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An advanced variable temperature refrigerator for preservation and management of food items
All food items will have shelf life period. The main aim of food preservation is to maximize the shelf life period and preservation of nutrients for a long period. One of the preservation methods is refrigeration. Each food item will have its own optimum storage temperature to maximize the shelf life period. Normal refrigerators have fixed temperature. The work proposes a refrigerator with six compartments which is equipped with temperature sensors to maintain the fixed temperature for that compartment and with weighing sensors to monitor the depleting food items with the help of a controller. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Classification of Medicinal Plant Leaves using Deep Learning Algorithms
This research explores the automated leaf-based identification of medicinal plants, utilizing machine learning and deep learning techniques to address the crucial need for efficient plant classification. Driven by the vast potential of medicinal plants in pharmaceutical development and healthcare, the study aims to surpass the limitations of existing methodologies through thorough experimentation and comparative analysis. The primary goal is to develop a robust and automated solution for classifying medicinal plants based on leaf morphology. The methodology encompasses acquiring diverse datasets. Specifically, set 1 data is processed by applying resizing, rescaling, saturation adjustment, and noise removal, while Set 2 data is processed by applying resizing, rescaling, saturation adjustment, noise removal, and PCA (Principal Component Analysis). The proposed algorithms include Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), YOLOv8, Vision Transformer (ViT), ResNet, and Artificial Neural Networks (ANN). The study evaluates the efficacy and effectiveness of each algorithm in plant classification using metrics such as accuracy, recall, precision, and F1 score. Notably, the ResNet model achieved 93.8% and 94.8% accuracy in Set 1 and Set 2, respectively. The SVM model demonstrated 56.5% and 56.6% accuracy in Set 1 and Set 2, while the Vision Transformer (ViT) model achieved 84.9% and 74.4% accuracy in Set 1 and Set 2, respectively. The CNN model showcased high accuracy at 96.7% and 94.8% in Set 1 and Set 2, followed closely by the ANN model with 96.7% and 96.6% accuracy. Lastly, the YOLOv8 model achieved 96.0% and 95.1% accuracy in Set 1 and Set 2, respectively. The comparative analysis identifies CNN and ANN as the top-performing algorithms. This research significantly contributes to the advancement of medicinal plant identification, pharmaceutical research, and environmental conservation efforts, emphasizing the potential of deep learning techniques in addressing complex classification tasks. 2026, Modern Education and Computer Science Press. All rights reserved. -
Compressed Data Representation Methods for High-Speed Search
The current age of computing revolves around data; the ability to fetch and store large quantities of information has become imperative for systems such as embedded systems and even search engines. Methods of compressed data representation are vital, as they enable faster query execution while reducing the storage space needed. This paper has analyzed such methods. The authors have reviewed bitmap indexing, inverted index compression, succinct data structures, LZ-based schemes, and compressed tries based on the set criteria of practical usefulness, search performance, and space efficiency. Through qualitative metrics, the authors performed a comparative evaluation, which is then represented in a conceptual figure and through tables. Moreover, the paper analyzes potential use case scenarios in domains such as bioinformatics, log management, edge computing, and AI-powered search pipelines. Other issues that have been explored include a balance between compression and query latency, optimizing for heterogeneous hardware, encrypted data search, and searching through encrypted data. The findings illuminate previously unexplored areas of research, including learned indexing, adaptive compression, and searching with minimal energy expenditure. The Research Publication,. -
Gender-based spatial segregation: ladies compartments in the Mumbai local trains
The local trains of Mumbai are one of the most crowded means of public transport, but often preferred by many women due to the provision of exclusive ladies compartments reserved for women. While these compartments provide women a space safe from sexual assault, conflicts due to class and religious differences arise among the women who occupy this in-between space. Moreover, the continuous use of ladies compartments leads to the labelling of general compartment as gents, affects the interactions between men and women in different railway spaces, and also configures the public as already segregated according to a binary understanding of gender. Based on the findings from an ethnographic study of womens experiences of using the ladies compartments, I argue that the provision of segregation gives rise to specific mobility practices in the ladies compartments. The logic of segregation spills over to other railway spaces, and these can complicate our understanding of mobility as empowering for women travellers. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Travel time activities Mobility and femininities in the ladies compartments of the Mumbai local trains
