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I Refuse to Die: Remembering G N Saibaba's Rhetoric of Resistance
The inhuman and brute force that G N Saibaba suffered during and after his arrest makes one question the non-enforcement of the legal rights of a disabled person. 2025 Economic and Political Weekly. All rights reserved. -
I Was One of Them: The Journey of Return Migrants Becoming Subagents in Keralas Recruitment Networks
Kerala has long been a prominent hub for international labour migration, with a unique phenomenon of return migrants transitioning into subagents supporting blue-collar workers navigating recruitment networks. These returnees leverage their personal migration journeys to address challenges faced by prospective migrants. Yet, little is known about what motivates them to assume this role and how they contribute to the broader migration process. This qualitative study examines the lived experiences of return migrants turned subagents, shedding light on their motivations and the ripple effects of their work on migration systems. Drawing on 22 in-depth interviews, the findings reveal three interconnected themes: (1) utilizing lived migration experiences to build empathy and trust; (2) creating a bridge between aspirants and recruitment agencies through cultural and procedural expertise; and (3) fostering a sense of purpose by addressing systemic challenges they once faced. These insights demonstrate how return migrants unique positionality enhances recruitment efficiency and reliability, offering micro-level support to migrants and meso-level improvements to recruitment systems. This study contributes to the growing literature on migration facilitation by highlighting the transformative role of return migrants in reshaping recruitment practices, with implications for migration policy, workforce mobility, and sustainable recruitment frameworks. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
I Woke Up Already Hurting: Postcolonial Affect in Tanya Tagaqs Split Tooth
Indigenous writing with postcolonial themes foregrounds the erasure and marginalization that result from colonialism. The genre-disrupting, coming-of-age novel Split Tooth (2018) by Inuit author Tanya Tagaq explores the personal and public life of a young Inuk woman from one of the Indigenous communities in the Canadian Arctic region. Split Tooth focuses on themes like the disappearances and deaths of Indigenous women, Inuit cultural settings, sexual assault, precarity, and violence. The novel meanders through emotions such as fear, shame, and grief, and can be analyzed through the theoretical framework of postcolonial affect. Postcolonial affect primarily examines the diverse emotional states of the colonized as indicators of the crisis that arises from colonization. The objective of the analysis is to highlight the delineation of affect in Split Tooth, as Tagaq blends the personal and the political in her narrative. Postcolonial affect is used for the theoretical examination of appropriation and violence that constitute the precarity of Inuit people, particularly women. 2025, Faculty of Philology, University of Bialystok. All rights reserved. -
IBA Graph Selector Algorithm for Big Data Visualization using Defence Dataset
International Journal of Scientific & Engineering Research Vol.4,Issue 3 pp. 1-5 ISSN No. 2229-5518 -
ICT as a driver of women's social and economic empowerment
The role of information and communication technologies as a tool for development has attracted the sustained attention of various agencies worldwide. If the gender dimensions of information and communication technologies-in terms of access and use, capacity-building opportunities, employment, and potential for empowerment-are explicitly identified and addressed, information and communication technologies can be a powerful catalyst for the political and social empowerment of women and the promotion of gender equality. ICT as a Driver of Women's Social and Economic Empowerment contributes to the growing body of literature and present state of knowledge by offering evidence on how new information and communication technologies impact women's economic and social empowerment and overall welfare creation leading to inclusive growth. Covering key topics such as economics, entrepreneurship, digital technologies, and inclusion, this premier reference source is ideal for industry professionals, policymakers, administrators, business owners, managers, researchers, academicians, scholars, practitioners, instructors, and students. 2023 by IGI Global. All rights reserved. -
ICT Policy Reforms for Innovation and Economic Development: A Comparative Study of India and China
