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Enhanced Autism Prediction using Hybrid Machine Learning Model
Autism Spectrum Disorder (ASD) is a condition where individuals face challenges in neurological development and have verbal, non-verbal, learning and behavioral disorders. Even though this condition is identifiable in the first few years in the children's life, many remain undiagnosed until later. This leads to long term developmental issues and delayed interventions. This is what makes the early detection critical for improving development in children. Despite that, traditional diagnosis approaches like behavioral checklists and pre structured interviews rely on the clinician's expertise and are time consuming and have a risk of inconsistency. This study entails and addresses the above problem by proposing a machine learning based multi model to automate early detection in toddlers aged 12 to 36 months. In the initial stage, the traditional classification algorithms like Logistic Regression, SVM are evaluated with high accuracy, F1 score. Then, hybrid models are developed by combining Gradient Boosting as the anchor model with other high performing algorithms, to overcome the limitation of single classification models. These hybrid models help to overcome the limitations of the individual classifiers. Finally, the best-performing hybrid model is enhanced further by Hyperparameter tuning, Feature selection and Cross validation. The outcome of this research will be a hybrid model, combining machine learning algorithms with the best scores, ensuring high accuracy and low false positives. This aims to help in the detection of ASD in early stages in toddlers. 2025 IEEE. -
Enhanced Autism Prediction using Hybrid Machine Learning Model
Autism Spectrum Disorder (ASD) is a condition where individuals face challenges in neurological development and have verbal, non-verbal, learning and behavioral disorders. Even though this condition is identifiable in the first few years in the children's life, many remain undiagnosed until later. This leads to long term developmental issues and delayed interventions. This is what makes the early detection critical for improving development in children. Despite that, traditional diagnosis approaches like behavioral checklists and pre structured interviews rely on the clinician's expertise and are time consuming and have a risk of inconsistency. This study entails and addresses the above problem by proposing a machine learning based multi model to automate early detection in toddlers aged 12 to 36 months. In the initial stage, the traditional classification algorithms like Logistic Regression, SVM are evaluated with high accuracy, F1 score. Then, hybrid models are developed by combining Gradient Boosting as the anchor model with other high performing algorithms, to overcome the limitation of single classification models. These hybrid models help to overcome the limitations of the individual classifiers. Finally, the best-performing hybrid model is enhanced further by Hyperparameter tuning, Feature selection and Cross validation. The outcome of this research will be a hybrid model, combining machine learning algorithms with the best scores, ensuring high accuracy and low false positives. This aims to help in the detection of ASD in early stages in toddlers. 2025 IEEE. -
Future Crop Designing: Antistress Capacities Gained by CRISPRmediated Releasing the Potential of Functional Genomes
Abiotic stresses, including temperature fluctuations, salinity, and drought, as well as biotic stresses such as viral, bacterial, and fungal infections, exert detrimental effects on plant growth and development, thereby significantly impeding overall plant productivity and crop yield. Traditionally, the sustainable mitigation of abiotic stress has been achieved through the breeding of tolerant cultivars; however, this process is characterized by its timeconsuming and labor-intensive nature, as well as its inherent lack of precision. Thus, there is a pressing need to adopt advanced genome technology to address these limitations and enhance the efficacy of stress-tolerance breeding efforts. This can be addressed by facilitating site-specific modifications of selected functional genomic elements, thus providing a potential avenue for introducing desired traits to combat adverse stress conditions. Among various genome engineering methodologies, CRISPR-Cas9 has emerged as the most promising genomeediting tool, attributed to its notable efficiency, precision, and rapidity. This study offers insights into the prospective trajectory of crop improvement through the advancement of crop enhancement strategies, employing CRISPR technology to enhance crop resilience against stress conditions by selectively modifying or activating specific functional genomes. CAB International 2025. All rights reserved. -
English language traning for core course instruction in commerce courses :
Tracing the scope and growth of English in the globalised world, this research focusses on helping the learners to improve their English language proficiency through core course instruction. The research has identified the scope of study in the Commerce discipline of higher education setting. The study aims to locate the possibility of learning and improving general vocabulary for the purpose of communication. It traces the existing studies in integrating English language in core course content at various levels and establishes the gap in the study. The mileage that English Language Teaching has covered in the past few decades is far newlinefrom listing. However, areas of study that might seem familiar and established still newlineseem to provide more scope for research. English language, no doubt has become newlinethe medium of instruction in most of the higher education settings. Students