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A Deterministic Key-Frame Indexing and Selection for Surveillance Video Summarization
Video data is voluminous and impacts the data storage devices as there are CCTV surveillance videos being created every minute and stored continuously. Due to this increase in data there is a need to create semantic information out of the frames that are being stored. Video Summarization is a process that continuously monitors changes and helps in reducing the number of frames being stored. This work enables summarization to be carried out based on selecting threshold-based system that can select key-frames ideally suit for storage and further analysis. Initially a Global threshold based on Otsus method is carried out for all frames of a surveillance video and based on the set threshold a retrospective comparison is done on each frame based on statistical methods to converge on determining the keyframes. A similarity index is generated based on the iterative comparison of frames based on global and local threshold comparison. The local threshold is indexed based on Analysing Method Patterns to Locate Errors(AMPLE), An-derbergs D(AbD), Cohens Kappa(CK), Tanimoto Similarity(TS), Tversky feature contrast model(TFCM), Pearson coefficient of mean square contingency(Pmsc). The Global threshold is updated each time a keyframe is selected based on the comparison of local and global threshold. The results are compared with five surveillance videos and six methods to identify keyframes Selection Rate is the metric used for calculating the performance. 2019 IEEE. -
A Diabetes Detection Framework Based on Datadriven Predictive Technologies
Diabetes is a chronic disease spreading worldwide with major health challenges. It is not only caused by medical factors but other factors too such as genetic, demographics and lifestyle factors. With traditional or manual diagnosis methods, timely diagnosis becomes challenging due to complex and fragmented datasets. Recent advancements in machine learning (ML) models have greatly enhanced the efficiency and accuracy in disease diagnosis and risk evaluation. This review synthesizes the findings from the recent studies in the field of diabetes, major contributions and limitations, identifies the directions for the future work. This review has included the articles from three databases: Scopus, IEEE Xplore and PubMed; published between 2017 and 2025. The study has employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model for the review process. The scope of this work includes datadriven predictive technologies for diabetes detection, risk of complications and disease progression. It also sheds light on ongoing challenges such as data imbalance, limited interpretability, and population generalizability, while pointing to future opportunities in explainable AI and more personalized approaches to diabetes care. The review highlights that hybrid or ensemble models performing better than classical single models for risk prediction. 2025 IEEE. -
A Dimensionality Reduction Model: A Retrospective Approach on Dementia Triggering Parameters and Feature Ranking
The medical sector has advanced in an imposing way, and are coming up with lifesaving models and wearable devices for disease predictions and patient monitoring. The prediction models and wearable devices will lead to immense amount of data collection leading to the dimensionality issues, overfitting and inaccurate results. From the pool of data that we use for our prediction model, we should be able to identify the required information and parameters which gives a positive contribution to the decision making model. Every dataset with higher number of parameters and high dimensionality will tend to the problems of overfitting. Here, we have a dataset of demented and non-demented patients with five conventional features and other physical parameters. Along with these parameters, we are adding three new prediction parameters like glyhb, BMI and Cholesterol, for proving the association of Diabetics and Dementia. After the addition of these parameters, the dataset will have thirty parameters, and dimensionality reduction is done to avoid the condition of overfitting. The work uses Principal Component Analysis(PCA)for reducing the dimensionality, t-SNE for visualization and K means clustering is used to cluster the target variable. The cluster mean of each variable is used to understand the performance of each variable in each cluster. Later, a basic feature ranking method is also implemented which can be further used for the prediction model. The performance metric used in this research work is Silhouette score, Inertia and Inter-Cluster Distance map. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Discrete Kumaraswamy Marshall-Olkin Exponential Distribution
Finding new families of distributions has become a popular tool in statistical research. In this article, we introduce a new flexible four-parameter discrete model based on the Marshall-Olkin approach, namely, the discrete Kumaraswamy MarshallOlkin exponential distribution. The proposed distribution can be viewed as another generalization of the geometric distribution and enfolds some important distributions as special cases. Some properties of the new distribution are derived. The model parameters are estimated by the maximum likelihood method, with validation through a complete simulation study. The usefulness of the new model is illustrated via counttype real data sets. 2022. Journal of the Iranian Statistical Society. All Rights Reserved. -
