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Many applications of Natural Language Processing (NLP) like machine translation, document clustering, and information retrieval make use of Word Sense Disambiguation (WSD). WSD automatically predicts the sense of an ambiguous word that exactly fits it as per the given situation. While it may seem very easy for humans to interpret the meaning of natural language, machines require the processing of huge amounts of data for similar tasks. In this paper, we propose an automatic WSD system for the Kashmiri language based on the Naive Bayes classifier. This work is the first attempt towards developing a WSD system for the Kashmiri language to the best of our knowledge. Bag-of-Words (BoW) and Part-of-Speech (PoS) based features are used in this study for developing the WSD system. Experiments are carried out on a manually crafted sense-tagged dataset for 60 ambiguous Kashmiri words. These 60 words are selected based on the frequency in the raw corpus collected. Senses for annotation purposes of these ambiguous words are extracted from Kashmiri WordNet. The performance of the proposed system is measured using accuracy, precision, recall and F-1 measure metrics. The proposed WSD model reported the best performance (accuracy = 89.92, precision = 0.84, recall = 0.89, F-1 measure = 0.86) when both PoS and BoW features were used at the same time.

Biometric template protection schemes are designed to provide specific security guarantees to biometric traits. However, most of these schemes do not address the unlinkability requirement (viz., multiple biometric templates should be independent of each other) from the theoretical vantage point. This study proposes a mechanism for storing fingerprint templates in an unlikable manner by hiding them within fixed-sized groups. The core of our model is based on the notion of k-anonymity, which guarantees that the biometric template of a particular subject remains concealed among the templates of other (k-1) subjects. The resulting anonymized features ensure that the identity of the subject does not get revealed under any circumstance, thereby preventing cross-matching or linking-based attacks within different applications. We have formally analyzed our model by quantifying the degree of anonymization of the fingerprint templates in the form of a metric having range [0, 1]. The entire scheme remains non-invertible due to the generalization of the fingerprint features. We have performed extensive empirical evaluations of our model over four benchmark fingerprint databases, for which we obtained EERs that are comparable to that in the stolen-token scenario. Hence our work introduces an approach for securing biometric databases in a provable manner.

This paper presents a fuzzy Delphi-ISM approach to determine the enablers and establish a hierarchical relationship of the enablers of the three strategies suggested by National Institution for Transforming India (NITI) Aayog and Rocky Mountain Institute (RMI) report i.e., cleaner technologies, modal shift, and operational efficiency. The enablers are categorized based on the PETTOHI framework. Twenty enablers each for cleaner technologies and modal shift, and fourteen enablers for operational efficiency, respectively, were shortlisted after the fuzzy defuzzification process using the fuzzy Delphi method. Three hierarchical models were then developed using Interpretive Structural Modelling (ISM) to identify the interrelationships among the enablers of each strategy. A Cross Impact Matrix Multiplication Applied to a Classification (MICMAC) analysed the enablers based on their driving and dependence powers. ISM model for enablers of cleaner technologies reveals that the policy and organisation related enablers like implementation of BS VI norms, public private sector collaborations are the major enablers. Study on the enablers of modal shift identifies the importance of policy and infrastructure related items. The development of Multi-modal logistic parks, imposition of eco taxes are the driving enablers in this ISM model. Implementation of PM Gati Shakti, development of highways, waterways, freight corridors are the key enablers for improving operational efficiency. Enablers pertaining to policy, organisation, and infrastructure such as imposing eco taxes and infrastructure development are critical.

3D CT and MRI brain images are used in brain tumor detection due to their tendency to compare tissue density. Hence various research has been presented previously to detect brain tumors from the CT and MRI image but they faced issues in both segmentation and classification processes. During the detection of brain tumors, the existing segmentation techniques require higher decomposition levels and were unable to accurately segment the mutually exclusive and exhausted regions and during the classification of various tumor types, the linear inseparability occurs due to tumor regions' significant similarity and lack of co-occurrence matrix for principal distinctive features. Hence, to accurately detect brain tumors, combined 3D CT and MRI brain images have been used in the novel model named Dictionary learning based Segmentation and Spearman Regression in which Sparse Mahalanobis Dictionary based MMRF Model has been proposed that utilized sparse coding with Mahalanobis Dictionary learning enables the creation of a dictionary matrix that discriminates between healthy and tumor tissues, achieving isolation without complex decomposition levels. When combined with the probabilistic weighted segmentation process in a Multimodal Markov Random Forest, it effectively delineates mutually exclusive and exhaustive regions in multimodal images, preventing tissue loss. Moreover, to solve the issues in the classification of various types of brain tumors, Nested PatchNet Spearman Regression is utilized in which principal distinctive features were extracted by forming nested 3 × 3 patches, and their co-occurrence matrix was generated to find the correlation between various tumor regions, thereby effectively eliminating linear inseparability and classifying brain tumors as the pituitary, meningioma, and glioma using Coyote Optimization-driven Lagrangian neural networks. The result obtained showed that the proposed model outperforms existing techniques with a high detection rate and low loss.

The manuscript proposes a novel spectrally efficient code-domain (CD) based intelligent reflecting surface (IRS) transmission scheme intended for sixth-generation (6G) communications. IRS transmission creates a new paradigm for ultra-reliable, high-speed communications even at low signal-to-noise ratio (SNR) through intelligent manipulation of channel phases. The proposed CD-based IRS scheme studies the performance of IRS transmission both in dual-hop (DH) and access point (AP) modes with the presence of the channel phases. The proposed scheme improves the SNR at the receiver, and the simulation results validate the performance improvement. The performance curves analyze the distribution of symbol error probability (SEP) for different SNR values, signal constellations, and the number of reflecting elements. The SEP performance of the proposed scheme is superior to the conventional IRS scheme in all scenarios. Besides, the overall system performance is also tested without the knowledge of the channel phases. The system capacity and throughput performance results manifest the dominance of the proposed scheme under the context of high spectral efficiency.

