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Voicing Out Parental Experiences of Schooling Their Children with Learning Disabilities: A Qualitative Study of Inclusive Government Schools of India
The paper shone light on the lived experiences of parents of children with learning disabilities. The specific objective was to understand the challenges, experiences and aspirations of parents for their children. A phenomenological study was adopted for the study so as to focus on the experiences of the parents. Participants were parents (female- 17 and male- 3) of children in primary classes, who were identified through purposive sampling from government schools of Delhi, NCR from 3 underdeveloped areas of Delhi - Nangloi, Mangolpuri and Ranhaula. The data was collected by semi-structured interviews and later thematically analyzed. The findings were on the basis of the past and present experiences and further their future aspirations for the children. They revealed that the parents faced challenges with applying and issuance of the UDID certificates, but with the collaborative efforts of the special educator and the parents along with various support systems that are provided by the school their experiences became positive. It was also brought to light that the mother was the main caregiver in most of the cases. All the parents were worried, what will happen to their children if they are not there with them. They aspired that the students will be financially independent and have a safe future ahead of them. They dream of a society where all the students are equal in an inclusive environment. The Author(s) 2025. -
Creating inclusive spaces in virtual classroom sessions during the COVID pandemic: An exploratory study of primary class teachers in India
The research paper reports insights into the primary class teachers experiences and inclusive methodologies in India during virtual class sessions. Teaching online during the COVID pandemic has turned out to be an adaptive and transformative challenge for teachers. Though Indian teachers are used to the chalk-and-talk method, an online setup has compelled them to discover innovative strategies to maintain an inclusive classroom. It was found that teachers are using puppetry, storytelling, energizers, ice-breakers in their sessions to make it engaging. An in-depth study was undertaken to understand the experiences of five primary class teachers from private schools in India. Data thus collected were analyzed qualitatively. The study results demonstrated that the teachers had improved professionally they have become independent in using the internet and exploring new ways of teaching them per their needs. Nevertheless, it was also found that the schools lack support, fear among the teachers of being asked to quit the job, blocking students from the online class if they fail to pay the fees, and exorbitant salary cuts. The challenges related to young students were - lack of proper resources for online sessions, low attention span, technical distractions, lack of physical development, excessive interference of the parents and lack of socialization. The paper concludes with policy proposals regarding standardized online education platforms and provisions for proper resources for virtual class sessions to marginalized families to minimize India's digital divide. 2021 -
A First Report of Docosahexaenoic Acid-Clocked Polymer Enveloped Gold Nanoparticles: A Way to Precision Breast Cancer and Triple Negative Breast Cancer Therapy and Its Apoptosis Induction
Functionalized gold nanoparticles (GNPs) are extensively utilized in various disciplines due to their excellent bioactivity, biocompatibility, and extended drug half-life, influenced by the ligands and size that are changed on surfaces. In this study, we successfully fabricated GNPs coated with ligands containing docosahexaenoic acid (DHA) and polyethylene glycol (PEG) clocked by a carboxyl group. These nanoparticles are referred to as MPA@GNPs-PEG-DHA. The cytotoxicity results demonstrate that MPA@GNPs-PEG-DHA exhibits superior cell selectivity, explicitly inhibiting the proliferation of breast cancerous cells than noncancerous cell lines. Apoptosis is involved in the reduction of cell proliferation by MPA@GNPs-PEG-DHA, as demonstrated clearly through many assays measuring apoptotic index, including AO/EB staining, DAPI, annexin V-FITC staining, mitochondrial membrane potential (MMP), and reactive oxygen species (ROS) measurement. The efficacy of MPA@GNPs-PEG-DHA in inducing apoptosis was demonstrated by its inhibition of mitochondrial dysfunction by ROS. MPA@GNPs-PEG-DHA has the potential to improve the induction of apoptosis in breast cancerous cells. 2024 Wiley Periodicals LLC. -
A comparative study on decision tree and random forest using konstanz information miner (KNIME)
With vast amounts of data floating around everywhere, it is imperative to comprehend and draw meaningful insights from the same. With the proliferation of Internet and Information Technology, data has been increasing exponentially. The 5 Vs of data i.e. Value, volume, Velocity, variety and veracity will only make sense if we are able to examine the data and uncover the hidden, yet meaningful insights. With large data becoming a norm, a lot of data mining algorithms are available that help in data mining. We have tried to compare two classification algorithms, primarily Decision trees and Random forest. A total of 10 datasets have been taken from UCI Repository and Kaggle and with the help of Konstanz Information Miner (KNIME) workflows, a comparative performance has been made pertaining to the accuracy statistics of Random Forest and decision Tree. The results show that Random Forest gives better and accurate results for a dataset as compared to decision trees. 2020 SERSC. -