A distinctive symmetric analyzation of improving air quality using multi-criteria decision making method under uncertainty conditions
This world has a wide range of technologies and possibilities that are available to control air pollution. Still, finding the best solution to control the contamination of the air without having any impact on humans is a complicated task. This proposal helps to improve the air quality using the multi-criteria decision making method. The decision to improve air quality is a challenging problem with todays technology and environmental development level. The multi-criteria decision making method is quite often faced with conditions of uncertainty, which can be tackled by employing fuzzy set theory. In this paper, based on an objective weighting method (CCSD), we explore the improved fuzzy MULTIMOORA approach. We use the classical Interval-Valued Triangular Fuzzy Numbers (IVTFNs), viz. the symmetric lower and upper triangular numbers, as the basis. The triangular fuzzy number is identified by the triplets; the lowest, the most promising, and the highest possible values, symmetric with respect to the most promising value. When the lower and upper membership functions are equated to one, we get the normalized interval-valued triangular fuzzy numbers, which consist of symmetric intervals. We evaluate five alternatives among the four criteria using an improved MULTIMOORA method and select the best method for improving air quality in Tamil Nadu, India. Finally, a numerical example is illustrated to show the efficiency of the proposed method. 2020, MDPI AG. All rights reserved. -
A distributed randomization framework for privacy preservation in big data
The privacy preservation is a big challenge for data generated from various sources such as social networking sites, online transaction, weather forecast to name a few. Due to the socialization of the internet and cloud computing pica bytes of unstructured data is generated online with intrinsic values. The inflow of big data and the requirement to move this information throughout an organization has become a new target for hackers. This data is subject to privacy laws and should be protected. The proposed protocol is one step toward the security in case of above circumstances where data is coming from multiple participants and all are concerned about individual privacy and confidentiality. 2014 IEEE. -
A Document Clustering Approach Using Shared Nearest Neighbour Affinity, TF-IDF and Angular Similarity
Quantum of data is increasing in an exponential order. Clustering is a major task in many text mining applications. Organizing text documents automatically, extracting topics from documents, retrieval of information and information filtering are considered as the applications of clustering. This task reveals identical patterns from a collection of documents. Understanding of the documents, representation of them and categorization of documents require various techniques. Text clustering process requires both natural language processing and machine learning techniques. An unsupervised spatial pattern identification approach is proposed for text data. A new algorithm for finding coherent patterns from a huge collection of text data is proposed, which is based on the shared nearest neighbour. The implementation followed by validation confirms that the proposed algorithm can cluster the text data for the identification of coherent patterns. The results are visualized using a graph. The results show the methodology works well for different text datasets. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A DSEESIPT-active organic luminogen for turn-on enantioselective recognition of chiral amino alcohols and selective hydrazine sensing
The development of dual-state emissive (DSE) organic luminogens has elevated the ease of recognition of various biological analytes, which demonstrates the multifaceted potential of dual-state emitters. Therefore, in this study, we synthesised a dual-state emissive excited-state intramolecular proton transfer (ESIPT)-based organic luminogen, (E)-4-(5-bromo-2-hydroxybenzylideneamino)-2,3-dimethyl-1-phenyl-1,2-dihydropyrazol-5-one (ANMB), exhibiting excitation-dependent phototunability with large Stokes shifts of 109 nm and 155 nm in both the solution and solid states, respectively, underscoring its potential as a biosensor. The metal-chelating ability of ANMB was investigated, revealing significant fluorescence quenching upon coordination with Cu2+ ions, leading to 96% reduction in emission intensity. The introduction of biological analytes, such as amino alcohols, enabled fluorescence recovery, where ANMB demonstrated enantioselective recognition: a single emission peak for the S-enantiomer and dual emission peaks for the R-enantiomer. Furthermore, ANMB demonstrated high