for such business enterprises where they use the inspection method to control the deterioration rate.
A Study On p-adic Analysis
(Department of Mathematics, NITS, 2025) Devjibhai, Rathod Gautam
This dissertation explores foundational and advanced topics in p-adic analysis, a field that extends classical analysis into the realm of p-adic numbers, which are constructed through the p-adic valuation and associated metric. The non-Archimedean nature of p-adic fields leads to unique analytical and topological behaviours that differ funda mentally from real and complex analysis. In this report, we have studied the p-adic systems and their relevance to broader mathematical structures. This report focuses on the p-adic numbers and some of the properties of them. We start by defining a norm and the p-adic absolute value and studied that it is a norm on the rational numbers. We also studied that the p-adic norm is a non-Archimedean norm which has strange properties compared to the ordinary (Archimedean) norm. Then we define the p-adic numbers and discuss that it is a complete field with respect to the p-adic norm.
A study on q-series and partitions
(Department of Mathematics, NITS, 2025) Shit, Sabuj Baran
This Thesis explores the rich field of the q-series, highlighting how it interacts with three important mathematical theorems: Euler’s identity, the Jacobi triple product and the Pentagonal number Theorem, Ramnujan’s Partition Congruences. A foundation for comprehending the structure of q-series and their generating functions is provided by Euler’s identity. The depth of q-series is further demonstrated by the Pentagonal Number Theorem, which relates to combinatorics and integer partitions. Together, these results illustrate the profound coherence of the q− series within mathematics.
A study on the Monkeypox virus disease using the Mathematical Modelling
(Department of Mathematics, NITS, 2025) Gupta, Abhishekh
In this study, we proposed a novel mathematical model to analyze the transmission dynamics of the Monkeypox virus. The model is formulated as a system of non-linear ordinary differential equations, incorporating key epidemiological classes for both hu man and rodent populations: susceptible, exposed, infected, and recovered humans (SEIR), and susceptible and infected rodents (SI). We conducted a thorough anal ysis of the model’s equilibrium points, identifying the Monkeypox-Free Equilibrium point (MFE), the Rodent-Free Endemic Equilibrium point (RDEE), and the Endemic Equilibrium point (EE). We rigorously established the local asymptotic stability of the model at the MFE and RDEE. Furthermore, the global asymptotic stability of the endemic equilibrium was demonstrated through the construction and analysis of a suitable Lyapunov function. A sensitivity analysis was performed on the parameters comprising the basic reproduction number to determine their relative influence on dis ease transmission. Finally, the theoretical findings were complemented and illustrated through specific numerical simulations.
A Symmetric Cryptography Algorithm with 1024 bit Data Block and 512 bit Key with Enhanced Fiestel Cipher
(Department of Computer Science & Engineering, NITS, 2020) Maheshwari, Bharat
The growth of latest technology specially internet and its applications imposes a major concern in respect to information security in terms of protecting essential and confidential content against any possible and vital attacks. Cryptography has made the difference over the period of time by using developed encryption algorithms where some of them have successfully fulfil its designed goals, however others have failed due to specific requirements or encryption/decryption procedures have been recognized. Recent encryption algorithms employed aims mainly to escalate the security levels of sensitive information in order to deliver confidentiality, authentication, integrity, and non-repudiation. In this study, the proposed encryption algorithm has 1024 bit data block as input and combined the permutation and substitution methods for in-round encryption / decryption in-side the enhanced fiestel cipher along with pseudo randomly generated 512 bit key. Key generation and encryption / decryption performing using pseudo random number generation technique and enhanced fiestel cipher with 7 rounds methodology which removes of previous vulnerabilities. Analysis and experimental results have clearly proven solid randomness on key generation, encryption and decryption also enhanced performance and security in which it has met all desired objectives.
A Two-Stage Method for Damage Detection in Members and Joints of Steel Frames
(National Institute of Technology, Silchar, 2026) Sagar, Sharjid
AB-INITIO STUDY OF R3m-Rbgei3 For Photovoltaic Application
(Department of Physics, NITS, 2022) Banerjee, Palash
To fulfil the global energy demand and reduce dependence on fossil fuel, new energy source need to be explored. In this search solar energy has emerged as a clean and viable alternative but, large scale implementation is still a challenge due high cost of material and the complicated manufacturing process of absorber material like crystalline Si. In this regard halide perovskite material has came to focus as a cheaper alternative with comparable photovoltaic properties. But, presence of organic compound reduces its stability while Pb toxicity is also an hurdle. Hence the search for lead-free, inorganic perovskite for PV application is of prime interest. In this study we have explored one such material, all inorganic RbGeI3 within the scope of DFT with FP-(L)APW+lo method implemented by Wien2k. Structural properties like optimized atomic coordinates, lattice parameters, tolerance factor have been calculated indicating the structure’s stability. To get more profound knowledge for PV application, electronic properties like band structure, DOS, PDOS and optical properties like absorption(𝛼), real and imaginary part of dielectric constant (𝜀1,𝜀2), reflectivity(R) and refractive index(n) have been found including and excluding SOC effect. From band structure the compound was found to be a direct bandgap semiconductor. Bandgap using PBE was observed to be 0.958 eV while PBE + SOC was 0.823 eV. The absorption curve suggested that the material absorbs in visible and low UV range. Mechanical properties like elastic coefficients were also explored indicating it to be a stable material.
Abstractive Text Sumarization Evaluation
(Department of Computer Science & Engineering, NITS, 2022) Tawmo
Abstractive Text Summarization is a burgeoning natural language processing task that has seen with the deep learning models. With the soaring amassment Of un- structured text data, text summarization has found applications in many domains such as law, medicine and news. The growth in research interest in developing better sum- marization systems, more fluent human-like summaries, and significant, more refined datasets have offered to test natural language processing models to improve existing model performance. Even so, no significant improvements are made while evaluating the machine summaries, still relying on metrics such as BLEU and ROUGE which only performs syntactic evaluation and cannot evaluate based On semantic equivalence. Semantic Textual Similarity is a Natural Language Processing (NLP) task that uses a predetermined metric to score the relationship between texts or documents. Measuring semantic similarity scores between texts is useful in many applications in Natural Language Processing (NLP) and fields. In this work, we propose a novel evaluation method to semantically analyse and measure the quality of the abstractive text summaries. The proposed model compared the resultant output across the datasets to determine the proficiency of the model and the datasets with regards to ROUGE, BLEU scores and the baseline system.
Accurate Dynamic Line Rating Forecasting using Quantum Models
(National Institute of Technology,Silchar, 2026) Panmei, Jaojenlung

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