Titanium Grade 2 strips or sheet metal, a commercial alloy used extensively in airframes, aircraft engines, marine applications, tubing and orthopedic implant/prosthetics, has been manufactured employing Large Strain Extrusion Machining (LSEM) process. LSEM is one of the severe plastic deformation processes (SPD), involving very large strains and shear forces, converting bulk material into very thin strips or sheets in a single pass. The strips or sheets produced by LSEM have improved bulk mechanical properties over the raw material owing to severe plastic deformation. The current work focusses on the influence of cutting speeds on the fabricated Titanium Grade 2 strips by LSEM on surface integrity in terms of surface microstructure, hardness, surface roughness, induced surface residual stresses and corresponding surface corrosion. The study showed that variation in cutting speed has significant bearing on surface characteristics of the strips produced employing LSEM. At comparative lower cutting speeds, surface quality and corrosion resistance of the produced strip is enhanced.

Volcanic eruption is known as multi-hazards to the surrounding environment and society causing the formation of lahar as the frequent hazards that shortly occurred due to airborne tephra after an eruption. Erosion triggered by rainfall on unconsolidated tephra material, such as volcanic ash, is the primary lahar initiation mechanism. The time and scale of lahars vary based on eruptions and watershed conditions. The variability of the erosion process is driven by a set of local factors including the grain size and spatial distribution of volcanic ash thickness, slope, and rainfall intensity. Laboratory simulation experiments were conducted in a 3.00 m long, 0.75 m wide, and 0.50 m deep flume to study the relationship of volcanic ash erosion rate to three driven parameters, namely slope, rainfall intensity, and volcanic ash thickness. Three slope gradients were selected to represent gentle (14.1%), mild (26.8%), and steep (46.6%) slopes. Meanwhile, the rainfall intensity ranged from 0.65 to 1.85 mm.min^-1, and the variations of volcanic ash layer thickness were 1.00 cm; 2.50 cm; and 5.00 cm. The erosion rate model was generated from a dimensional analysis accommodating slope, rainfall intensity, flow discharge, and the ratio of critical and applied boundary shear stress as independent variables. The variable coefficients were obtained by parameter optimization of experiment data through nonlinear regression analysis. The erosion rate model performance was tested using the Nash-Sutcliffe model Efficiency (NSE), Index of Agreement (IOA), and Root Mean Square Error (RMSE). The performance of the volcanic ash erosion rate model was proven to be satisfactory with the NSE>0.75, IOA>0.95, and RMSE values ranging from 0.005 to 0.009. This model has proven applicable better for volcanic ash erosion than other erosion models which are sandy soil material-based experiments because of the specific characteristics of volcanic material itself. The volcanic ash erosion model in this study can be implemented to predict the sediment yield of tephra as part of volcanic disaster mitigation.

Underground coal mining operations are vital to global energy supply that often take place in challenging environments where structural stability and safety are paramount. Shotcrete is a widely adopted technique for reinforcing underground rock surfaces which has been proven effective in safeguarding against roof collapse and structural instability. However, the underground environment remains susceptible to low velocity impacts from falling debris, blasting and equipment interactions which can pose significant risks to miners and infrastructure. This research article presents an experimental investigation into the effectiveness of tuned mass dampers (TMDs) in enhancing the structural resilience of shotcrete reinforced coal mines when subjected to low velocity impact loads. Further, this study employs a systematic experimental approach using variable head-free falling impact testing equipment to explore the potential benefits of integrating tuned mass dampers into the support systems of shotcrete reinforced coal mines. The study employs drop weight falling head impact tests with varying drop height of 1.0 m, 1.5 m and 2.0 m to study the influence of the impact energy. It was observed that the peak force characteristics increased for all damped cases with an increase of 15 kN, 18.41 kN and 12.64 kN and corresponding increment of 17.64%, 14.13% and 9.15% respectively as compared to undamped cases in addition to the reduced strain values in coal mine for damped cases under drop weight impact. The findings of this study conclude that after installation of tuned mass dampers the structural damage caused by low velocity impact was reduced and provides valuable insights into the applicability of TMDs and their potential to enhance safety and structural integrity in underground coal mines.

The transportation problem is one of the most significant mathematical programming applications that appears in various real-world decision-making problems. In an actual scenario, the supply, demand, and cost parameters of a transportation problem cannot be exactly quantified due to market instability. To deal with such types of impreciseness, the researchers have widely used fuzzy numbers and their extensions. Pythagorean fuzzy set theory is a prominent tool for handling uncertain and vague information in complex decision-making situations. This paper aims to develop a solution approach to solve the transportation problem with uncertainty in input parameters by incorporating Pythagorean fuzzy numbers. To do so, first, a new score function is proposed to rank Pythagorean fuzzy numbers more efficiently. A comparative study highlights some flaws in existing score functions, which depicts the advantages of the proposed score function over existing ones. Afterward, we solve the Pythagorean fuzzy transportation problem using the proposed score function. The solution technique is demonstrated with the help of some numerical examples. In addition, a comparative study is also included to show the efficacy of the proposed approach over existing ones.