The widespread adoption of Information and Communication Technologies (ICTs) has become essential for economic and social growth across the world. This paper aims to examine the impact of ICT policies and reforms on the level of economic development and adoption of ICTs in two countries, India and China. Previous studies have shown the positive impact of ICT adoption on economic growth, productivity, and innovation. However, the effectiveness of specific policy measures in promoting ICT adoption and economic development remains ambiguous to the users of ICT. This paper presents a comparative analysis of the ICT policies and reforms implemented in India and China from 2010 to 2021 and their impact on GDP per capita and internet usage. The study aims to identify and analyze the key ICT policies and reforms implemented in the two countries and examine their impact on economic development. The data for this study have been collected from the World Bank indicators database. The sample consists of the two fastest-growing economies in the world, India and China. The data analysis involves conducting descriptive statistics, correlation, and regression analysis to examine the relationship between ICT policies and reforms and their impact on GDP per capita, internet usage, and research and development expenditure. The findings of this study will contribute to the existing literature on the relationship between ICTs and economic development and provide insights into the policy measures that can promote ICT adoption and economic growth in different contexts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Ideal co-secure domination in graphs
A set S ? V of a graph G = (V, E) is a co-secure dominating set if for every u ? S, there exists v ? V \ S such that uv ? E and (S \ {u}) ? {v} is a dominating set of G. The minimum cardinality of a co-secure dominating set of G is the co-secure domination number and is denoted by ?cs(G). In this paper we initiate the evaluation of a domination parameter known as the ideal co-secure domination and is defined as follows: A set D ? V is an ideal co-secure dominating set of a graph G = (V, E) if for every u ? D and for every v ? V \ D such that uv ? E, (D \ {u}) ? {v} is a dominating set of G. The minimum cardinality of an ideal co-secure dominating set of G is the ideal co-secure domination number and is denoted by ?ics(G). We look to determine the ideal co-secure domination number of some families of standard graphs and obtain sharp bounds. We also provide the conditions necessary for the trees to have ideal co-secure domination number equal to n - 2. 2020 Author(s). -
Idealised Bilinear Moment-Curvature Curves of Reinforced Masonry (RM) Walls
In this paper, an analytical investigation of the axial loadflexural strength interaction of reinforced masonry walls is carried. The curvature ductility of masonry walls is evaluated for walls with different modes of reinforcement configurations under different levels of axial loads. An analytical expression for evaluating the curvature ductility of masonry walls at varying axial loads is proposed in this paper. Value of curvature ductility obtained from the proposed expression is compared with existing methods. Results indicate the proposed model can be used to determine the ductility of reinforced masonry walls. 2020, Springer Nature Singapore Pte Ltd. -
Identification and structure-activity relationship studies of small molecule inhibitors of the human cathepsin D
Cathepsin D, an aspartyl protease, is an attractive therapeutic target for various diseases, primarily cancer and osteoarthritis. However, despite several small molecule cathepsin D inhibitors being developed, that are highly potent, most of them show poor microsomal stability, which in turn limits their clinical translation. Herein, we describe the design, optimization and evaluation of a series of novel non-peptidic acylguanidine based small molecule inhibitors of cathepsin D. Optimization of our hit compound 1a (IC50 = 29 nM) led to the highly potent mono sulphonamide analogue 4b (IC50 = 4 nM), however with poor microsomal stability (HLM: 177 and MLM: 177 ?l/min/mg). To further improve the microsomal stability while retaining the potency, we carried out an extensive structureactivity relationship screen which led to the identification of our optimised lead 24e (IC50 = 45 nM), with an improved microsomal stability (HLM: 59.1 and MLM: 86.8 ?l/min/mg). Our efforts reveal that 24e could be a good starting point or potential candidate for further preclinical studies against diseases where Cathepsin D plays an important role. 2020 Elsevier Ltd -
Identification of ambulance in traffic videos using image processing techniques
Traffic congestion is one of the commonly faced problems in the Urban areas. To eliminate these problems, there is a need for an Intelligent Transportation System (ITS) that proposes an efficient method to reduce the traffic problems and introduces the priority system for the Emergency vehicles. This paper proposes two frameworks that identify ambulance in traffic videos based on features such as color, siren and text. Frames are extracted from videos to employ methods like multilevel thresholding and region matching. Multilevel thresholding is used for segmenting the ambulance from the other occurring vehicles based on the white color. Region matching for text detection method is employed in the segmented vehicle. Color space thresholding is used for the detection of siren based on red or blue color feature. Optical character recognition (OCR) is employed to extract the text in the frame. Word comparison and Matching detects the ambulance text based on the outcome of OCR. The performance of Framework 1 and Framework 2 are evaluated based on Word accuracy and from the experimental results it is observed that Framework 2 is better from 75% word accuracy. 2018, Institute of Advanced Scientific Research, Inc. All Rights reserved. -
Identification of bioactive metabolites in Turnera ulmifolia: Preliminary phytochemical screening and FTIR analysis