Activities undertaken during everyday commutes have often been studied to prove the inherent value of travel time to the commuters. Women commuters using the ladies compartments of the Mumbai local trains use this time to eat and share food, shop, chitchat, and watch sitcoms on their phones. Undertaken in a gender-segregated space, these activities make womens mobility an avenue for the performance of their femininities. Thus, while the association between masculinity and mobility stands questioned, what merits enquiry is whether mobility for women is premised on their effective performance of hegemonic femininities. 2022 The Society for Ethnographic Theory. All rights reserved. -
Estimation of Vehicle Distance Based on Feature Points Using Monocular Vision
In this digital era safety and security have the highest precedence, the advanced driver assistance system is the latest trend and where many challenges are open for researchers. Vehicle to vehicle distance estimation is one of the most important challenges to provide the security and safety alerts for the driver. In order to achieve this, image of the front vehicle is captured using the single camera under monocular vision to estimate the vehicle distance. Then three key steps are designed to estimate the vehicle distance: extracting and locating the key features of the vehicle, characteristic triangle is drawn between those features to calculate pixel area and develop the measuring formula to calculate the distance. For efficient feature extraction and localizing of the feature position, conventional AdaBoost algorithm is utilized to find the strong features for scalable samples. Distance measurement formulation is used to derive the correlation between the pixel area and distance by considering the different parameters from the prototype of pinhole camera, camera standardization and plotting of area. Formula is developed to estimate the optimum moving distance between vehicles to vehicle. After the experimental analysis, the accuracy rate is improved and time complexity satisfies the precision. 2019 IEEE. -
Classification of Vehicle Make Based on Geometric Features and Appearance-Based Attributes Under Complex Background
Vehicle detection and recognition is an important task in the area of advanced infrastructure and movement administration. Many researchers are working on this area with different approaches to solve the problem since it has a many challenge. Every vehicle has its on own unique features for recognition. This paper focus on identifying the vehicle brand based on its geometrical features and diverse appearance-based attributes like colour, occlusion, shadow and illumination. These attributes will make the problem very challenging. In the proposed work, system will be trained with different samples of vehicles belongs to the different make. Classify those samples into different classes of models belongs to same make using Neural Network Classifier. Exploratory outcomes display promising possibilities efficiently. 2019, Springer Nature Singapore Pte Ltd. -
Classification of Vehicle Type on Indian Road Scene Based on Deep Learning
In Recent days an intelligent traffic system [ITS] is implemented on indian traffic sytem. Different applications are widely used to improvies the performance of the system. To improve the intelligence of the system deep learning can used to classify the vehicles into three different classes. The combination of Faster RCNN classifier and RPN can used to detect the objects and classify those objects into different classes. Analysis of the experimental results shows the improved accuracy and efficiency in classifying the vehicles on indian roads into different categories. 2021, Springer Nature Singapore Pte Ltd. -
Smart Vehicle Recognition System on Indian Roads Under Rainy Conditions
Recognition of vehicles under the different weather condition is very challenging. This work aims to recognize vehicles on Indian road in accordance with their visibility. It is important to recognize the surround roadside objects, particularly front and rare vehicles to avoid the accidents. Especially in raining conditions vehicle recognition is rate traffic surveillance cameras get decreases due to water droplets. Hence, we proposed a method for recognition of vehicles on road in rainy condition using image processing in computer vision techniques to improve the recognition rate. In the proposed method, an instance segmentation technique is used to segment the vehicles in Indian road scene and the visual noise and texture features are analysed and computed in the segmented images to recognize the vehicles more accurately in rainy conditions. By integrating the visual noise features with the texture feature and instance segmentation, the accuracy of vehicle recognition is improved. The experimental findings demonstrated that the suggested approach could more accurately predict the visibility of vehicles in rainy weather conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
