Browse Items (9798 total)
Sort by:
-
Identifying the population of T-Tauri stars in Taurus: UVoptical synergy
With the third data release of the Gaia mission, Gaia DR3 with its precise photometry and astrometry, it is now possible to study the behavior of stars at a scale never seen before. In this paper, we developed new criteria to identify T-Tauri stars (TTS) candidates using UV and optical color-magnitude diagrams (CMDs) by combining the GALEX and Gaia surveys. We found 19 TTS candidates and five of them are newly identified TTS in the Taurus molecular cloud (TMC), not cataloged before as TMC members. For some of the TTS candidates, we also obtained optical spectra from several Indian telescopes. We also present the analysis of distance and proper motion of young stars in the Taurus using data from Gaia DR3. We found that the stars in Taurus show a bimodal distribution with distance, having peaks at 130.17-1.241.31 pc and 156.25-5.001.86 pc. The reason for this bimodality, we think, is due to the fact that different clouds in the TMC region are at different distances. We further showed that the two populations have similar ages and proper motion distribution. Using the Gaia DR3 CMD, we showed that the age of Taurus is consistent with 1Myr. 2023, Indian Academy of Sciences. -
Identifying Wage Inequality in Indian Urban Informal Labour Market: A Gender Perspective
This chapter elucidates the wage differential between male and female informal workers in urban labour market by using employment and unemployment survey 61st (2004-2005) round, 68th (2011-2012), and Periodic Labour Force Survey 2019-2020 data of National Sample Survey Office (NSSO) unit level data. This study found that gender inequality not only increased during getting job but also persists after getting job during wage distribution. Based on the Oaxaca-Blinder (OB) decomposition, it is revealed that gender wage inequality is more in the labour market due to the labour market discrimination, that is, unexplained components. Hence, this study helps researcher, policy makers and government to fix the gender wage discrimination issues exist in the Indian labour market. This will enhance economic growth through the rise of the women labour force participation. 2024 A. Vinodan, S. Mahalakshmi, and S. Rameshkumar. -
IDENTITIES AT THE DINNER TABLE: COMMENSALITY, SELF-PERCEPTION, AND RELATIONSHIPS IN ANNE CHERIANS A GOOD INDIAN WIFE
Food studies is rapidly gaining ground as a multidisciplinary area of research. Within it, literary food studies brings an interdisciplinary perspective as works of literature are viewed through the lens of food that is informed by frameworks and concepts that are rooted in a variety of fields including cultural anthropology, sociology, and more. one such concept that is in focus here is that of commensality that is associated with food and food practices. Commensality, drawing from notions of conviviality, refers to the practice of sharing a table and consuming food together. Deeper meanings of communal identities come to the fore in this social practice, leading it to shape how identities are understood and projected. Commensality can be a complex site of belonging and alienation depending on the context, and this paper seeks to explore the representation of the same in Anne Cherians A Good Indian Wife (2008). Leila, the titular Indian wife in the novel, moves to the US from India after her marriage to Neel and grapples with finding her place in the foreign land. With this displacement comes the endeavor to reaffirm her new identity, which now includes the role of being a wife and the aspect of being an immigrant. Neel also deals with complicated feelings towards the projection of his identity. With food playing a crucial role in the everyday experiences of their lives, commensality becomes a point of enquiry into how they see themselves and how their relationships with each other and themselves evolve through the course of the narrative. 2024 Nayana George. -
Identity in Consumption: Reading Food and Intersectionality in Anita Desai's Fasting, Feasting
With the resurging interest in Food Studies, this rapidly emerging field of study has seen multiple disciplines adding in their distinct flavours that truly make this an area to savour. Literary food studies, in particular, has become a relevant field of study with the understanding that food in literature always plays a symbolic role, as food in literature is never depicted for the sustenance of the literary characters. This paper seeks to explore the novel Fasting, Feasting (1999) by Anita Desai through the lens of food and foodways to explicate how the characters interact with the culinary arena, and ultimately, interact with each other and themselves. These interactions will serve as crucial insights into their identities, particularly their intersectional gender identities considering the facets of nationality, class, and the like. A special focus will also be rendered on the notion of marginalisation seen in the text, of which gender is a crucial deciding factor. The title of the novel hints at consumption-at both its presence and absence-which will prove as the gateway to the interactions of the characters with food in the novel to examine who it is that gets to feast while who are forced to starve. 2022 Aesthetics Media Services. All rights reserved. -
Identity-based message authentication scheme using proxy vehicles for vehicular ad hoc networks