Minimizing the waste management effort by using machine learning applications
Waste management is a process of collecting, transporting, disposing, and monitoring waste materials generated by human activities. It is an essential part of maintaining public health, hygiene, and environmental sustainability. Waste management systems can be designed to handle different types of waste, such as household waste, industrial waste, hazardous waste, and medical waste. The increasing amount of waste has become a major issue for the development of sustainable communities. Machine learning can help solve this problem by allowing scientists to analyze and reduce waste. This chapter aims to provide a comprehensive overview of the various aspects of waste management using machine learning. The chapter covers the various aspects of waste disposal, generation, transportation, and collection. It also explores machine learning's potential in this area, such as data analysis and prediction. It additionally compiles case studies about how machine learning has been utilized in this field. 2024, IGI Global. -
Eco-friendly innovations in food packaging: A sustainable revolution
Packaging is crucial in ensuring the quality and safety of food, protecting it from various contaminants, and extending its shelf life. Materials used for packaging food must be economical, durable, and possess good barrier properties. One of the major challenges faced by the food industry is developing an eco-friendly, economical, and sustainable packaging system. The conventional materials, which majorly depend on petroleum-derived polymers, are associated with several significant problems, such as environmental pollution, depletion of resources, generation of single-use wastes, leakage of chemicals into food products, limited recycling, and so on. As the food sector focuses on reducing its environmental impact, by encouraging revolutionary changes for an effective sustainable food packaging approach. The core objective of industrial packaging was to innovate a biodegradable material, especially derived from renewable biomass resources as eco-friendly alternatives in the food industry. One of the significant trends involves production of bioplastics, which are derived from renewable polymers such as corn starch, sugarcane, or algae. These materials offer a viable alternative to traditional petroleum-based plastics, as they are often compostable or biodegradable. The development of advanced bioplastics with improved barrier properties and durability is gaining traction, addressing environmental and health concerns and functionalizing a packaging material. The present review discusses the limitations of conventional packaging materials used in the food industry and focuses on the various polymers derived from natural sources, their physio-chemical properties, and their potential application as a sustainable material that reduce carbon emission, and enhance preservation of food and ensure food safety. 2024 Elsevier B.V. -
Evolving Dynamics of Contactless Technology in Hospitality: Insights From Recent Research and Future Implications
The hospitality sector has been forced by the COVID-19 pandemic to go contactless to protect safety, efficiency, and customer satisfaction. Innovations like mobile check-ins, smart room controls, and touchless payments have brought new services to the market that are customer-friendly and still safe to use. These technologies provide a double advantage by either enhancing guests experience or efficiency for the staff. On the other hand, such factors as the high costs, problems of system integration, and fears of depersonalization are still raising the worries of the hoteliers. It is therefore very important to balance the degree of technology} The concept of Hospitality 5.0 aims at the fusion of both the traditional and the modern elements to create secure yet personalized experiences. The main strategies for achieving the requirement include trust-building, technology simplification, and gamification to promote guest engagement. By taking such approaches, the hospitality industry will be able to redefine and be still competitive and client-oriented. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Enhancing Cloud Security and Privacy With Blockchain Technology
This chapter explores blockchain's potential to address cloud computing security challenges. Despite cloud computing's scalability and cost efficiency, it faces risks like data breaches and regulatory non-compliance, as seen in the 2019 Capital One AWS breach. Blockchain's decentralized ledger, cryptographic hashing, smart contracts, and consensus mechanisms (e.g., PoW, PoS) enhance security through decentralized access control, secure storage, and intrusion detection. Privacy techniques like homomorphic encryption and zero-knowledge proofs protect data. Case studies, including IBM Food Trust and MedRec, show practical applications. However, scalability, interoperability, regulatory conflicts (e.g., GDPR), and high costs pose barriers. Solutions like sharding and layer-2 protocols aim to overcome these. Future research focuses on scalability, privacy, hybrid cloud integration, and AI-driven security. Blockchain strengthens cloud security but requires innovation to achieve widespread adoption. 2026, IGI Global Scientific Publishing. All rights reserved. -