selectivity for hydrazine detection in both the solution and solid states, with new emission bands observed at 411 nm and 432 nm, respectively, indicating a fluorescence shift from green to blue. Complementarily, ANMB was successfully applied for real-time imaging of hydrazine in food and plant samples, showcasing its practical adaptability. Additionally, in silico molecular docking studies were performed, revealing the potential therapeutic activity of ANMB against diarrheal targets. Overall, this work highlights the multifunctionality and tunability of DSEESIPT-based organic luminogens, positioning ANMB as a promising candidate for the selective recognition of biologically significant analytes in analytical and real-world contexts. This journal is The Royal Society of Chemistry, 2026 -
A Dual Step Strategy for Retinal Thin Vessel Enhancement/Extraction
Blood vessel extraction from retinal images is a challenging and fundamental step in pathological analysis. Most of the vessel extraction algorithms face difficulty in the extraction of thin vessels. In this paper, a dual step strategy for retinal thin vessel enhancement/extraction is proposed. Since thin vessel pixels have intensities closer to the background non-vessel pixels, the first level enhancement algorithms usually suffers in its accurate extraction. This led to explore a novel idea of eliminating the effects of thick vessel pixels in a reference image, via replacing it with neighboring non-vessel pixels. By applying second level enhancement on the vessel subtracted image, thin vessels are projected and improvement in extraction is attained subjectively as well as objectively. 2019 IEEE. -
A dual-fluorescence approach for turn-on ammonia and turn-off explosive picric acid detection via ESIPT inhibition: experimental, theoretical, and biological studies
A fluorescent naphthalene-anthracene dyad (AMN) was developed as a dual-mode sensor for turn-on detection of ammonia (NH3) and turn-off detection of picric acid (PA). AMN initially emits strong fluorescence at 427 nm due to excited-state intramolecular proton transfer (ESIPT), showing a large 62 nm Stokes shift. Upon PA addition, fluorescence is quenched and red-shifted to 463 nm. Conversely, NH3 induces a red shift to 435 nm. These spectral responses are attributed to ESIPT inhibition via strong hydrogen bonding between the hydroxyl group of AMN and the analytes. AMN has been successfully applied in dipstick-based PA detection and as a low-cost food spoilage indicator for NH3. Detection limits are 8.77 ?M for PA and 5.29 ?M for NH3, with a Stern-Volmer constant of 5.62 105 M?1 for picric acid. Additionally, AMN shows ratiometric fluorescence upon interaction with BSA and ct DNA, accompanied by notable absorption changes. These findings, supported by UV-vis, fluorescence spectroscopy, NMR, molecular docking, and DFT studies, underscore the potential of AMN as a multifunctional fluorescent sensor for environmental and biological applications. 2025 The Royal Society of Chemistry. -
A dual-functional rhodamine B and azo-salicylaldehyde derivative for the simultaneous detection of copper and hypochlorite: synthesis, biological applications and theoretical insights
A multifunctional rhodamine derivative containing azo-salicylaldehyde (BBS) was designed and synthesized as a colorimetric and fluorescence turn-on probe for the selective detection of copper cations (Cu2+) and hypochlorite anions (OCl?) in aqueous media. In the presence of Cu2+, the probe BBS exhibited turn-on absorption and fluorescence change at 554 nm and 585 nm, respectively. The binding mechanism of BBS with Cu2+ induces the opening of a spirolactam ring in the rhodamine moiety by the formation of a metal-ligand complex, achieving 10-fold enhancement in fluorescence and quantum yield, along with a binding constant of 1 104 M?1 and a detection limit of 2.61 ?M. Addition of OCl? enhanced the absorbance and fluorescence intensities at 520 nm and 575 nm, respectively. The probe BBS underwent hypochlorite-mediated oxidation, followed by hydrolysis, resulting in the formation of rhodamine B itself, which is detectable by the naked eye via the color and fluorescence enhancement by 11-fold with a high quantum yield and a detection limit of 1.96 ?M. For practical applications, sensor BBS can be used to detect Cu2+ in water samples and on cotton swabs. For biological applications, the interaction of the BBS-Cu(ii) complex with transport proteins such as bovine serum albumin (BSA) and ct-DNA was investigated through UV-vis and fluorescence titration experiments. Additionally, the structural optimization of BBS and the BBS-Cu(ii) complex was demonstrated using DFT, and the interactions of the BBS-Cu(ii) complex with BSA and ct-DNA were analysed through theoretical docking studies. Bioimaging studies were conducted by capturing fluorescence images of BBS with Cu2+ and OCl? in a physiological medium containing living plant tissue using green gram seeds. 2024 The Royal Society of Chemistry. -
A dual-phase-lag mathematical framework with mechanics-informed machine learning for predicting ocular thermal risk under environmental heating
