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Integration of Hyperspectral Imaging and Deep Learning for Sustainable Mangrove Management and Sustainable Development Goals Assessment
Mangrove forests support biodiversity and provide valuable ecosystem services. Their conservation is important for maintaining these benefits. In addition to this, understanding and preserving these forests is important for the assessment of Sustainable Development Goals (SDGs) such as SDG 1,2,3,6,8,11,12,13,14 and15. This review paper explores how the integration of Hyperspectral Image (HSI) technology and Deep Learning (DL) algorithms is helpful in mangrove conservation and SDGs assessment. HSI in mangrove research allows detailed analysis of tree health, species types and environmental stress factors (includes salinity levels, waterlogging, soil erosion, pollution, habitat fragmentation, disturbances from human activities etc.) with enhanced spectral and spatial resolution. Combining DL algorithms like Convolutional Neural Network (CNN) with HSI data automates mangrove mapping, detects change in mangrove health, estimates carbon sequestration and manages ecological zone. Rich spectral information from HSI empowers DL algorithms to identify patterns and features for accurate and efficient classification tasks in both supervised and unsupervised methods. This review aims to comprehensively summarize the research efforts reported in monitoring mangrove ecosystems through varied remote sensing approaches, algorithms and their support towards SDGs assessment. HSI and DL together offer a powerful approach for researchers, environmentalists and climate activists working towards sustainable development objectives. This paper not only focuses on mangrove conservation but also addresses challenges associated with integrating technologies such as data processing complexities and the need for specialized expertise. This study outlines advancements in HSI technology, DL applications and future directions to drive sustainable management strategies for mangrove ecosystems. The Author(s), under exclusive licence to Society of Wetland Scientists 2025. -
Self Risk Assessment Model Embedded with Conversational User interface for Selection of Health Insurance Product
In this research, we propose a dynamic model that works through Human-Computer Interaction to facilitate a smooth customer experience for health insurance prospects. The model facilitates the prospects to self assess their health risks. The integration of Conversational User interface, such as Mobile User Interface, Graphic User Interface and Bots with transcoder permits seamless use of the model by any category of prospects, irrespective of their language. Moreover, the model also helps the visually impaired person to interact without any hassle with the presence of a transcoder that permits conversion of text into speech and vice versa. The learner model comprises of the Prospects' detail module and Risk Assessment modules. The Prospects' detail module collects data from the predefined list. The risk assessment module profiles and assesses the risk based on the data inputted in the Prospects' detail module. The risk assessment level module categorizes the level of risk as low, moderate or high for each prospect depending on the total risk exposure level. The total risk exposure level is computed based on the pre-defined threshold. This model aids the prospect in determining the risk level and thereby facilitates self-selection of health insurance policy, thus reducing over reliance on the insurer. This model helps the prospect to take an independent purchase decision. 2022 IEEE. -
Fractional approach for a mathematical model of atmospheric dynamics of CO2 gas with an efficient method
In the present work, we find the series solution for the system of fractional differential equations describing the atmospheric dynamics of carbon dioxide (CO2) gas using the q-homotopy analysis transform method (q-HATM). The analyzed model consists of a system of three nonlinear differential equations elucidating the dynamics of human population and forest biomass in the atmosphere to the concentration of CO2 gas. In the current study, we consider Caputo-Fabrizio (CF) fractional operator and the considered scheme is graceful amalgamations of Laplace transform with q-homotopy analysis technique. To present and validate the effectiveness of the hired algorithm, we examined the considered system in terms of fractional order. The existence and uniqueness are demonstrated by using the fixed-point theory. The accomplished consequences illustrate that the considered scheme is highly methodical and very efficient in analyzing the nature of the system of arbitrary order differential equations in daily life. 2021 Elsevier Ltd -
Carrying capacity assessment for religious crowd management - An application to sabarimala mass gathering pilgrimage, India