Machine Learning Methods leveraging ADFA-LD Dataset for Anomaly Detection in Linux Host Systems
Advancement in network technology and revolution in the global internet transformed the overall Information Technology (IT) infrastructure and its usage. In the era of the Internet of Things (IoT) and the Internet of Everything (IoE), most everyday gadgets and electronic devices are IT-enabled and can be connected over the internet. With the advancements in IT technologies, operating systems also evolved to leverage these advancements. Today's operating systems are more user-friendly and feature-rich to support current IT requirements and provide sophisticated functionalities. On the one hand, these features enabled operating systems accomplish all current requirements, but on the other hand, these modern operating systems increased their attack surface considerably. Intrusion detection systems play a significant role in providing security against the broad spectrum of attacks on host systems. Intrusion detection systems based on anomaly detection have become a prominent research area among diverse areas of cyber security. The traditional approaches for anomaly detection are inadequate to discover the operating system level anomalies. The advancement and research in Machine Learning (ML) based anomaly detection open new opportunities to tackle this challenge. The dataset plays a significant role in ML-based system efficacy. The Australian Defence Force Academy Linux Dataset (ADFA-LD) comprises thousands of normal and attack processes system call traces for the Linux platform. It is the benchmark dataset used for dynamic approach-based anomaly detection. This paper provided a comprehensive and structured study of various research works based on the ADFA-LD for host-based anomaly detection and presented a comparative analysis. 2022 IEEE. -
The role of artificial intelligence autonomy in higher education in India
To leverage the benefits of artificial intelligence applications for experiential learning, many higher education institutes have started using artificial intelligence by adopting many artificial intelligencedriven technologies. Some of them are chatbots, generative AI, concepts of virtual tutors, and providing students with various automated assessment tools for their own assessment, which might change the traditional teaching methodology. This chapter examines the role of artificial intelligence autonomy in higher education in India, deep-diving into AI's impact on students' learning outcomes by leveraging AI-driven technologies in education. This research will specifically have five major variables that will be examined and measured. These variables are usage intention, thought autonomy, action autonomy, sensing autonomy, and culture. This study will examine the five main dimensions of AI autonomy: usage intention, thought autonomy, action autonomy, sensing autonomy, and culture. 2025, IGI Global Scientific Publishing. All rights reserved. -
Education suffering within structural inequalities: A Critical Discourse Analysis of a policy framework
Education acts as an important catalyst for socioeconomic and democratic evolution in society and is a critical tool for building an equitable system. In our paper, we have historicized one of the most important educational policies, viz. Samagra Shiksha Abhiyan (SAMSA) in India that carries large expectations to minimize the educational divide. We have studied the policy through the lens of Political Economy and have further critiqued it through the framework of Critical Discourse Analysis. We find in our paper that the budget allocated to SAMSA was revised in 2022, from its preceding years with a 28 per cent slash. We critically reflect on the principles mentioned in the policy and find that although there has been an attempt to mitigate the hazards of banking education the Public-Private Partnership initiative reinforces struggles for equitable education, and further, the privatization sets the government free from any accountability. Moreover, a constitutional right like the Right to Education (RTE) is not sufficient enough to meet the goals of universalisation of education. Besides, we analyse the principles such as Education for All, Equity, Equal Opportunity, Access, Gender Concern, Centrality of teacher, Moral Compulsion, and Convergent and integrated system of education management, and argue that although some of the facets of societal structural inequalities are addressed, however, there exists hardly a proper roadmap that could be monitoring the process of creating an inclusive educational paradigm. 2023, Institute for Education Policy Studies. All rights reserved. -
Optimizing Fake News Classification Using Data Fusion and NLP-Based Machine Learning Techniques