Message authentication verifies the identity of the sender vehicle, ensuring it in between vehicles and Road Side Units (RSU) is an essential part of Vehicular Adhoc Networks. Signature verification in RSUs will be troublesome if a large number of vehicles enters in its region at the same time. In such cases the efficiency of the RSUs will be affected due to high computational overhead. To address this issue, proxy vehicle based message authentication scheme (ID-MAP) is proposed by Asaar et al. (ITVT 67: 5409, 2018). It uses proxy vehicles to reduce the overheads of the RSU by verifying multiple messages at the same time. Even though it deals with the efficiency issues of RSU, the computational cost of signature generation is high. Since the ability of a vehicle to act as a proxy vehicle is based on the number of signed messages, it has a major impact. It also cannot guarantee privacy preservation and hence it is insecure against attacks based on privacy preservation. It also has other drawbacks like storage issues and high overheads. Hence, a new identity based message authentication using proxy vehicles is proposed in this paper. Elliptic Curve Cryptography based scheme is used without pairings for message authentication. Proxy vehicles will verify multiple messages from vehicles through batch verification and send the result to the RSU. The identity of multiple proxy vehicles will be verified by RSU, it can also cross check the correctness of the received result. Thereby RSUs can verify a large number of messages at the same time with the help of proxy vehicles. Security analysis shows that if each proxy vehicle verifies 300 messages of its neighbor vehicles, then with the help of proxy vehicles an RSU can verify 226,244 messages per second which is 40% less than that of ID-MAP scheme. It also shows that the computational cost to generate a signature in the proposed scheme is 50% less than that of ID-MAP scheme. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Ideological preferences versus national integration of India
[No abstract available] -
Idiosyncratic Deals: Understanding Effect of Intrinsic and Extrinsic Motivation I-Deals on Innovative Work Behaviour
I-deals or Idiosyncratic deals are specialised, adaptable work patterns by mutual agreement between employees and their managers to meet demands of a dynamic work place. Innovative work behaviour also known as IWB is referred to as the employee behaviour that intends to create and introduce novel and valuable products, processes, innovations and ways of working within a job-role or work-group of an organization. This research discovers the connection between various types of intrinsic and extrinsic motivational deals such as the work responsibility idiosyncratic deals, flexibility deals and financial ones and innovative behaviour, specially within the purview of the working women. It also provides an overview on the outcome of these deals on innovation at a workplace. Our study adopted descriptive research to assess the association of Idiosyncratic deals with IWB using a quantitative study across 352 female employees of Indian Corporate sector. It was found that there exists a direct and positive association amid intrinsically and extrinsically motivated Idiosyncratic deals and an innovative mind-set, in the context of Indian IT sector. This study establishes the influence of idiosyncratic deals and the motivational factors within them in driving an innovative mind set. Thus, the study helps to recognize the value that I-deals brings in establishing an effective innovative environment for employees playing a vital role in the growth of the organization. 2024, Iquz Galaxy Publisher. All rights reserved. -
IDS for Internet of things (IoT) and Industrial IoT Network
The Internet of Things (IoT) is a swiftly increasing domain of interconnected gadgets, technologies, and structures that may be achieved in a small, tightly associated environment or can travel across big geographic areas, including Smart Cities. IoT devices are increasingly deployed for numerous goals inclusive of records sensing, accumulating, and controlling. The IoT enhances user affairs by permitting a huge variety of smart gadgets to link and possible information. IoT gadgets are hastily evolving universally while IoT offerings have become pervasive. IoT devices include a big assortment of devices, along with small, embedded sensors, AI assistants, digital cameras, and so on, which can be found in various backgrounds, i.e., Smart Homes, Smart Communities, and Smart Cities. Smart Cities have developed into intriguing areas with technologies consisting of traffic-conscious streetlights which dynamically react to emergencies by editing site visitors styles. Moreover, with the adoption of 5G networks, technologies and techniques throughout towns have become blended. This persevered improvement of IoT advocated the expansion of sophisticated and complicated systems which appreciably adjust the community. However, these technologies have guided to a brand new threat to the security of grids. Many present-day malware assaults, targeted at classic computer systems linked to the Internet, will also be required for IoT gadgets. With those enhancements, malicious actors have found new methods to control their weaknesses. One of the biggest cyber-attacks in instances of terabits in step with 2d operated, infected IoT gadgets harmonized within a botnet provides a massive DDoS assault which disrupts the Internet range for large geographic regions. This attack underlines