Crowd Management is always a challenging task when people gather in large numbers. Crowd disasters in India, including recurring incidents at religious venues, demands a crowd management system developed on the characteristics of the place, event, and participants. Assessment of carrying capacity is the prime process to design crowd management protocols and regulations. Carrying capacity assessment of religious gathering venues in India is often an overlooked process. The present study assessed the crowd carrying capacity of Sabarimala pilgrimage, Kerala, India. Physical carrying capacity assessment methods used for tourism venues have been applied and contextualised for crowd carrying capacity assessment. Characteristics of the venue, pilgrimage and pilgrims were studied to map the active crowd area and space utilisation zones. The physical carrying capacity was estimated based on the comfortable crowd density and threshold crowd density assessments. The study identified two factors influencing pilgrim movement within the venue viz. service level at the holy step and capacity of the darshan facility. Service level at the holy step is the prime factor that regulates the flow of the pilgrim within the venue including the pilgrim movement for deity darshan and hence the comfortable capacity of the holy step was distinguished as the effective carrying capacity of the venue. Physical carrying capacity at the comfortable crowd density has to be maintained throughout the event to avoid the triggering of crowd crushes. The crowd carrying capacity assessment (CCCA) method applied in this study is a simple process. Considering the crowd density and crowd regulation factors, the CCCA method can be applied to design crowd management protocols of other religious pilgrimage destinations in India. International Journal of Religious Tourism and Pilgrimage -
VALIDATION OF CONTINUOUS FLOW METAL PLATE REACTORS IN THE TERPENE KETONE SYNTHESIS BY ALCOHOL OXIDATION
The present study elucidates the oxidation of alcohols to terpene ketones using dichloro(p-cymene) ruthenium (II) dimer catalyst by continuous flow process using a metal plate reactor. The synthesized products were separated and validated using GC, GCMS,1 H-NMR, and13 C-NMR techniques. The reaction process exhibited product yield in the range of 80-95% on a scale of 1-80 grams. Optimization studies were conducted to calibrate the reaction conditions to improve the product yield. The scope of the reaction was explored using aromatic, cyclic, and aliphatic alcohols under optimized conditions, which resulted in high yields of terpene ketones. A reaction mechanism is proposed for the oxidation of alcohols by a continuous flow process. The significant advantages of the current protocol include synthesis at mild conditions, safer handling of reagents, flexibility to tune reaction conditions, and straightforward scale-up in the range of 1-80 grams with high efficiency and reproducibility. 2024, Rasayan Journal of Chemistry, c/o Dr. Pratima Sharma. All rights reserved. -
CONTINUOUS FLOW TERPENE SYNTHESIS: METAL PLATE REACTOR-ENABLED DIELS-ALDER REACTIONS
This study presents an eco-friendly, high-efficiency protocol for the industrial production of terpenes using a continuous flow process with metal plate reactors. Traditional methods like batch processing for terpene production via the Diels-Alder reaction suffer from low efficiency, high waste disposal, and high costs due to stringent requirements. In the current study, the reaction parameters were optimized in a continuous flow setup, focusing on conversion, selectivity, and waste reduction to enhance economic viability. The results of optimization studies demonstrate that reaction completion is attainable at temperatures of 90C and above, under conditions of 4 bar pressure, 1.05 mole equivalents of methyl pentenone, and 0.05 mol% % catalyst loading. The usage of polar solvents was found to adversely affect reagent conversion efficiency. The protocol was validated by comparison with batch methods and demonstrated reproducibility and scalability through a 24-hour continuous process, achieving a 95% mole yield and 96% conversion under optimized conditions. Analyses of Gas chromatography, GC-MS,1 H-and13 C-NMR were used to confirm the products. The findings support the industrial potential of the protocol for sustainable terpene production, reducing polymerization and minimizing by-products. 2025, Rasayan Journal of Chemistry, c/o Dr. Pratima Sharma. All rights reserved. -
An algorithm to detect an object in a confined space by using improved fingerprinting approach
The rapid evolution of location-based services has made tremendous changes in the society. In this paper, Trilateration method is implemented in fingerprinting methodology to obtain very precise and low error position details of the client portable device. Trilateration is a method in which the portable device is determined by the received signal strength intersecting at one position from the three reference points. Fingerprinting method involves several steps like training stage and positioning stage in which the training stage consists of the creation of the database of the signal strengths along with its associated location measurements. In the positioning step where effective and efficient received signal strength collected from the portable device is matched with the data saved into the database to get the position information of the client. The position of the user is estimated by collecting the received signal strengths from three reference points by using the concepts of trilateration approach in fingerprinting methodology to obtain more precise and accurate information. 2005 - ongoing JATIT & LLS. -