Thermal damage to ocular tissues is a significant medical issue, as even minor increases in temperature can compromise corneal endothelial function, hasten cataract development, and disturb retinal metabolism. The aim of this study is to create a dependable model for forecasting temperature distributions in the human eye during external heat exposure, thereby facilitating safer therapeutic interventions, refined clinical risk evaluation, and improved environmental health surveillance. A dual-phase-lag (DPL) bioheat transfer framework with two relaxation times is created to capture the behavior of thermal waves that travel at a finite speed. Normal-mode analysis is then used to find closed-form analytical solutions for all six ocular layers. Parametric investigations measure the effects of things like temperature, evaporation, porosity, and perfusion. When compared to the LordShulman and Fourier models, DPL is clearly better at predicting thermal responses that are realistic for the body. Complementary thermal-safety mapping, sensitivity analysis, surrogate-model validation, and response-surface visualization offer enhanced engineering insights and expedited predictive capabilities. The study reveals that non-Fourier effects are essential in regulating peak temperatures, and tissue-level parameters substantially affect intraocular thermal loads. The model's limitations consist of axisymmetric geometry and temperature-independent material properties, which could be rectified in forthcoming three-dimensional or patient-specific investigations. This work offers a medically pertinent and computationally efficient methodology for ocular thermal safety, enhancing healthcare modeling, precision diagnostics, and protective measures for populations subjected to extreme thermal conditions. 2026 Wiley-VCH GmbH. -
A Dynamic Anomaly Detection Approach for Fault Detection on Fire Alarm System Based on Fuzzy-PSO-CNN Approach
Early detection is crucial due to the catastrophic threats to life and property that are involved with fires. Sensory systems used in fire alarms are prone to false alerts and breakdowns, endangering lives and property. Therefore, it is essential to check the functionality of smoke detectors often. Traditional plans for such systems have included periodic maintenance; however, because they don't account for the condition of the fire alarm sensors, they are sometimes carried out not when necessary but rather on a predefined conservative timeframe. They describe a data-driven online anomaly detection of smoke detectors, which analyzes the behavior of these devices over time and looks for aberrant patterns that may imply a failure, to aid in the development of a predictive maintenance approach. The suggested procedure begins with three steps: preprocessing, segmentation, and model training. A pre-processing unit can enhance data quality by compensating for sensor drifts, sample-to-sample volatility, and disturbances (noise). The proposed approach normalizes the data in preparation. The smoke source can be detected by using segmentation to differentiate it from the background. Following segmentation, Fuzzy-PSO-CNN is used to train the models. CNN and PSO, two of the most used alternatives, are both outperformed by the proposed method. 2023 IEEE. -
A facile and economic electrochemical sensor for methylmalonic acid: A potential biomarker for vitamin B12 deficiency
A facile and cost-effective method based on a modified pencil graphite electrode (PGE) has been developed for the sensing of methylmalonic acid (MMA). The electrode (Ag-PEDOT/PGE) was designed by the electrodeposition of Ag nanoparticles (NPs) on carbon fibre paper (CFP) coated with poly(3,4-ethylenedioxythiophene) (PEDOT). Field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), and other electroanalytical techniques were used to characterize the modified electrodes. The fabricated sensor showcased a wide linear dynamic range (0.50 pM-55 nM) and a low detection limit (0.16 pM). A sharp increase in anodic peak current shows the excellent rate of electron transfer arising from Ag-PEDOT and PGE. The developed electrode was effectively utilized towards electrochemical MMA determination in urine and human blood serum samples. The results obtained certainly indicate that the sensor has high selectivity, ensures rapid detection, is reproducible, and has high stability towards the quantification of MMA in real samples. This journal is The Royal Society of Chemistry and the Centre National de la Recherche Scientifique. -
A Facile One-Pot Solvent-Free Synthesis, in Vitro and in Silico Studies of a Series of Tetrahydropyridine Derivatives as Breast Cancer Inhibitors
Ammonium trifluoroacetate (ATA) catalysed synthesis of 1,2,5,6-tetrahydropyridine (THP) derivatives, under eco-friendly conditions via a facile one-pot strategy. We have synthesized fifteen THP derivatives, and docked into the crystal structure of Phosphatase and Tensin Homolog deleted on Chromosome 10 (PTEN) tumour suppressor protein (PDB ID: 1D5R) based on drug-likeness prediction and pharmacokinetic properties. Molecular docking simulation studies reveal that four of our synthesised compounds are potential hit candidates because they bound to the receptor through 57 conventional hydrogen bonds with ?9.7 to ?8.6 kcal/mol of binding energy. The compounds were evaluated using the in vitro inhibitory activity of MCF-7 breast cancer cell lines. Identified hit compounds showed moderate inhibition at (160320 ?g/mL) and inhibitory concentration IC50 values in the low micromolar range of 171.062, 189.803, 195.469 and 181.272 ?g/mL respectively. The results obtained are very promising; therefore fine-tuning the substituents of hit molecules with appropriate bioisosteres can lead to the development of potential leads. 2023 Wiley-VCH GmbH. -