the increasing hazard posed via uncertain IoT devices. Moreover, attacks that include those are evolving as greater threats as a larger quantity of exposed gadgets is introduced to networks throughout the globe. Their actions are anomalous and higher are the numbers of hazards and assaults toward IoT devices. Cyber-attacks arent new to the IoT, however as the IoT may be deeply interwoven in our lives and societies, traditional protection resolutions are inadequate when managing these dangers. Oftentimes, safety answers are created to run locally on host appliances, i.e., antivirus software, or as standalone machines (i.e. community firewalls and intrusion detection structures (IDSs). However, the IoT has obtained a clean set of community protocols, together with Zigbee, Ant+, and 6LoWPAN, that traditional safety solutions, such as rule-primarily based firewalls and host-based total antivirus software programs, had been not equipped with or have no longer been revised to account for. Moreover, many IoT gadgets suffer from computational, storehouse, or network situations. Due to those constraints, IoT safety answers, especially an IoT IDS, must be lightweight enough, in phrases of the computational, garage, and networking resources, to be living on the devices but sturdy enough to accurately hit upon potential intrusions. Therefore, a holistic method needs to be regular while coming to IoT intrusion detection. IoT devices cant be considered in a vacuum as self-contained machines due to the fact a totally fledged, modern protection answer is just too aid-annoying for constructing on those gadgets. The normal safety of the network necessitates IoT gadgets to be included as associates within a security answer rather than as man or woman nodes. Therefore, green protection of IoT devices could keep millions of net customers away from malicious moves. However, present malware detection techniques are afflicted by excessive computational complexity. Hence, theres a real necessity to protect the IoT, which has therefore resulted in a requirement to completely recognize the threats and assaults in an IoT infrastructure. 2024 selection and editorial matter, Mayank Swarnkar and Shyam Singh Rajput; individual chapters, the contributors. -
IEEHR: Improved Energy Efficient Honeycomb Based Routing in MANET for Improving Network Performance and Longevity
In present scenario, efficient energy conservation has been the greatest focus in Mobile Adhoc Networks (MANETs). Typically, the energy consumption rate of dense networks is to be reduced by proper topological management. Honeycomb based model is an efficient parallel computing technique, which can manage the topological structures in a promising manner. Moreover, discovering optimal routes in MANET is the most significant task, to be considered with energy efficiency. With that motive, this paper presents a model called Improved Energy Efficient Honeycomb based Routing (IEEHR) in MANET. The model combines the Honeycomb based area coverage with Location-Aided Routing (LAR), thereby reducing the broadcasting range during the process of path finding. In addition to optimal routing, energy has to be effectively utilized in MANET, since the mobile nodes have energy constraints. When the energy is effectively consumed in a network, the network performance and the network longevity will be increased in respective manner. Here, more amount of energy is preserved during the sleeping state of the mobile nodes, which are further consumed during the process of optimal routing. The designed model has been implemented and analyzed with NS-2 Network Simulator based on the performance factors such as Energy Efficiency, Transmission Delay, Packet Delivery Ratio and Network Lifetime. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
IIRM: Intelligent Information Retrieval Model for Structured Documents by One-Shot Training Using Computer Vision
Various information retrieval algorithms have matured in recent years to facilitate data extraction from structured (with a predefined template) digital document images, primarily to manage and automate different organizations invoice and bill reimbursement processes. The algorithms are designated either rule-based or machine-learning-based. Both approaches have respective advantages and disadvantages. The rule-based algorithms struggle to generalize and need periodic adjustments, whereas machine learning-based supervised approaches need extensive data for training and substantial time and effort for manual annotation. The proposed system attempts to address both problems by providing a one-shot training approach using image processing, template matching, and optical character recognition. The model is extensible for any structured documents such as closing disclosure, bill, tax receipt, besides invoices. The model is validated against six different structured document types obtained from a reputed title insurance (TI) company. The comprehensive analysis of the experimental results confirms entity-wise extraction accuracy between 73.91 and 100% and straight through pass 81.81%, which is within business acceptable precision for a live environment. Out of total 32 tested entities, 17 outperformed all state-of-the-art techniques, where max accuracy has been 93 % with only invoices or sales receipts. The system has been set operational to assist the robotic process automation of the TI mentioned above based on the experimental results. 2022, King Fahd University of Petroleum & Minerals. -