Turnera ulmifolia L., a member of the Passifloraceae family, is widely distributed across tropical and subtropical regions. Though frequently considered a weed, it has been commonly used in folk medicine to treat inflammation, infections, wounds and digestive ailments. Earlier studies have found alkaloids, flavonoids, tannins, terpenoids and polyphenols in species from the same genera that contribute to their therapeutic efficacy. Despite its ethnomedicinal value, the phytochemical profile and functional group characterization of T. ulmifolia are still unexplored. This study aimed to investigate the phytochemical composition of its leaf, stem and root extracts using different solvents (methanol, ethanol, hexane and acetone) and identify key functional groups through FTIR analysis. Phytochemical screening confirmed the presence of diverse secondary metabolites. FTIR analysis further revealed functional groups such as O=C=O, C=C and S=O, which are associated with therapeutic properties. Notably, alkaloids were abundant in leaf extracts, while sulfoxide groups, known for their herbicidal and medicinal effects, were detected in the stem. These findings reinforce the pharmacological potential of T. ulmifolia as a promising source of bioactive metabolites with medicinal and ecological applications. Its capacity to diversify in various habitats and create bioactive molecules under stress points to possible uses in medicine discovery, sustainable agriculture and environmental restoration. This study lays the groundwork for future research to validate its therapeutic potential and explore its integration into modern pharmaceutical and ecological solutions. (2025), (Horizon e-Publishing Group). All rights reserved. -
Identification of Brain Tumors Using CNN and ML with Diverse Feature Selection Techniques
Early diagnosis and treatment is very essential in monitoring Brain tumor using MRI images. Convolutional Neural Networks (CNN) and Machine Learning (ML) classifiers have been widely used but there is not much work on how feature selection techniques would affect the performance of the CNN. Secondly, there is a need for investigation concerning small dataset adaptability and ML-CNN comparisons. To improve the classification accuracy, we integrate Univariate, Recursive Feature Elimination (RFE), Recursive Feature Elimination with Cross Validation (RFECV) with CNN in this study. Preprocessing, feature extraction & selection was carried out on the dataset consisting of 253 MRI images and they are classified using CNN and ML models (Logistic Regression, Decision Tree, Random Forest, Nae Bayes). With the results 96%, CNN with Univariate Feature Selection performed better than ML classifiers, and other selection techniques. The results demonstrate that feature selection is necessary to get the best performance out of CNN models operating on small datasets. Future studies should be based on different deep learning architectures to improve classification and application i n other datasets. 2025, Interdisciplinary Publishing Academia. All rights reserved. -
Identification of broken characters in degraded documents
Optical Character Recognition (OCR) deals with the recognition of characters in a text document. Steps like Preprocessing, Segmentation and Recognition are embedded in the OCR machine. When a document is scanned it will be taken into OCR and will recognize the characters. But noisy scanning of documents, low-quality printed documents and thresholding error leads to the generation of broken characters. When these documents are given as inputs into OCR, the recognition becomes a tedious process since the broken characters are misunderstood by the OCR machine. So the broken characters have to be identified and segmented separately. This work aims to enhance the degraded documents with broken characters using image processing techniques. For identifying or recognizing the broken character from the image various techniques like vertical projection profile, horizontal projection profile, chain code, mean based thresholding are used. The lines from the document are separated using line segmentation. Separate characters are extracted using Vertical Projection Profile and Horizontal Projection Profile. The character is identified using chain coding. The broken characters are found from them using Mean-based Thresholding and is merged using Heuristic information. The proposed method achieves an accuracy of 92.88% and also performs well for color image documents as well as black and white image documents also because of the effective preprocessing. 2018 Intelligent Network and Systems Society. -
Identification of Consumer Buying Patterns using KNN in E-Commerce Applications