Enabling context-awareness: A service oriented architecture implementation for a hospital use case
The medical field is continuously flooded with newer technologies and tools for automating all kinds of medical care processes. There are a variety of software solutions and platforms for enabling smart healthcare and for assisting care providers such as doctors, nurses, surgeons and specialists with all kinds of timely insights to diagnose and decide the correct course of actions. There are patient monitoring and expert systems to simplify and streamline healthcare service design, development, and delivery. However there are concerns and challenges with the multiplicity and heterogeneity of technologies and solutions. The dense heterogeneous medical devices available in the intensive therapy units pose a challenge of medical device integration. Needless to say, lot of research work has gone in devising techniques in integrating these systems for exchange of data. However mere device integration does not exploit the modern technologies until meaningful and critical information is presented to doctors and patient care personals adapting to the changes in the patient condition. The goal of this research is to apply context aware computing using service oriented architecture in acquiring, analysing and assisting doctors and nurses with necessary information for easy and critical time saving decision making. This paper presents an implementation of the identified web services which can be consumed during a treatment at the Intensive Therapy Unit (ITU). 2015 IEEE. -
Framework for automatic examination paper generation system /
International Journal Of Computer Science And Technology, Vol.6, Issue 1, pp.128-130, ISSN No: 0976-8491 (Online) 2229-4333 (Pint). -
DFT study of structural and electronic properties of [Fe(CO)4(PbX)] complexes (X = O, S, Se and Te): Influence of terminal lead chalcogenide ligands on bonding and stability
Density Functional Theory (DFT) calculations at the B3LYP level were performed to investigate the structural and electronic properties of axial and equatorial isomers of [Fe(CO)4(PbX)] complexes, where X = O, S, Se, and Te. Total energy evaluations indicate that equatorial isomers are generally more stable than their axial counterparts. Detailed bonding analysis was carried out using Natural Population Analysis (NPA) and Energy Decomposition Analysis (EDA), providing insight into the nature of the FePbX interactions. The FePbX bond strengths were further assessed through Wiberg Bond Index (WBI) calculations. Frontier Molecular Orbital (FMO) analysis revealed HOMOLUMO gaps ranging from 3.04 to 3.97 eV, all of which are narrower than the corresponding gap in Fe(CO)5, suggesting enhanced electronic reactivity due to PbX substitution. Natural Bond Orbital (NBO) analysis indicated a greater electron density contribution from the Pb atom to the FePb bond, whereas for FeC bonds, carbon atoms contributed more significantly than Pb. These results collectively highlight the influence of terminal lead chalcogenide ligands on both the geometric and electronic structure of iron carbonyl complexes. 2025 Elsevier Inc. -
Driving purchase intentions through visual storytelling: a study of social media platform reels sponsored advertising
This study explores how social media platforms (for example, Instagram) and Reels-sponsored ads influence what makes consumers stop, watch and decide to buy. Using Consumer Engagement Theory (CET) as a lens, it looks at how people emotionally, cognitively and behaviourally respond to short-form video content. Four key factors were examined: engaging content, scenario-based experiences, user participation and perceived usefulness. Data from 393 active social media platform users in Indias National Capital Region revealed that all four elements positively shaped consumer attitudes, significantly influencing purchase intentions. Notably, relatable and emotionally engaging content had the most substantial impact. Attitude played a central role, bridging how consumers feel about a Reel and what they choose to do next. For marketers, the takeaway is clear: Reels that are visually appealing, useful and invite interaction are more likely to turn engagement into action. The study offers timely insights into how brands can connect meaningfully through short-form video. 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Classification of a New-Born Infant's Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning
A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive. 2023 Lavoisier. All rights reserved. -
Comprehensive study on using hydrogen-gasoline-ethanol blends as flexible fuels in an existing variable speed SI engine