A facile one-step microwave synthesis of Pt deposited on N & P co-doped graphene intercalated carbon black - An efficient cathode electrocatalyst for PEM fuel cell
A facile, single step microwave assisted polyol route for simultaneously depositing platinum as well as co-doping graphene oxide, is herein proposed. However, low durability and full cell performance of Pt/NPG (platinum deposited on nitrogen phosphorous co-doped graphene) was observed due to restacking of graphene layers. This issue was addressed by intercalating CB into the graphene layers as spacers during the synthesis (in-situ addition of spacers - Pt/(NPG + S)). Moreover, to study the influence of spacers, external addition of spacers (ex-situ - Pt/(NPG) + S) were also examined. Results from our study indicate that in-situ addition of spacers- Pt/(NPG + S) enhanced the full cell performance (405 mW cm?2) and exhibited <40% ECSA loss (37.47%), thereby attaining DoE target. Thus, emerging as a durable cathode electrocatalyst (Pt/(NPG + S)) for PEM fuel cells. 2022 Hydrogen Energy Publications LLC -
A facile, green synthesis of carbon quantum dots from Polyalthia longifolia and its application for the selective detection of cadmium
Carbon quantum dots (CQDs) has received world-wide recognition for their outstanding physicochemical properties that have the ability to substitute the semiconductor quantum dots. Herein, we have developed a strategy to determine the presence of Cd2+ using CQDs as a fluorescence probe. The CQDs were synthesized from the leaves of Polyalthia longifolia (a natural source) through a one-step hydrothermal method. The CQDs obtained from Polyalthia longifolia (p-CQDs) was characterized using XRD, TEM, FTIR, Raman Spectroscopy, XPS Studies, UVVisible spectroscopy and PL Spectroscopy. The p-CQDs displayed bright red fluorescence under the UV light, with good water solubility, and appreciable photostability and a quantum yield of 22%. The p-CQDs had a quasi-spherical morphology with an average particle size of 3.33 nm. The p-CQDs showed high selectivity and sensitivity for the detection of Cd2+ with a low limit of detection of 2.4 nM and a wide linear range of 7.3 nM12 ?M. The PL intensity of the p-CQDs showed a quenching effect in presence of Cd2+ and the mechanism of quenching was validated via fluorescence lifetime decay studies. We have also studied the effectiveness of the fluorescent probe developed for Cd2+ sensing in real samples of ground water and industrial effluents. 2022 Elsevier Ltd -
A facile, green synthesis of carbon quantum dots from Polyalthia longifolia and its application for the selective detection of cadmium /
Dyes and Pigments, Vol.210, ISSN No: 0143-7208.
Carbon quantum dots (CQDs) has received world-wide recognition for their outstanding physicochemical properties that have the ability to substitute the semiconductor quantum dots. Herein, we have developed a strategy to determine the presence of Cd<sup>2+</sup> using CQDs as a fluorescence probe. The CQDs were synthesized from the leaves of <em>Polyalthia longifolia</em> (a natural source) through a one-step hydrothermal method. The CQDs obtained from <em>Polyalthia longifolia</em> (p-CQDs) was characterized using XRD, TEM, FTIR, Raman Spectroscopy, XPS Studies, UV–Visible spectroscopy and PL Spectroscopy. The p-CQDs displayed bright red fluorescence under the UV light, with good water solubility, and appreciable photostability and a quantum yield of 22%. The p-CQDs had a quasi-spherical morphology with an average particle size of 3.33 nm. -
A FAMILY OF CONGRUENCES FOR (2, ?)?REGULAR BIPARTITIONS
The congruence of certain restricted partition functions known as regular bipartition is discussed in this paper. We particularly investigate the (2, ?)-regular bipartitions of n, denoted by B2,? (n), and establish certain congruences for B2,? (n) when ? ? 3. We derive infinite families of congruences modulo 4 for the (2, 3)-regular bipartition. We also obtain a generalisation of the regular bipartition for modulo p and p2. Indian Mathematical Society, 2022. -
A Family of Mexican Hat Wavelet Stieltjes Transform for Unbounded Non-decreasing Functions
In the present article, we examine the characteristics of the Mexican hat wavelet Stieltjes transform (MHWST) for a specific set of functions belonging to one of the sub-class of bounded variation functions. The subset comprises functions that are unbounded and non-decreasing. Further, a unified approach is applied to establish a uniqueness theorem and subsequently derive a representation theorem for the MHWST. The Author(s), under exclusive licence to The National Academy of Sciences, India 2024.