get newlineexposed to different course content through English, and training teachers for various skills has become an important quarter in the education setting. With each passing generation, there is a need to create a training approach that suits the lifestyle, advancements in various forums and needs of the learners. This research attempts to create a training module for the purpose of equipping teachers with the ability to teach English, which is the medium of instruction, through core course instruction in the higher education scenario. The research provides a module that could serve as a model for teachers to use language effectively and equip their learners not just with the knowledge of the subject, but also the knowledge of the language through which the content is delivered. The purpose of this study is to highlight the need for a holistic understanding of the language used for content delivery and also to enable students to be able to use the language inputs received here, in daily life communication too. -
Hairy Root Engineering for Enhanced Production of Secondary Metabolites
[No abstract available] -
Role of Augmented Reality (AR) in Promoting Media Literacy and Sustainability Awareness: A Mixed Method Approach
Augmented reality (AR) has emerged as a transformative tool in education, offering immersive experiences that enhance engagement and understanding across various domains. This study explores the potential of AR in promoting media literacy and sustainability awareness, two critical competencies in the modern information landscape. Through a mixed-methods approach, the research investigates how AR interventions can improve individuals ability to critically assess media content while simultaneously raising awareness about environmental sustainability. The study employs pre- and post-test evaluations, focus groups, and user interaction data to measure changes in media literacy and sustainability awareness among participants exposed to AR-based educational content. Findings indicate that AR significantly enhances media literacy by enabling users to better identify fake news, understand media bias, and critically evaluate information sources. The implications of these findings suggest that AR is not only a powerful tool for enhancing media literacy and sustainability awareness but also a catalyst for promoting informed, responsible, and proactive citizenship in the digital age. 2026 selection and editorial matter, Sonal Trivedi, Vishal Jain, Balamurugan Balusamy, Subhendu Pani, and Danish Ather. -
Internet of Things Enabled Device Fault Prediction System Using Machine Learning
Internet of Things (IOT) started as a niche market for hobbyists and has evolved into a huge industry. This IoT is convergence of manifold technologies, real-time analytics, machine learning and Artificial Intelligence. It has given birth to many consumer needs like home automation, prior device fault detection, health appliances and remote monitoring applications. Programmed recognition and determination of different kinds of machine disappointment is a fascinating process in modern applications. Different sorts of sensors are utilized to screen flaws that is discovers vibration sensors, sound sensors, warm sensors, infrared cameras, light cameras, and other multispectral sensors. The modern devices are becoming ubiquitous and pervasive in day to day life. This device is need for reliable and predicate algorithms. This article is primarily emphases on the prediction of faults in real life appliances making our day to day life easier. Here, the database of the device includes previous faults which are restored in online by using cloud computing technology. This will help in the prediction of the faults in the devices that are to be ameliorated. It additionally utilizes Nae Bayes calculation for shortcoming location in the gadgets. The proposed model of this article is involves the monitoring of each and every home appliance through internet and thereby detect faults without much of human intervention. Springer Nature Switzerland AG 2020. -
Performance of pradhan mantri fasal bima yojana: Perception of farmers in rural bangalore
Crop insurance is an agricultural development program supporting the sustainability of farmers. PradhanMantriFasalBimaYojana crop insurance scheme was introduced to provide insurance cover, financial stability, innovative and modern methods of agricultural practice. The study primarily focuses on the reasons for enrollment, benefits, challenges and suggestions regarding the PradhanMantriFasalBimaYojana with respect to farmers of Rural Bengaluru. A qualitative thematic analysis using a primary study reveals PMFBY as a source of financial security and financial stability with reduced premium that increases the confidence level among the farmers. 2019 SERSC. -
A happy mother raises a happy child: insights from employed mothers in Bengali families in Kolkata
The present study explores the complexities of motherhood in Bengali middle-class families, where mothers are traditionally viewed as primary caregivers. Despite societal shifts and increased female workforce participation, mothers still face pressure to prioritize intensive mothering. Through qualitative analysis, the research explores how employed mothers balance work and childcare responsibilities, shedding light on their agency and empowerment within patriarchal structures. Findings reveal a nuanced landscape where mothers navigate societal expectations while striving for autonomy. Support systems, changing socio-economic dynamics, and technological advancements contribute to reshaping maternal roles. Mothers, though not uniformly identifying as feminists, challenge traditional norms, embracing an egalitarian approach to mothering. The study underscores the resilience of mothers in negotiating patriarchal constraints, highlighting their capacity to foster empowerment for themselves and their children within familial and societal contexts. This qualitative study conducted in-depth interviews with 37 employed mothers representing diverse professions and roles. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Lived experiences of urban working mothers during pandemic: A matricentric exploration in the Indian context