In recent days, with the advancement of technologies, people use electronic medium to carry out their businesses. E-commerce is a process of allowing people to buy and sell products online using electronic medium. E-commerce has a wide range of customer base as well. The data generated through transaction helps the enterprises to develop the marketing strategy. The growth of this e-commerce application depends on several factors. Some of the factors are follows 1) Customer demand, 2) Analyzing buying pattern of the users, 3) Customer retention, 4) dynamic pricing etc. It is very difficult to analyze the buying pattern of customers as there is a wide range of customer base in the online platform. To overcome this problem, this research study discusses about the challenges and issues in e-commerce applications, also identifies and analyses the buying patterns of customer using various machine learning techniques. From the implementation it is identified that, KNN algorithm performed well while comparing it with various other machine learning algorithms. Performances of these algorithms have been analyzed using various matrices. For analyzing, the model is tested using e-commerce dataset (Amazon dataset downloaded from Kaggle.com). From the analysis it found that KNN algorithm computes and predicts better compared to other machine learning algorithms either Nae Bayes, or Random Forest, or Logistic Regression etc. 2023 IEEE. -
Identification of coronary artery stenosis based on hybrid segmentation and feature fusion
Coronary artery disease has been the utmost mutual heart disease in the past decades. Various research is going on to prevent this disease. Obstructive CAD occurs when one or more of the coronary arteries which supply blood to myocardium are narrowed owing to plaque build-up on the arteries inner walls, causing stenosis. The fundamental task required for the interpretation of coronary angiography is identification and quantification of severity of stenosis within the coronary circulation. Medical experts use X-ray coronary angiography to identify blood vessel/artery stenosis. Due to the artefact, the image has less clarity and it will be challenging for the medical expert to find the stenosis in the coronary artery. The solution to the problem a computational framework is proposed to segment the artery and spot the location of stenosis in the artery. Here the author presented an automatic method to detect stenosis from the X-ray angiogram image. A unified Computational method of Jerman, Level-set, fine-tuning the artery structure, is developed to extract the segmented artery features and detect the arterys stenosis. The current experimental outcomes illustrate that this computational method achieves average specificity, sensitivity, Accuracy, precision and F-scores of 95%, 97.5%, 98%, 97.5% and 97.5%, respectively. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Identification of Cyberbullying and Finding Target User's Intention on Public Forums
Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. 2023 IEEE. -
Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine
In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved. 2023 IEEE. -
Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm
Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset's attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent. 2023 S. Krishnan et al. -
Identification of Dynamics of Tractor Chassis Structure through Ground Vibration Testing
This study investigates the vibrations arising from mass imbalance and variable inertia forces within dynamic systems, specifically focusing on agricultural tractors. Impulse test method is used to determine the modal characteristics of the tractors structure, including its frequencies, damping and mode shapes. The random responses of various components, such as the chassis, bonnet, muffler, seat and axle, were measured at engine speeds of 800, 1500 and 2500 rpm. The results indicate that the vibration amplitude depends on material properties and operational conditions, with the maximum random vibration response observed at the highest engine speed of 2500rpm. The overall root-mean-square acceleration (grms) was used to quantify vibration levels, revealing significant acceleration values of 2-4 grms across the entire tractor structure. Increased vibrations, particularly at high engine speeds, lead to amplified noise, dynamic stresses and accelerated wear on the chassis and subsystems, necessitating periodic maintenance and part replacements. The study also assessed the impact of road and field surface conditions on the vibration levels. The dynamic modes identified provide insights into potential improvements in tractor performance by implementing semiactive or active vibration control mechanisms utilizing smart materials without changing the existing engine dynamics. 2025. Carbon Magics Ltd. -
Identification of emission-line stars in transition phase from pre-main sequence to main sequence
Pre-main-sequence (PMS) stars evolve into main-sequence (MS) phase over a period of time. Interestingly, we found a scarcity of studies in existing literature that examine and attempt to better understand the stars in PMS to MS transition phase. The purpose of this study is to detect such rare stars, which we named as 'transition phase' (TP) candidates-stars evolving from the PMS to the MS phase. We identified 98 TP candidates using photometric analysis of a sample of 2167 classical Be (CBe) and 225 Herbig Ae/Be (HAeBe) stars. This identification is done by analysing the near-and mid-infrared excess and their location in the optical colour-magnitude diagram. The age and mass of 58 of these TP candidates are determined to be between 0.1-5 Myr and 2-10.5 M?, respectively. The TP candidates are found to possess rotational velocity and colour excess values in between CBe and HAeBe stars, which is reconfirmed by generating a set of synthetic samples using the machine learning approach. 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.