ESG efficiency analysis in the IT industry: a DEA-based approach
Unlocking the power of sustainable growth, Environmental, Social, and Governance (ESG) principles are redefining the future of responsible investment and corporate excellence. ESG regulations ensure that organizations maintain sustainable development and improve non-monetary metrics, such as stakeholders engagement, customer satisfaction, market acceptability, societal ethics, and values. Higher ESG scores demonstrate commitment towards responsible business practices and indicate higher market value for companies, which are valid for all sectors, including IT. However, existing literature reveals that IT sector companies pay less attention to planning their operations to make them more sustainable. Therefore, IT firms must identify methods and practices to maintain high ESG scores to achieve sustainable growth. The current study leads the readers into a new area of ESG through the help of an advanced method, DEA. DEA (Data Envelopment Analysis) methodology has been used to identify the decision units relative efficiency scores and helps identify peers and followers based on ESG scores. The study reveals that among the selected IT firms using the output-oriented strategy, 56.25% experience increasing returns to scale, 18.75 per cent experience decreasing returns to scale, and the remaining 25.00 per cent report constant returns to scale. This indicates that most IT industry firms can generate greater output change in proportion to the input change. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
PMFRO: Personalized Mens Fashion Recommendation Using Dynamic Ontological Models
There is a thriving need for an expert intelligent system for recommending fashion especially focusing on mens fashion. As it is an area which is neglected both in terms of fashion and modelling intelligent systems. So, in this paper the PMFRO framework for mens recommendation has been put forth which indicates the semantic similarity schemes with auxiliary knowledge and machine intelligence in a very systematic manner. The framework intelligently creates mapping of the preprocessed preferences and the user records and clicks with that of the items in the profile. So, this model aggregates community user profiles and also maps the mens fashion ontology using strategic semantic similarity schemes. Semantic similarity is evaluated using Lesk similarity and NPMI measures at several stages and instances with differential set thresholds and the dataset is classified using the feature control, machine learning bagging classifier which is an ensemble model in order to recommend the mens fashion. The PMFRO framework is an intelligent amalgamation and integration of auxiliary knowledge, strategic knowledge, user profile preferences as well as machine learning paradigms and semantic similarity models for recommending mens fashion and overall precision of 94.68% and FDR of 0.06 was achieved using the PMFRO model. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Consumption of antibiotics as self-medication from over-the-counter purchase: An empirical study
Objective: The primary objective of this study is to find the reasons behind the practice of self-medication (SM) by the people with over-the-counter (OTC) drugs which are usually available in all medical stores. Methods: This article presents an empirical view of SM practice with OTC drugs. The research design of the study is descriptive, and the population of the study is customers who buy OTC drugs. The target population of this research is the common public who are all having the possibility to consume OTC drugs ever. The sampling technique used for this study is a systematic random sampling, and the sample size is 144. An instrument used for collecting data is a self-administered questionnaire and personal interview with the pharmacists. The data were analyzed using descriptive statistics. Results: The study results that most of the OTC customers consider SM is not a good practice, even though they practice SM of antibiotics in certain circumstances, due to reasons such as time-saving, convenience, cost saving, avoid waiting time to consult a doctor, easy and quick availability of antibiotics in neighbourhood drug stores, etc. Conclusion: The study concludes the reasons behind the SM practice and some remedies to overcome OTC drug-related problems from SM. 2017 The Authors. Published by Innovare Academic Sciences Pvt Ltd. -
Impact of Social Networking Sites on Academic Performance and Career through Collaborative Learning (with Reference to Students of Self-financing Engineering Colleges in Tiruchirappalli)