In India, entrenched patriarchal norms dictate gender roles, perpetuating men-headed families and patrilineal traditions deeply ingrained in its culture. Within this framework, working mothers daily confront gender biases despite society undervaluing their crucial roles in caregiving and the economy. The Covid-19 pandemic intensified these challenges, as working mothers faced heightened expectations to excel in both professional and maternal roles. With inadequate support and intensified caregiving demands, their physical and mental well-being significantly suffered. This article explores the complex realities experienced by Indian working mothers during the pandemic. Viewing motherhood through a matricentric lens underscores its importance to society while highlighting the need to redistribute caregiving responsibilities beyond mothers alone. The researchers conducted a qualitative study, interviewing 30 Indian working mothers from various professional backgrounds using semi-structured interviews. Thematic analysis revealed that despite seventy-seven years of independence, mothers in India continue to grapple with patriarchal oppression, inequality, and violence, underscoring the persistent challenges faced in navigating societal norms and expectations. By gaining insight into their experiences, policymakers can better grasp these burdens and implement measures to address associated physical and mental health concerns. 2025 Elsevier Ltd -
My Motherhood, My Way: A Sociological Study of Contemporary Employed Mothers in Kolkata
Motherhood in India has been understood primarily by placing mothers in the domestic space. A mother is constructed as a protector and the complete caregiver of her children. But there have been significant changes in the status of Indian women recently. In the 21st century, with suitable qualifications and employment opportunities, women have the choice to be economically independent and career-driven, which has a profound impact on their roles and responsibilities as protectors and caregivers in the home. It is essential to study and document how women in this generation have started to redefine their roles and negotiate what a mothers duties are at home. This study aims to make a systematic inquiry to understand the issues and challenges faced by employed mothers in everyday life and how they balance their career and childcare activities. Researchers investigate this through a qualitative study on mothers employed in different types of professions in the city of Kolkata. Data was collected by conducting in-depth interviews of around twenty-nine urban, upper-middle class employed mothers from different professional backgrounds to have a set of diverse narratives about their experiences and struggles. The key findings of this study provide an insight into the challenges that mothers face and their balancing mechanisms. Such studies have the scope to motivate many employed mothers by presenting some cases of women who have succeeded in breaking the stereotypical ideas of motherhood and are redefining their stories in more humane terms. 2021. Journal of International Womens Studies. -
A happy mother raises a happy child: insights from employed mothers in Bengali families in Kolkata
The present study explores the complexities of motherhood in Bengali middle-class families, where mothers are traditionally viewed as primary caregivers. Despite societal shifts and increased female workforce participation, mothers still face pressure to prioritize intensive mothering. Through qualitative analysis, the research explores how employed mothers balance work and childcare responsibilities, shedding light on their agency and empowerment within patriarchal structures. Findings reveal a nuanced landscape where mothers navigate societal expectations while striving for autonomy. Support systems, changing socio-economic dynamics, and technological advancements contribute to reshaping maternal roles. Mothers, though not uniformly identifying as feminists, challenge traditional norms, embracing an egalitarian approach to mothering. The study underscores the resilience of mothers in negotiating patriarchal constraints, highlighting their capacity to foster empowerment for themselves and their children within familial and societal contexts. This qualitative study conducted in-depth interviews with 37 employed mothers representing diverse professions and roles. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Real-time video segmentation using a vague adaptive threshold