In this research, the performance of different machine learning algorithms for identifying fake news using a dataset of news articles labeled as fake or real. The dataset was preprocessed to remove stop words, punctuation, digits, and special characters, and text normalization was applied. Two feature extraction methods, BOW (Bag-of-Words) and TF-IDF, were utilized to convert text data into numerical features. The dataset was split into training and testing phases to train and evaluate models, including Support Vector Classifier, Logistic Regression, Decision Trees, Gradient Boosting Classifier, Random Forest, and Multinomial Naive Bayes. Ensemble models combining various classifiers were also tested. Performance metrics, including precision, recall, and F1-score, were assessed, and confusion matrices were analyzed. Results showed that TF-IDF generally outperformed BOW. The Random Forest model achieved the highest precision (93%) but had a lower recall (83%). The SVC model showed a balanced performance with a precision of 90%, recall of 87%, and an F1-score of 86%. Ensemble models like GB?+?RF exhibited high precision (99%) but lower recall. These findings highlight the strengths of different algorithms in fake news detection and inform the development of practical classification tools. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Navigating Digital Transformation: The Role of Technology in HRM Efficiency and Effectiveness
Technology has grown increasingly common in contemporary workplaces, helping to improve the efficiency and efficacy of Human Resource Management (HRM) processes. The purpose of this research is to investigate the ever-changing role of technology in HRM, especially its impact on organizational efficiency and effectiveness. The goal of this study is to look at the usage of artificial intelligence (AI) and automation in human resources (HR) activities in order to identify the possible advantages and obstacles of incorporating technology. Furthermore, the goal is to identify the best techniques for using technology to improve HR operations and decision-making processes. The goal of this research is to highlight the importance of HR analytics in allowing data-driven decision-making and improving organizational performance by a thorough examination of current literature and empirical data. Furthermore, this study investigates the practical implications and empirical examples of technology integration in the field of HRM, focusing on its influence on both organizational success and employee engagement. To leverage the benefits of technology-driven HRM practices, the passage highlights the need of aligning HR strategy with organizational goals and cultivating a data-driven culture. This study adds to the current literature by giving valuable insights into the changing environment of HRM in the digital age. It also advises professionals and academics on how to utilize technology most effectively in order to achieve organizational success. 2025 by Dr Sofia Khan and Dr Kartikeya Singh. All rights reserved. -
Immobilization of TiO2 on Various Substrates
Recovery of photocatalytic materials after the degradation of organic pollutants remains a challenge. To address this issue, immobilizing the material on a suitable substrate presents a viable solution. Immobilization of the commonly used titanium dioxide (TiO2) photocatalyst onto various substrates is typically achieved through adsorption, hydrogen bonding, or chemical bonding. Coating TiO2 onto different substrates is a common approach to enhance its durability, reusability, and catalytic efficiency across multiple applications, such as photocatalysis, sensors, and heterogeneous catalysis. The choice of substrate depends on the specific application, desired properties, and its ability to improve the photocatalytic performance. Substrates such as glass/quartz, polymeric materials, metal oxides, carbon-based materials, textiles, and cellulose each offer unique characteristics that enhance the potential of the photocatalytic material. 2026 WILEY-VCH GmbH. -
Beyond Transcripts: A Learner-Centred Review for Closing the Graduate Skills Gap
The majority of the university graduates leave their courses with high grades, but they usually do not have the necessary skills needed in the working environments including teamwork, problem-solving and digital skills. This disconnect between higher education training and the needs of the industry is what is referred to as the graduate skills gap. The article consists of a literature review from 2020-2025 to explore how learner-centred pedagogy can be used to reduce this gap. The results have shown that project-based learning, real-life assessment, internship and micro-credential equip students better than conventional exams. Employers prefer technical and soft skills to academic performance, but most universities are facing problems with stiff curricula and lack of faculty training. This review proposes the incorporation of practice projects, industry partnership, and online skill records to fill the gap. These are some of the strategies that can be used to equip the students with the competencies needed in the current dynamically changing labour market. 2025 IEEE. -
Automated Waste Segregation using Raspberry Pi and Deep Learning