ILeHCSA: an internet of things enabled smart home automation scheme with speech enabled controlling options using machine learning strategy
Nowadays, communication schemes and the related automation logics have improved drastically, and people are moving from classical to intelligent applications. This naturally raises the growth ratio of the automation industry and enables researchers to work accordingly. The field of automation is essential in specific unavoidable environments such as hospitals, industrial units, individual residences, disaster areas, etc. In this paper, a novel machine-learning enabled speech-based home automation system is designed, called Intelligent Learning-enabled Home Controlling with Speech Assistance (ILeHCSA). This scheme integrates several latest technologies to control the home intelligently, including machine learning, speech assistance technology, and Internet of Things (IoT) support. Based on these advanced technologies, the logic of smart home automation systems has been designed in this approach, and it provides intellectual home controlling options to people. The following are the devices and sensors which are essential to control the electronic devices embedded into the home environment: Node Microcontroller Unit (MCU) Wi-Fi enabled Microcontroller, Relay Unit, Voice Capture Module with Mic, Speech-to-Text (STT) Converter Module, and Global Positioning System (GPS) to identify the location of the device. The machine-learning logic is utilized to provide a statistical analysis of device usage and to provide a clear summary and traces to maintain the device accordingly. These smart technologies can innovatively change the living atmosphere with sufficient support and comfort. The main intention of this paper is to provide a robust home automation system to support people efficiently, especially the people who are physically suffering from illness and the aged ones. The proposed work provides a 96.5% accuracy ratio when compared with other methods. 2021 Nismon Rio Robert et al. -
IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques
In recent years, there has been a significant increase in the number of people suffering from eye illnesses, which should be treated as soon as possible in order to avoid blindness. Retinal Fundus images are employed for this purpose, as well as for analysing eye abnormalities and diagnosing eye illnesses. Exudates can be recognised as bright lesions in fundus pictures, which can be the first indi-cator of diabetic retinopathy. With that in mind, the purpose of this work is to cre-ate an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis (IM-EDRD) with multi-level classifications. The model uses Support Vector Machine (SVM)-based classification to separate normal and abnormal fundus images at the first level. The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix (GLCM). Furthermore, the presence of Exudate and Diabetic Retinopathy (DR) in fundus images is detected using the Adaptive Neuro Fuzzy Inference System (ANFIS) classifier at the second level of classification. Exudate detection, blood vessel extraction, and Optic Disc (OD) detection are all processed to achieve suitable results. Furthermore, the second level processing comprises Morphological Component Analysis (MCA) based image enhancement and object segmentation processes, as well as feature extraction for training the ANFIS clas-sifier, to reliably diagnose DR. Furthermore, the findings reveal that the proposed model surpasses existing models in terms of accuracy, time efficiency, and precision rate with the lowest possible error rate. 2023, Tech Science Press. All rights reserved. -
Image Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks
Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. 2023 IEEE. -
Image and signal processing in the underwater environment
To handle submerged action recognition, researchers must first understand the fundamental principles of photonic crystals mostly in the liquid phase. Deterioration effects are produced by the mediums physical attributes, which are not present in typical pictures captured in the air because light is increasingly reduced as it passes through water, submarine pictures are characterized by low readability. As a consequence, the sceneries are poorly contrasting and murky. Its vision capability is limited to approximately twenty meters in clear blue water and five meters or less in muddy water due to light dispersion. Absorbing (the removal of incident light) and dispersion are the two factors that produce light degradation. So the actual quality of submersible digital imaging is influenced by the destructive interference processes of light in water. Longitudinal scattered (haphazardly diverted light traveling from objects to the cameras) causes picture details to be blurred. 2021, SciTechnol, All Rights Reserved. -
Image contrast enhancement by scaling reconstructed approximation coefficients using SVD combined masking technique
The proposed method addresses the general issues of image contrast enhancement. The input image is enhanced by incorporating discrete wavelet transform, singular value decomposition, standard intensity deviation based clipped sub image histogram equalization and masking technique. In this method, low pass filtered coefficients of wavelet and its scaled version undergoes masking approach. The scale value is obtained using singular value decomposition between reconstructed approximation coefficients and standard intensity deviation based clipped sub image histogram equalization image. The masking image is added to the original image to produce a maximum contrast-enhanced image. The supremacy of the proposed method tested over other methods. The qualitative and quantitative analysis is used to justify the performance of the proposed method. 2015 The Science and Information (SAI) Organization Limited. -