Use of Social Networking Sites (SNSs) is having a growing importance among students in their everyday life. It acts as an essential tool for them in higher level education. Among the social networking sites Facebook, WhatsApp, Twitter, LinkedIn is gaining more support from the student community. It helps the students to communicate each other directly like face to face interaction. SNSs are having an ability to induce students decision making capability. Based on this, the study tries to emphasize the ability of the SNSs to induce the students academic performance using collaborative learning among various groups in which they belong. The result reveals that collaborative learning will have a major impact on academic performance, i.e. 49.6% of the respondents stated that they are using SNSs for academic purpose. They use SNSs for sharing of information and study materials with their peers. SNSs help them to be interacting with their peers and teachers. Serials Publications Pvt. Ltd. -
Occupants perspective on building maintenance and workers maintenance performance in residential buildings - An empirical study
The entire life cycle of a building involves planning, design, construction, occupancy, operation, maintenance, demolition and removal of wastes. Maintenance is a never ending process in the life cycle of buildings which plays an essential role in building operation. To meet the changing needs of building construction, environment and technology, building maintenance is essential for improving the performance of buildings and for retaining the value and service life of buildings. The main purpose of this study is to establish a sound maintenance strategy for improving the building maintenance services in the areas of Tanjore district, Tamil Nadu which helps to improve occupants satisfaction to a desired level. The present study focuses on the factors influencing building maintenance and assessment of performance of building maintenance service providers. This study attempts to identify the parameters that strengthens and weakens the performance of building maintenance from the occupants perspective. An extensive survey has been conducted with 104 occupants who are residing in pertinent areas of Tanjore in order to determine the predominant factors that have causation effect on building maintenance and workers performance from the perspective of occupants. The research findings indicated that there exists a positive significant correlation exists statistically between performance of maintenance workers and effective functioning of residential buildings for comfort living of occupants. Serials Publications Pvt. Ltd. -
ESG efficiency analysis in the IT industry: a DEA-based approach
Unlocking the power of sustainable growth, Environmental, Social, and Governance (ESG) principles are redefining the future of responsible investment and corporate excellence. ESG regulations ensure that organizations maintain sustainable development and improve non-monetary metrics, such as stakeholders engagement, customer satisfaction, market acceptability, societal ethics, and values. Higher ESG scores demonstrate commitment towards responsible business practices and indicate higher market value for companies, which are valid for all sectors, including IT. However, existing literature reveals that IT sector companies pay less attention to planning their operations to make them more sustainable. Therefore, IT firms must identify methods and practices to maintain high ESG scores to achieve sustainable growth. The current study leads the readers into a new area of ESG through the help of an advanced method, DEA. DEA (Data Envelopment Analysis) methodology has been used to identify the decision units relative efficiency scores and helps identify peers and followers based on ESG scores. The study reveals that among the selected IT firms using the output-oriented strategy, 56.25% experience increasing returns to scale, 18.75 per cent experience decreasing returns to scale, and the remaining 25.00 per cent report constant returns to scale. This indicates that most IT industry firms can generate greater output change in proportion to the input change. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Arming Farmers with Smart Farming: The Future of Agriculture
Internet of Things (IoT) innovation is currently one of the growing fields across a diversity of industries, together with agriculture. IoT enhances our lives by making and promoting developments in a wide range of actions to encourage them to become more appropriate, practicality, and enhanced using suitable man-made recognition. Smart agricultural frameworks recognize a social trade toward more helpful, lower-cost agribusiness because of this innovation. The proposed work is to use IoT in the agriculture industry to collect real-time data (soil moisture, temperature, and so on) to help one look at a few climate scenarios from afar, efficiently, and greatly increase production. A global solution for monitoring and managing the agricultural field remotely has been proposed. Implementation of a local stand-alone field control unit that includes detection and activation capabilities. Developed a cloud solution for data storage, real-time monitoring, and historical data visualization based on the ThingSpeak cloud platform. Remote managing and control functions have been realized in both the local unit and the cloud using IoT infrastructure. 2022 IEEE. -
