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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. -
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. -
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. -
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 -
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. -
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 -
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. -
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. -
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. -
Evaluating Building Damage Classification Accuracy: A Benchmarking Study of UNet
Building damage classification must be done accurately and quickly in order to support disaster response and recovery activities. Deep learning models, particularly U-Net, have demonstrated strong potential in automating damage assessment from satellite and aerial imagery. This study benchmarks the accuracy of U-Net in classifying building damage across multiple datasets, evaluating its performance against ground truth labels. Key factors such as data preprocessing, augmentation techniques, and model variations are analyzed to determine their impact on classification accuracy. The results provide insights into the strengths and limitations of variations in U-Net for damage assessment, highlighting areas for improvement and future research directions 2025 IEEE. -
An Optimized Algorithm for Selecting Stable Multipath Routing in MANET Using Proficient Multipath Routing and Glowworm Detection Techniques
Mobile Ad Hoc Networks (MANETs) depend on the selected and constant path with an extended period and the flexibility of the battery power condensed in searching end nodes, leading to numerous link failures. This kind of link damages occurs, and it also affects the packet success rate. We presented a Proficient Multipath Routing and Glowworm detection (PMGWD) technique to overcome such a Manets failure. Initially, a proposed Proficient Multipath Routing (PMR) technique identifies the damaged or failure routes and continues communication inefficiently. Secondly, the Glowworm detection node technique is implemented for both fault node identification and for extending the nodes network lifetime. Another reason to select the glowworm optimization is to update the node based on the glow to improve its neighbor its search space. Lastly, the PMGWD technique is utilized for identifying an optimal route and fault nodes in the manet. It is achieved to correct the identification of fault nodes using the glowworm detection node technique, and it helps to explore more paths for the optimal route by using proficient multipath routing. Hence, this proposed PMGWD technique is used to perform a problem-free communication process in a network system. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Optimization and Design of a Sustainable Industrial Grid System
Electricity is a multifaceted form of energy and is used globally, with a continuously growing demand. Electrical power grids are there for more than 150 years. The generated electrical power is delivered to different industrial, commercial, and residential sectors, thereby fulfilling the ever-growing demand. In this research paper, the design and optimization of an industrial grid for various electrical loads is discussed. The electrical grid ensures a stable power supply to the loads by providing quality power with the minimum total harmonic distortion (THD) possible. A complete study of the short circuit current has been done in two different electrical grid systems, as it is seen that the short circuit current depends on the impedance of the transformer which feeds the load. These two designs of a single diagram will be simulated by using a power system analyzer, the Electrical Transient Analyzer Program (ETAP) software. The different electrical parameters, like choosing the optimised rated generator, cables, and transformers, are done. Load flow analysis is performed on both the design to evaluate the THD, short circuit fault, as well as to choose the right protection circuit for the system. 2022 Samat Iderus et al. -
2D Photonic Crystal for the Detection of Infectious Virus and Bacterial Diseases
In this paper, photonic crystal (PhC) sensor for analysis and modelling for viral and bacterial detection is proposed. Optical biosensors detect cancer, Bacillus cereus, malaria, typhoid, tuberculosis, etc. Optical biosensors work by shifting the peak resonance wavelength with modest refractive index changes. Because viral pathogens rapidly mutate and replicate in the human cell nucleus, sensors that offer accurate results for viral and bacterial diseases in seconds are in high demand. Hence, optical biosensors provide fast, sensitive results. The sensor detects influenza H1N1, hepatitis B (HBV), and typhoid, respectively. A maximum sensitivity of 443.33nm/RIU with a quality factor of 1309 is obtained. Simulations are performed using finite-difference time-domain (FDTD). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Integrated photonic devices for cancer detection
[No abstract available] -
Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence
In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brain function is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer's disease, Schizophrenia and Parkinson's diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm (DBNJZZ) approach. The suggested approach is executed and tested by using the performance metric measure such as accuracy, root mean square error, Mean absolute error and mean absolute percentage error. Proposed DBNJZZ gives better performance than previously available methods. 2023 Authors. All rights reserved. -
Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model
With the explosion of digital images across multiple sectors like social media, health care, medical imaging, and remote sensing, there is a demand to optimise the storage and transmission of images. In this paper, a novel Structural Fidelity Weighted Ensemble model is proposed to dynamically adjust the weights between SVD and PCA outputs to enhance the quality of reconstructed images.Unlike traditional static fusion techniques, the proposed SFWE deploys a fast bounded scalar optimization strategy so as to dynamically estimate the optimal fusion weights thereby ensuring non-negativity and simplex constraints while significantly reducing computational overhead compared to Sequential Quadratic Programming(SQP) or constrained gradient descent methods.Validation was done across multiple benchmarks datasets namely, USC-SIPI Sequences (grayscale TIFF), Kodak, BSDS500, DRIVE (Digital Retinal Images for Vessel Extraction), and ISPRS Potsdam which cover natural, medical, and remote-sensing images. Per-image processing, runtime measurement, and compressed ratio (CR) were produced automatically by the provided evaluation pipeline;The SFWE method provides greater image quality and structural fidelity across diverse datasets, attaining a PSNR of 40 dB and SSIM of 0.95, outperforming existing approaches such as Discrete Cosine Transform (DCT), Wavelet Transform, Singular Value Decomposition (SVD), and Principal Component Analysis and JPEG2000 + CNN models. In addition, it also maintains a good compression ratio leading to an effective balance between the reduction in file size as well as visual quality of the images, which confirms enhanced structural preservation across diverse image types. To implement a novel ensemble model (SFWE) that optimally balances the outputs of SVD and PCA for doing effective image compression. To achieve a higher SSIM (0.95) and good PSNR (40 dB) compared to compression techniques such as DCT, Wavelet, SVD, PCA, and JPEG2000 + CNN. To ensure adaptive high-quality reconstruction across multiple datasets, demonstrating its suitability for diverse image-intensive applications. 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ -
IOT based intelligent traffic management system /
Patent Number: 202131062286, Applicant: Bikas Mondal.