With rapid urbanization and increasing waste generation, efficient waste segregation has become a critical challenge for sustainable waste management. Traditional waste disposal methods rely heavily on manual sorting, which is inefficient, labor-intensive, and prone to errors, leading to improper recycling and environmental hazards. To address these problems, a clever waste segregation method is presented in this research. It automatically sorts waste into four categoriesglass, metal, plastic, and paper/cardboardusing computer vision and machine learning. A 720p webcam is used to collect images in real time, and the system is powered by a Raspberry Pi 4B with 4GB of RAM. A Convolutional Neural Network (CNN) model that was developed using the TrashNet dataset forms its basis. The model can correctly identify the waste in the photos due to an optimized training method that incorporates data augmentation, regularization strategies, and early stopping to prevent overfitting. An SG90 servo motor controls the lid, ensuring the garbage is placed in the appropriate compartment, while an MG996R servo motor swings the bin into place after the waste has been classified. The bin and lid go back to their initial places once the garbage has been dumped, preparing the system for usage again. Here, we are able to combine automated mobility, automated categorization, and real-time waste detection with embedded technologies, machine learning, and automation to separate waste with the least amount of human intervention. Furthermore, the system's scalability and adaptability make it appropriate for smart city initiatives, urban trash management, and wider industrial application. Consequently, this technology helps to tackle intelligent waste management problems, which facilitates the emergence of a sustainable and eco-friendly future. The system achieved a testing accuracy of 88.1%, showcasing its effectiveness and reliability. Grenze Scientific Society, 2025. -
Fake News Detection Using TF-IDF Weighted with Word2Vec: An Ensemble Approach
Social media platforms' utilization for news consumption is steadily growing due to their accessibility, affordability, appeal, and ability to propagate misinformation. False information, whether intentionally or unintentionally created, is being disseminated across the internet. Certain individuals spread inaccurate information on social media to gain attention, financial benefits, or political advantage. This has a detrimental impact on a substantial portion of society that is heavily influenced by technology. It is imperative for us to develop better discernment in distinguishing between fake and genuine news. In this research paper, we present an ensemble approach for detecting fake news by using TF-IDF Weighted Vector with Word2Vec. The extracted features capture specific textual characteristics, which are converted into numerical representations for training the models and balanced dataset with the Random over Sampling technique. The implementation of our proposed framework utilized the ensemble approach with majority voting which combines 2 machine learning models like Random Forest and Decision Tree. The proposed strategy was adopted empirically evaluated against contemporary techniques and basic classifiers, including Gaussian Nae Bayes, Logistic Regression, Multilayer Perceptron, and XGBoost Classifier. The effectiveness of our approach is validated through the evaluation of the accuracy, F1-Score, Precision, Recall, and Auc curve, yielding an impressive accuracy score of 94.24% on the FakeNewsNet dataset. 2023, Ismail Saritas. All rights reserved. -
ENHANCING FAKE NEWS DETECTION ON SOCIAL MEDIA THROUGH ADVANCED MACHINE LEARNING AND USER PROFILE ANALYSIS
Social media news consumption is growing in popularity. Users find social media appealing because it's inexpensive, easy to use, and information spreads quickly. Social media does, however, also contribute to the spread of false information. The detection of fake news has gained more attention due to the negative effects it has on society. However, since fake news is created to seem like real news, the detection performance when relying solely on news contents is typically unsatisfactory. Therefore, a thorough understanding of the connection between fake news and social media user profiles is required. In order to detect fake news, this research paper investigates the use of machine learning techniques, covering important topics like feature integration, user profiles, and dataset analysis. To generate extensive feature sets, the study integrates User Profile Features (UPF), Linguistic Inquiry and Word Count (LIWC) features, and Rhetorical Structure Theory (RST) features. Principal Component Analysis (PCA) is used to reduce dimensionality and lessen the difficulties presented by high-dimensional datasets. The study entails a comprehensive assessment of multiple machine learning models using datasets from "Politifact" and "Gossipofact," which cover a range of data processing methods. The evaluation of the XGBoost classification model is further enhanced by the analysis of Receiver Operating Characteristic (ROC) curves. The results demonstrate the effectiveness of particular combinations of features and models, with XGBoost outperforming other models on the suggested unified feature set (ALL). 2023 Little Lion Scientific.