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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. -
Portrayal of Latin American culture and characters in hollywood /
Latin American culture is known to be rich in terms of literature, art, music and history, but is also famous for its conservative attitude. There are however, a few aspects of the Latin American culture that are stereotyped in Hollywood films like their family system, drugs, mafia, religion and society and their economic status. -
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 -
A Study on Popular Naga Cuisine and Its Representation on Instagram
Food is a very sensitive topic as it is the representation of culture that shapes identity such that any flaw in representation could result in identity confusion or identity clashes. Food culture and its meaning varies from one culture to another. Also, very often one will notice that a cuisine which is a delicacy for a community could be a taboo or unacceptable for the other. India is known for its rich diverse culture, which includes geography, lifestyle, food habits, biodiversity and more. It is commonly seen that dominant food are often presented as the national cuisine while relegating others to the margins or erasing them altogether. A society is dynamic in nature, which goes through constant social issues too. But while the society strives to solve or seek for a solution to the conventionally defined social problem it fails to count in the misrepresentation of food culture as an issue that results in identity crisis. The dependency on media has increased tremendously such that a personal opinion and views about a subject are shaped by the sources that are readily available at their convenience in the form of social media. Today the concept of food gram that is the combination of Food and Instagram is a very popular trend among netizen. Instagram is an online photo-video sharing application where popularly in these context users post food images of what they eat, with whom and where. Whereas for professional based account it is seen as a marketing forum. The representation of food culture on social media is seen as an advantage and a challenge. The food culture of the Naga Tribes of Nagaland, Northeast, is the core of the study. It aims to understand the dominant tribal representation of Naga food and seek to understand how these representations on Instagram shape perceptions about the Naga population. The researcher has adopted a theoretical framework of Representation and Semiotics. A triangulation method approach has been applied for the study in analyzing Instagram post that is hash tagged, #nagacuisine from the month of July and August 2018. How Instagram as a medium represent Naga food and how it shapes an identity for the Naga population is what this study will seek to understand. -
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. -
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. -
Establishing a service composition frame work for smart healthcare system
As the idea of location awareness has already matured and numerous applications are flooded in today???s word, the logical next step reasons out, to context-awareness. Though the idea of context-awareness has been in the research field for close to two decades, the recent advancement in Internet of Things has brought a more compelling thrust in its research. Sensor networks integrating billions of sensors and actuators will be prevalent in the near future producing big data. Filtering and analysing this data with the contextual information will yield more significant results. But deducing the context information itself poses many challenges and unresolved research problems. Context-awareness systems involve acquiring, analysing, reasoning the data and composing the services for suitable action. Service composition either by orchestrating or choreographing technique has been deployed in certain applications, however, each domain requires unique methodology. Healthcare has always been the top priority when it comes to applying novel technologies. Applying context-awareness computing in the healthcare service sector is of paramount importance. The problem context for this research lies in a cardiology speciality hospital???s Intensive Therapy Unit or the post-surgery recovery ward which has lot of scenarios emanating that involves course of actions to be delivered by the healthcare professionals depending on the context. Depending on mere human service may not be adequate. With the available advancements in technologies, it would be possible to leverage optimum service in that time critical situations, provided technology can sense the changes in context and act accordingly. The course of actions to be taken involves an amalgamation of understanding the location, presence availability, relevance of and coordination among various departments, machines and personnel. This can be summarized as ???Response??? with ???Context-Awareness???. The primarily task is to sense the context and then determine and locate the relevant services, which are distributed in the World Wide Web, to achieve a goal situation as a solution to the problem. In order to deliver such a solution we need to develop an exclusive context-aware framework. The existing frameworks will not be adequate to meet such a demanding situation and hence, the research problem is to evolve a comprehensive service composition framework for smart healthcare systems. In order to solve this problem, a use-case approach was followed. After identifying an appropriate use-case, the solution was first modelled using Automata. The concept of service automata and timed automata were fused to deliver a timed-service automaton which is appropriate to model and test the framework and algorithm for service composition. As a solution to the research problem, a composition based framework of a context-aware smart healthcare system has been presented. It will guide software developers to deploy services for critical healthcare, under the umbrella of Service Oriented Architecture. The matured concept of Automata has been tweaked to present novel timed-service automata which will enable service composition precisely for meeting the time constrained demands of modern healthcare service requirements. It has been tested with UPPAAL verification tool for validity and concurrency. A prototype has been implemented to study the validity of the established framework. Apache JMeter tool was used to test the strength of the services and engine developed based on the proposed algorithm for effective service composition. -
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). -
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. -
Noise removal feature enhancement and speech recognition techniques for artificial larynx transducer speech
Speech impediments are the state of difficulty for a person to speak comfortably. These impediments make the spoken speech distorted and they are generally categorized as disordered speech. The quality of disordered speech is poor as clarity, intelligibility and naturalness is missing. In most type of disordered speech the voice is natural and produced by the vocal system of the human being. The vocal system includes the organ called as Larynx placed in the upper part of the neck. This organ has the vocal folds that contribute for pitch variation and volume of the speech. This organ will be malfunctioning some time or will be removed because of cancer. In both the case in order to restore speech, an external device called Artificial Larynx Transducer (ALT) is used to produce the sound. It is a small handheld battery operated device and is used for decades to obtain the audible speech for people who lost their speech because of removal of larynx. The quality of speech and its intelligibility of AL speakers have not improved for decades. The reason for poor quality is constant vibration of ALT, direct sound from ALT and pressure offered to produce the vibration. newlineSo in this research the nature of the speech produced from ALT is analyzed, a possible enhancement of the parameter is done and a recognition technique of the spoken word with the help of trained data is done. Here the approach followed to tackle the problem of poor quality in AL speech involves both speech enhancement and recognizer technique development. When it is looked as enhancement problem noise region localization, noise estimation and noise suppression methods were adopted. In the process of parameter enhancement, pitch frequency estimation and improvement is implemented. When it is looked as recognition problem the parameters pitch frequency, formant frequency, glottal excitation, spectral tilt, coefficients are extracted. As formant frequency is a sensitive parameter, its estimation was done using Recurrent Neural network. -
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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.