The rising human population is causing the utilization of enormous amounts of fossil fuels to fulfill energy needs. Various renewable sources are used as fossil fuels however those resources are not powerful in supplanting customary non-renewable energy sources like gasoline in vehicles. The depletion of conventional fossil fuel utilized in a vehicle contributes to an increased portion of air contamination and is a danger to human well-being. Also, to maintain the supply demand, many active types of research have been carried out in mixing a higher percentage of ethanol over gasoline and further moving towards flex-fuel vehicles. But there arises a problem of knocking and higher CO, and HC emissions from the engine. To overcome the above problem, ethanol could be mixed in a higher percentage over gasoline with the help of hydrogen assistance and can completely avoid the problem of knocking and reducing CO and HC emissions. In this research, the combustion, emission, and performance characteristics of a variable-speed gasoline engine fuelled with ethanol-blended gasoline along with hydrogen assistance are taken for investigation at variable speeds like 1800, 1600, 1400, and 1200 rpm. Hydrogen is added to blended fuel (E30) which has better combustion, emission, and performance than other blended fuels. Hydrogen addition is done at 2, 3, and 4 ms respectively. The outcomes showed that the E30 + H2 at 3 ms has better combustion, emission, and performance, still, the emission of NOx is higher in comparison with all the other blends due to complete combustion. Thus, a two-stage analysis has been done, one is making a comparison among various blends of ethanol, and the second one is the comparison among the various energy shares of hydrogen. 2023 Hydrogen Energy Publications LLC -
ALT speech recognition system using F0 improvement and spectral tilt method
Human Beings use voice as the medium for communication. Human Speech is a very complex signal with multiple frequencies, amplitudes and intensities that mix up to convey specific information. In international terminology, voice disorders are described as dysphonia. Various dysphonias are clearly organic origin due to nervous, muscular, neuro or cellular degenerative disease affecting the body or it is from local laryngeal changes. Other dysphonias having no visible laryngeal causes are grouped as non organic involving habitual dysphonias that arise from faulty speaking habits or the psycho genic dysphonias that stem from emotional causes. This paper looks at a speech recognition system for disordered speech generated by Physically Disabled people using Artificial Larynx Transducer (ALT) device from the perspective of Speech Signal Processing. From the ALT speech features like formant, pitch and spectral tilt is estimated. For formant frequency estimation RNN technique is used. Before training the system pitch frequency improvement is accomplished. Now the features and homomorphic based coefficients are used for training the system. The same operation is performed during the test phase and compared with the training set. Comparison and decision making is accomplished using distance estimator. BEIESP. -
Investigation of speech synthesis, speech processing techniques and challenges for enhancements
The sound produced by any human being or instrument can be used for various applications using the concept of extraction or selection. Using this concept, virtual sounds are produced which is prime requirement for various speech synthesis applications. In this paper we review the different speech processing methodologies, parameters involved and the various applications based on the speech quality produced. Though an overview is given on the processing and involved parameter, priority is given to the speech enhancement application. This survey helps to identify the challenges involved in various processing technique involved in speech enhancement of healthy and disordered speech. These findings with different speech production and speech synthesis techniques will help to improve the quality in various application of speech to text (STT), text to speech (TTS), Automatic speech production (ASP) and Automatic speech recognition (ASR). Copyright 2019 American Scientific Publishers All rights reserved. -
Enhancement of substitution voices using F1 formant deviation analysis and DTW based template matching
Speech is the best way to express the thoughts and feelings among the human beings. But for many reasons the sound produced by human beings becomes disordered voice and termed with many names based on the cause as stammering, dys-theria, apraxia and so on. In the above mentioned few examples, the voice becomes disordered because of the underperformance of body's organ. The larynx is removed in some human beings because of cancer. For them an artificial larynx transducer (ALT) is used to produce the sounds. The above all sounds are categorized as disordered voice and the sound produced by ALT is also called as Substitution voice. In this paper, a method is used to improve the quality of substitution voice produced by ALT. Algorithm is developed to estimate undesired audio components from the device output and remove the same using Non Linear Spectral Subtraction (NLSS) technique. Further, Fundamental (F0) contour and novel parameter F1 formant deviation of healthy speech (HE) and ALT speech are determined. The above two parameters are estimated and stored during the training phase of the system. In the test phase, the above mentioned parameters are estimated and they are used to scale down the database to reduce overall enhancement time. Next step is template matching done by mapping test data with training data using Dynamic Time Warping (DTW) Technique. The data base with least distance estimation is recognized as the utterance and the same is played back. 2017 IEEE. -