Image Pre-Processing Algorithms for the Quality Detection of Tea Leaves
This Identification and prediction of the tea quality is the essential research focus nowadays in the field of agriculture. Nowadays the Artificial Intelligence has become the latest topic in the region of pattern recognition. The various combination and permutation of the different techniques has resulted in proper solving the problem as well as have better accuracy in recognition. Therefore, there is urge need of a detailed survey AI techniques used for the identification of the tea leaf quality for the different grades of tea plants. In this paper, we aim on the various methods used for the pre- processing of the input image to extract the processed image which will further be useful for the feature extraction and the classification of the proposed image. It is very important to get the effective and accurate processed data which will further act as an input for the next level modules. This paper shows various methods of edge detection are applied on the image like Canny, Sobel and Laplacian are used. The further results are compared for quality metrics parameters such as the Mean Square Error (MSE) & Structural Similarity Index Metric (SSIM). The main agenda of this paper is to perform the edge detection and to check the quality measure of the processed image. The software used here is python. 2022 IEEE. -
Image Processing and Artificial Intelligence for Precision Agriculture
Precision agriculture is a novel approach to increase the productivity of crops that employs recent technologies such as Artificial Intelligence, WSN, cloud computing, Machine Learning, and IoT. This paper reviews the development of different techniques effectively used in precision agriculture. The paper details the technological impact on precision agriculture followed by the different image processing schemes such as Satellite imagery and unmanned aerial vehicle (UAV). The role of precision agriculture is disease detection, weed detection from UAV images, and detection of trees and contaminated soils from satellite imagery is discussed. It reviews the impact of artificial intelligence (AI) namely machine learning &deep learning in precision agriculture. The performance of the recent image processing schemes in precision agriculture is analyzed. The paper also discusses the challenges that exist in implementing the precision agriculture system. 2022 IEEE. -
Image Recognition, Recusion Cellular Classification Using Different Techniques and Detecticting Microscopic Deformities
Deep convolutional neural networks (CNNs) have turn out to be one of the most advanced approaches trendy distinguishing snapshots in extraordinary fields. White blood cell classification is crucial for diagnosing anaemia, leukaemia, and a variety of other hematologic illnesses. Transfer learning with CNNs is frequently used in biological image categorization. Traditional methods for WBC classification is costly is terms of time and money. In the paper three convolutional neural network architectures are proposed which is based on transfer learning for microscopic image classification and compare the performance of models. The paper compares Transfer learning models like VGG-16, VGG-19, VGG-19 SVM hybrid and AlexNet. VGG-16 gives the best classification performance in comparison. VGG-16 model is which has a train accuracy of 0.9538 and train loss of 0.1322. 2022 IEEE. -
Image Steganography Using Discrete Wavelet Transform and Convolutional NeuralNetwork
The practice of steganography involves concealing messages within another thing, which is referred to as a carrier. Is thus performed in order to build up a covert communication channel in a rather way that any observers whom has access to such a channel will not be able to detect the act of communication itself. In this research, using the process of stenography, a secret text is transferred across a communication channel using an image as a cover. Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) is used in the above process. The encoding and decoding operation is done by using DWT while the preprocessing and training of images is done by CNN. The training and prediction rate of CNN is 72.4 %. 2022 IEEE. -
Imagining the sustainable future with Industry 6.0: A smarter pathway for modern society and manufacturing industries
Industry is defined as the production of goods and services through the transformation of raw materials and resources into valuable products. It involves the creation of finished products or services through various stages of production that may include manufacturing, processing, assembly, packaging, and distribution. Industries have played a significant role in the economic growth and development of nations throughout history. They have contributed to the creation of employment opportunities, the development of new technologies, and the improvement of living standards. Over the years, the industrial sector has gone through numerous changes, and each of these changes has been termed as an "Industry Revolution." 2024, IGI Global. All rights reserved.