Explainable Artificial Intelligence and Social Theory Integration for Advancing Educational Equity in Nepal | Chapter 2| Mathematics and Computer Science: Research Updates Vol. 8

 

This chapter examines entrenched socioeconomic disparities in Nepal’s education system through the integration of explainable artificial intelligence (XAI) and foundational social theories of equity. While Nepal has made progress in enrollment, persistent gaps in access, retention, and learning outcomes remain among groups marginalized by caste, gender, and geography. Existing policy analyses often rely on linear statistics or descriptive methods and lack operational links to sociological theory. To address this lacuna, we develop a mixed-methods framework that blends predictive machine learning with interpretability (SHAP) and qualitative inquiry to ground algorithmic findings in lived experience. Using national-level datasets — notably the Education Management Information System (EMIS) and the Nepal Living Standards Survey (NLSS)—we operationalize a Capability Index and train ensemble models (Random Forest and XGBoost) to predict capability deprivation and dropout risk. SHapley Additive exPlanations (SHAP) are applied to attribute model outputs to observable socioeconomic and school-level features. We formalize the predictive problem and its interpretability as follows: given feature set X = {x1, . . . , xn} and an outcome Y (capability index or dropout probability), we estimate \(\hat{Y}\) = f(X; θ) and decompose \(\hat{Y}\) additively into baseline and feature contributions \(\hat{Y}\) = ϕ0 +\(\Sigma\)i ϕi. This decomposition informs policy levers by quantifying marginal contributions of poverty, distance to school, caste status, and school resources. Beyond technical contributions, the chapter situates model outputs within Sen’s Capability Approach and Bourdieu’s Cultural Capital Theory to interpret how structural constraints and cultural resources shape educational opportunity. Deliverables include a resource allocation framework, SHAP-driven simulation dashboards for policymaking, and early-warning indicators for dropout prevention. Qualitative interviews with educators and community stakeholders are used to validate and contextualize the quantitative results. Together, these elements advance both theory and practice: they demonstrate how XAI can produce socially meaningful, policy-ready evidence for more equitable education in Nepal and similar low- and middle-income contexts.

 

Author(s) Details

Anmol Adhikari
Department of Computer Science, Noida International University, India.

 

Vivek Kumar Sinha
Department of Computer Science and Engineering, Noida International University, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/6555

From Cart to Click: Understanding Consumer Attitude towards Technology Enabled Online Grocery Shopping | Chapter 2 | Science and Technology - Recent Updates and Future Prospects Vol. 6

Online grocery shopping is trending worldwide as one of the most expedient e‑commerce practices. Many people have started purchasing their groceries online and this has allowed the researchers to identify and analyse the factors that influence the consumers for making online purchases. We have observed that until around 5 years back, the daily grocery requirements of the Indian consumers were fulfilled by the local Kirana store and to some extent by the emerging hypermarkets/supermarkets. But, with the popularity of the internet and the penetration of smartphones into the daily lives of people, several online grocery stores have been able to penetrate the Indian markets. The busy lives of people especially the urban working population, make online grocery shopping a popular choice. With traditional households still being widely spread in Indian society, online companies need to have a resounding approach to influence the buying habits and shopping patterns of consumers. The aim of this study is to understand the attitude of Indian consumers toward online grocery shopping and determine the factors that influence the consumer decision making to shop for groceries online. In this paper, we have comprehensively explored different areas associated with online grocery shopping and this study can be advantageous for online grocery retailers to articulate effective policies to gain customer confidence toward online grocery shopping. First, in-depth interviews with 20 to 25 people were conducted to have an overall impression of the views of Indian consumers on shopping the groceries online. Then, a survey with a well-thought-out questionnaire was circulated to around 150 people. The total number of complete responses was collected from 125 people. The primary data was collected and analysed to determine the consumer attitude towards online grocery shopping. The findings of the study show that the consumers are influenced by various factors like time-saving and convenience provided by online grocery shopping platforms. The result indicated that 64% of the respondents shop for groceries online, yet a considerable population was still comfortable shopping through the traditional brick-and-mortar stores. They are also influenced by the quality of products and the return policy as well as the level of comfort while using the online shopping website/ app. We have also been able to understand the reasons that prevent people from online shopping. Some people find themselves lacking the technical skills to shop online, but most people who do not shop online like going to the market personally. In the case of online shopping, an area of concern for most shoppers is trusting online applications. People are concerned about the privacy of their personal information while shopping online and need the thoughtfulness of the retailers to encourage more consumers to opt for online shopping platforms.

 

Author (s) Details

 

Dr. Deepshikha Aggarwal

Department of Information Technology Jagan Institute of Management Studies, Delhi, India.

Deepti Sharma

Department of Information Technology Jagan Institute of Management Studies, Delhi, India.

Archana B Saxena

Department of Information Technology Jagan Institute of Management Studies, Delhi, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/strufp/v6/12355F

Protection for 5G Network Access through Data-driven Deep Neural Network Clustering | Chapter 7 | Research and Developments in Engineering Research Vol. 8

 This study presents an creative security model for wireless approach in 5G networks, referred to as 5GDoSec. Considering that a concern inside the security of 5G network access refers to Distributed Denial of Service (DOS) attacks accredit its orientation towards the Internet of Things (IoT), a safety model is put forth. This novel model offers a judgment to this predicament, demanding slightest user dossier, user-friendly operation, modernized training and arrangement, as well as modest computational demands and irregular adaptability. The basic target of this model search out identify potential trespassers and malicious actors through the request of Deep Neural Networks coupled with machine intelligence methodologies. The methodology trails an evolutionary process established prototypes where an alone security model is buxom through data analysis. This approach influences access dossier collected from a specific effort point that aggregates, profiles, and classification authorized network users. The aim search out discern, established access metrics and alive durations, those individuals that ability pose a security risk. The adaptable type of the 5GDoSec model has been tentatively demonstrated and stands as a dependable method of accurately classification hazardous users. Empirical confirmation, gauged through the DaviesBouldin index, underlines its superiority over alternative methods such as Kmeans and Linkage.