As the population grows and there are more cars on the roads, a large number of people may visit a site. It may be difficult for those who work there to keep track of everyone who comes in. Cloud-based interconnection between vehicles will benefit traffic cops by allowing them to monitor traffic and flow patterns without having to get out and do anything. Customers with valid driver's licences can only use a vehicle with an automatic ignition that is solely based on biometrics. -
Enhancement of efficiency of military cloud computing using lanchester model
Cloud computing is a technology that uses centrally processed computing resources over the Internet by a large number of users. Because many requests are concentrated on cloud servers, they must be properly distributed to avoid degradation of quality. Load balancing categorizes requests from users according to established algorithms and assigns appropriate virtual machines. Because load balancing algorithms are developed according to the cloud's usage environment, various algorithms are being utilized. Recently, government agencies are also interested in introducing cloud technologies beyond private sectors. Many militaries have selected Cloud as its basic task to apply new technologies such as AI to military operations. However, there is no precedent for military cloud development, and the lack of doud technology research considering the operational environment has delayed the progress of cloud adoption. The algorithm presented by this paper makes the combat power, which varies according to the importance of the operation, an important variable. This variable makes each user's access to computing resources different. Although similar to other dynamic algorithms, the impact of priorities is so big that the degree of imbalance between tasks was higher. 2020 IEEE. -
Fuzzy Logic Based Energy Storage Management for Parallel Hybrid Electric Vehicle
For the parallel hybrid electric vehicle, the various control strategies for energy management are illustrated with the implementation of fuzzy logic. The controller is designed and simulated in two modes for the economy and fuel optimisation. In order to manage the energy in HEV with three separate energy sources - batteries, Fuel cell and a supercapacitor system, - this article intends to create a fuzzy logic controller. By considering a complete system, the operating efficiency of the components need to be optimized. the control strategy implementation will be performed by the forward-facing approach. The fuel economy is optimised by maximising the operating efficiency in this strategy while other strategies does not have this extra aspect. The ability controller for parallel hybrid vehicles is mentioned in this research to enhance fuel economy. Although the earlier installed power controllers optimise operation, they do not fully utilise the capabilities. Hybrid vehicles can be equipped with a variety of power and energy sources such as batteries, internal combustion engines, fuel cell systems, supercapacitor systems or flywheel systems. The Authors, published by EDP Sciences, 2024. -
Transforming Customer Relations: Emotional AI and Behavioural Insights as Strategic Enablers in the Automotive Industry
This research examines how emotional AI deepens customer relations within the American automotive industry and how behavioural insights (BLI) mediate this process. Underlying framework: In the automotive industry, prioritising customer needs is crucial. Using emotional AI that analyses emotions and behavioural patterns can contribute to customer loyalty and satisfaction. The research employs a quantitative survey research approach. The sample pool comprised 237 customer experience managers representing various automotive companies in Texas. Emotional AI, business intelligence (BI), and consumer research are assessed using survey questionnaires, and the responses are recorded electronically. Descriptive regression and correlation analysis methods are employed to understand the connections between emotional AI, BLI, and customer relations. Evidence: The survey results indicated a positive relationship between the developed emotional AI and BLI, highlighting aspects of customer relations, including satisfaction and trust levels. The researchs findings suggest that emotional AI and BI could be a strategic intervention for enhancing customer loyalty. 2026 Haitham M. Alzoubi and Shanmugan Joghee and 2026 The authors.