MADTRAS: Dataset for aspect-based sentiment analysis of movie reviews in Tamil
The rise of online platforms has led to a growing trend of people expressing their thoughts and emotions in their native languages. Movies have been a predominant topic of discussion on online platforms where people reflect on various aspects of movies. Aspect-based Sentiment Analysis (ABSA), a computational technique, assists in examining the sentiments hidden in these discussions. Two challenges arise when attempting to use ABSA to identify sentiments in movie reviews written in the Indian regional language Tamil; the former being the unavailability of potential Tamil movie review datasets and the latter being the difficulty that arises due to the agglutinative nature of Tamil Language. This work addresses the first challenge by curating an annotated movie review dataset in Tamil, MADTRAS (Dataset for Aspect-based Sentiment Analysis of Movie Reviews in Tamil). The quality of the dataset is ensured through content and annotation evaluation. To prove the efficiency of the dataset, the multilingual BERT (mBERT) was used, and the performance was compared with other Deep Learning(DL) models. 2025 The Authors -
Novel super stack passivation in AlGaN/GaN HEMT for power electronic applications
A super-stack passivation technique is proposed for an AlGaN/GaN HEMT in order to improve the breakdown voltage and cutoff frequency. The performance of the proposed technique is benchmarked against a conventional GaN HEMT. The analysis and investigation are carried out using Technology Computer-Aided Design (TCAD). The simulation results are validated with experimental data. It is observed that the breakdown voltage of the conventional and proposed devices is 356V and 449V, respectively. In contrast to the conventional device, the breakdown voltage of the proposed device is improved by 21%. This is the manifestation of the suppression of the electric field by the super-stack passivation technique in the proposed device. Furthermore, it is also observed that the Johnsons figure of merit in the proposed GaN-HEMT is also improved. 2024 The Author(s). Published by IOP Publishing Ltd. -
A Novel Investigation on p-GaN GATE with and without AlGaN Back Barrier for AlGaN/GaN High Electron Mobility Transistors
p-GaN layers are relatively mature and controllable, making p-GaN HEMTs the leading structure that is most likely to be commercialized. The analysis of the gate design parameters, such as transconductance, breakdown voltage, threshold voltage, Johnson Figure of Merit (JFOM), and gate turn-on current of p-GaN devices, which determine the on-state characteristics, needs to be investigated. The AlGaN barrier, p-GaN gate, GaN, AlGaN back barrier, and SiC substrate constitute the structure of p-GaN, which is operated in the E-mode. The use of an AlGaN back barrier reduces the punch-through current. Silicon carbide (SiC) is used as a substrate to have lower lattice mismatch with the nitride layer. The transfer characteristics, transconductance, threshold voltage, breakdown voltage, and JFOM are analyzed. The device demonstrates a positive threshold voltage that varies linearly with changes in ambient temperature. In addition, the device featuring an AlGaN back barrier shows a higher breakdown voltage of 105 V, in contrast to the device lacking a back barrier. 2025, Society for Communication and Computer Technologies. All rights reserved. -
HEES-Based IFVR for Energy-Saving Application Using DCDC Converter
The rapid response capabilities of high-conducting electromagnetic energy storage (HEES) devices are advantageous for mitigating sudden fluctuations in voltage and power. However, the cost of HEES coils significantly exceeds that of traditional battery energy storage solutions. To enhance the efficiency of energy use and diminish the costs associated with energy storage across multiline power distribution systems, this study presents an innovative approach involving an interline dc flexible voltage restorer (IFVR) configuration. This approach utilizes a single HEES coil connected to several compensating circuits. The innovation introduces a currentvoltage (VI) chopper assembly with multiple input/output power connections, enabling the connection of one HEES coil to various power lines. This setup ensures the independent management of energy exchanges for any compensated line. Importantly, when multiple power lines require compensation simultaneously, the HEES coil can be selectively activated to prioritize compensation based on the designated order of importance of the lines. The practicality of this method is confirmed through technical verification, demonstrating its ability to sustain transient voltage stability during voltage increases and decreases on multiple lines. These scenarios may arise from fluctuations in output voltage from power external supplies or variations in load demand from locally connected loads. 2021 IEEE.