For the last two decades, video shot segmentation has been a widely researched topic in the field of content-based video analysis (CBVA). However, over the course of time, researchers have aimed to improve upon the existing methods of shot segmentation in order to gain accuracy. Video shot segmentation or shot boundary analysis is a basic and vital step in CBVA, since any error incurred in this step reduces the precision of the other steps. The shot segmentation problem assumes greater proportions when detection is preferred in real time. A spatiotemporal fuzzy hostility index (STFHI) is proposed in this work which is used for edge detection of objects occurring in the frames of a video. The edges present in the frames are treated as features. Correlation between these edge-detected frames is used as a similarity measure. In a real-time scenario, the incoming images are processed and the similarities are computed for successive frames of the video. These values are assumed to be normally distributed. The gradients of these correlation values are taken to be members of a vague set. In order to obtain a threshold after defuzzification, the true and false memberships of the elements are computed using a novel approach. The threshold is updated as new frames are buffered in and is referred to as the vague adaptive threshold (VAT). The shot boundaries are then detected based on the VAT. The VAT for detecting the shot boundaries is determined by using the three-sigma rule on the defuzzified membership values. The effectiveness of the real-time video segmentation method is established by an experimental evaluation on a heterogeneous test set, comprising videos with diverse characteristics. The test set consists of videos from sports, movie songs, music albums, and documentaries. The proposed method is seen to achieve an average F1 score of 0.992 over the test set consisting of 15 videos. Videos from the benchmark TRECVID 2001 are selected for comparison with other state-of-the-art-methods. The proposed method achieves very high precision and recall, with an average F1 score of 0.939 on the videos chosen from the TRECVID 2001 dataset. This is a substantial improvement over the other existing methods. 2020 Elsevier Inc. -
Spectral Evolution of GX 17+2 Using AstroSat and NICER Observations
We study the spectral evolution of the Z-track source GX 17+2 using AstroSat and NICER observations taken between 2016 and 2020. The AstroSat observations cover the period when the source is in the normal branch (NB) and the flaring branch (FB), while for the NICER ones the variability can be associated with the FB branch. The source spectra at different regions of the branches are well described by accretion disk emission, blackbody surface emission, and a thermal Comptonization component. In the NB, the total bolometric unabsorbed flux remains constant and the variation is due to changes in the Comptonization, disk fluxes. In particular, the inferred luminosity (LT) and accretion rate ( M ? ) remain constant, while there is significant variation in the inner disk radii and fraction of disk photons entering the corona, indicating changes in the geometry of the system. On the other hand, in the FB, there is significant variation in luminosity from ?4.0 to ?7.0 1038 erg s?1. Despite this significant variation in luminosity and in the inner disk radii, the accretion efficiency, defined as ? = L T / M ? c 2 , remains nearly constant at ?0.20 throughout the evolution of the source, as expected for a neutron star system. 2025. The Author(s). Published by the American Astronomical Society. -
Automated hyperspectral image clustering using multilevel quantum differential evolution on quantum /
Patent Number: 202141013977, Applicant: Tulika Dutta.
Hyperspectral images are data cubes composed of huge spectral information. The spectral bands contain abundant information but are also full of redundant data. The huge information content also increases the space and time complexity to deal with hyperspectral images and due to Hughes phenomena, the accuracy also decreases with increase in information content. The constraint of research data and ground truth images of hyperspectral images is a real limitation of efficiently developing algorithms, especially supervised ones which require priori knowledge about the dataset. -
Automatic detection of violence activity-A tool for women's safety /
Patent Number: 202041006858, Applicant: Debanjan Konar.
Human activity detection has gained popularity owing to wide range of applications from game development to surveillance. Recent development of Deep Pose (Human pose detection using deep neural network. -
A microcontroller based low cost electronic locking system using 2-way authentication /
Patent Number: 202041036194, Applicant: Siddhartha Bhattacharyya.
Multi-factor authentication aims in prevention of unauthorized access to any secured system, and the 2-way or 2-factor verification is one of the most effective and indispensable solution for this purpose. In this work, a low cost electronic locking system using 2-way authentication has been conceived to provide secured access to any assets or resources. -
Exploring Socio-Political Factors and Quality of Life Among LGBT Individuals in India
The quality of life of queer individuals in India is a result of a complex sociopolitical climate which is what this study aims to explore through qualitative methodology. Previous research has explored the social factors that impact the wellbeing of LGBT individuals in western countries, while the impact of politics on the wellbeing of marginalized groups is still largely unexplored. Through thematic analysis, this study found that family support and peer networks are the two most important social structures that determine the quality of life of LGBT emerging adults in India, whereas the impact of politics on wellbeing depends on the level of political awareness of the participants and their socio-political privilege in terms of caste, class and gender. However, there were significant differences in the relevant factors that affect the quality of life for cisgender and transgender participants which leaves room for further research. The findings indicate intra-community conflicts and changing dynamics within the community, and there needs to be extensive research on understanding the intersectionality of different identities within the community and their impact on the lives of queer individuals. 2024 Taylor & Francis Group, LLC. -
Hybrid Computational Intelligence: Challenges and Applications
Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development. Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems. 2020 Elsevier Inc.