Formant frequency estimation of artificial larynx transducer speech using recurrent neural network
Human Beings communicate with each other by speaking. The speech as a signal has 2 components voiced and unvoiced speech. Voiced speech is produced by the excitation produced at glottis and unvoiced is produced by noise created at the mouth. The voiced components that is produced at glottis passes through the vocal tract and then reach the mouth. The nature of the speech is determined mostly at the vocal tract. But for some reason for some people the speech produced is not proper because of the organ problems or motor disorder issue. In these cases, the speech produced is called disordered speech and termed with the names like stammering, apraxia, dysar-theria and so on. In some case, the larynx is removed from human body because of cancer or other issues. For them, Artificial Larynx Transducer (ALT) is given to produce substitution voice. This paper aims at formant frequency estimation of the speech produced with the help of ALT using Recurrent Neural Network (RNN) method. The speech produced with the help of ALT will lack in naturalness and intelligibility. The direct noise coming from ALT device is called DREL noise. This also creates irritability to the listener. So in this paper, a method is proposed for the DREL noise removal and formant frequency estimation of the ALT speech. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Microbial fuel cells for electricity generation and environmental bioremediation
The environmental impact on the use of fossil fuels and their unsustainable nature has led to the development of techniques using renewable energy and fuel cells. The recent decade has captured the attention of scientists towards the importance of microbial fuel cells (MFCs) with the role of microbial ability in converting organic wastes directly to electricity through microbially catalyzed anodic reactions along with microbial/enzymatic cathodic electrochemical reactions. MFC represents an environmental friendly approach for the use of generating electricity using wastewater, thus ensuring a bioremedial approach for effluent treatment with the achievement of chemical oxygen demand (COD) of about 50% chemical oxygen demand and power densities. This MFC utilises microbial metabolism for electricity generation. The overall performance of electricigens or MFC is based on the reactor design, operating conditions, electrode material used, types of substrates, and microorganisms involved. The optimization parameters studies for commercial production and their applications for MFC need to be intensified. Microbes have applications as biopolymer electrolytes that can be variously used in the applications of batteries, fuel cells and dye-sensitized solar cells. The use of MFCs has many advantages as they are eco-friendly, they have high performance abilities and they are costeffective and therefore can be used for modern applications. 2022 by Nova Science Publishers, Inc. -
Rating of Online Courses: A Machine Learning Based Prediction Model
Online courses market has provided an economical and easy access to knowledge. When it comes to make a decision related to purchase of online course, little is known about what attributes can be depended upon to guess the quality of an online course. Ratings for online courses act as a reliable signal for assessing the quality of a course. The study discusses the prediction of ratings for online courses using Artificial Neural Network based on Particle Swarm Optimization (ANN-PSO). The experimental results suggests that ANN-PSO model has the capacity to predict the ratings for online courses on the basis of its attributes with accuracy. 2021 IEEE. -
Unveiling the Potential of Bacillus paramycoides, a Halotolerant Endophytic Bacterium with Heavy Metal Tolerance and Plant Growth Promotion Properties
The use of heavy metal resistant plant growth promoting endophytes is an effective method for improving crop yield and cleaning up contaminated sites. In our study, we have isolated thirteen bacterial endophytes from the shoots of Alternanthera philoxeroides, an aquatic plant from Bellandur lake, Bangalore, India. Among the isolates, Bacillus paramycoides showed significant plant growth promotion properties including an extortionate amount of indole acetic acid (IAA) production (144.69 1.01 g/mL) along with other plant growth promoting attributes like ammonia production, nitrogen fixation, phosphate, potassium solubilization, 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase and siderophore production. The isolate also demonstrated the ability to resist pathogen attacks by producing extracellular enzymes, which could have potential industrial uses. Furthermore, it displayed resistance to multiple heavy metals like chromium (Cr), copper (Cu), lead (Pb), zinc (Zn) and cadmium (Cd) as well as the ability to tolerate high salt concentrations (up to 7% NaCl). These characteristics make it an ideal candidate for promoting plant growth in stressful environments and as an effective bioremediation agent. 2024 World Researchers Associations. All rights reserved.