Author(s) Details:

Sebastian Camilo Vanegas Ayala,
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Intelligent Internet Research Group, Bogotá D.C., Colombia.

Octavio José Salcedo Parra,
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Intelligent Internet Research Group, Bogotá D.C., Colombia and Department of Systems and Industrial Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá D.C., Colombia.

Brayan Leonardo Sierra Forero,
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Intelligent Internet Research Group, Bogotá D.C., Colombia.

Please see the link here: https://stm.bookpi.org/RADER-V8/article/view/11955

Smart Hospitals: Intelligent IoT Solutions for Enhanced Patient Experience and Workflow Optimization | Chapter 2 | Research Highlights in Science and Technology Vol. 9

This member examines the field of Intelligent Internet of Things (IoT) resolutions, examining how they ability improve patient delight and streamline medical processes in "smart hospitals." The member provides a comprehensive view of the current healthcare whole, highlighting the troubles faced by conventional healing facilities and the need for imaginative solutions. It delves into the core pieces of smart hospitals, in the way that IoT devices, dossier analytics, and connectivity, in addition to their various healthcare requests. The chapter conducts a thorough review of the article and analyzes case studies in consideration of provide a current overview of smart emergency room implementations. The topic of dossier security and privacy in smart emergency rooms is discussed in agreements of both potential benefits and difficulties. Finally, the chapter draws to a close by outlining potential future guidances and highlighting the life-changing impact of smart hospitals on healthcare delivery. The main aim is to explain a thorough understanding of how creative IoT solutions can revolutionize hospitals into patient-centric healthcare backgrounds, boosting output and satisfaction for both sufferers and healthcare providers.

Author(s) Details:

M. Laxmaiah,
Department of Electronics and Communication Engineering, Government Polytechnic, Hyderabad, Telangana, India.

B. Neeraja,
Department of Electrical and Electronics Engineering, Government Polytechnic, Hyderabad, Gana, India.

Aparna Atul Junnarkar,
Department of Computer Science and Engineering, PVG College of Engineering, Pune, Maharashtra, India.

Mandeep Kaur,
Department of Electronics and Communication Engineering, Punjabi University Patiala, Punjab, India.

Nazeer Shaik,
Department of Computer Science and Engineering, Srinivasa Ramanujan Institute of Technology (Autonomous) Anantapur, Andhra Pradesh, India.

Please see the link here: https://stm.bookpi.org/RHST-V9/article/view/11632

Role of Artificial Intelligence and Machine Learning for Enabling IoT-Enabled Healthcare Systems | Chapter 4 | Research and Applications Towards Mathematics and Computer Science Vol. 2

 The Internet of Things (IoT) and machine intelligence (AI) technologies are a organic match for future expansion. IoT uses the computer network to link everything in the sphere. IoT devices have infinite information in chips and sensors because there are many connected devices, which resources there is plenty data that maybe used to empower individuals completely facets of their history. The significant amount of dossier created by IoT devices is excessive for even humans and calculating algorithms to analyse and analyse. Algorithms for machine learning and artificial intelligence so aid in ruling them. The working answer to ruling the numerous linked IoT elements is provided by AI. Learning proficiencies and limitless data conversion that are produced by IoT devices are the important concern. The businesses are engaging machine learning (ML), a potent arm of artificial intelligence (AI), to resolve this problem. The smart wholes give an accurate forecast for asking ML to IoT data. IoT AI requests help businesses decrease unscheduled downtime, create new aids and products, run more capably, and manage risk better. In healthcare, smart homes, independent cars, farming, and marketing, this alliance is mostly employed. This survey study addresses healthcare uses based on machine intelligence (AI) and the internet of belongings (IoT) in these numerous applications. Additionally, it presents a review of various IoT AI algorithms for early illness prophecy in the medical field.

Author(s) Details:

Aanchal Tehlan,
Maharaja Surajmal Institute, IP University, New Delhi, India.

Ankita Moharana,
Department of Pharmaceutics, School of Pharmacy, ARKA JAIN University, Jamshedpur, Jharkhand, India.

Gowri Sankar Chintapalli,
Department of Pharmaceutics, School of Pharmacy, ARKA JAIN University, Jamshedpur, Jharkhand, India.

P. Arockia Mary,
Department of Information Technology, V.S.B. Engineering College, Karur, Tamilnadu, India.

Snigdha Rani Behera,
Department of Pharmaceutical Analysis, School of Pharmacy, ARKA JAIN University, Jamshedpur, Jharkhand, India.

Please see the link here: https://stm.bookpi.org/RATMCS-V2/article/view/11107

Enhancing Manufacturing Processes through Data Analytics in Mastercam Setup Sheets | Chapter 8 | Research and Developments in Engineering Research Vol. 2

 This episode focuses on the benefits of utilizing simulation and forming for data analytics in smart production. The authors highlight the usefulness of imitation models in facilitating data data for various areas, including logistics, administration, transportation, health plans, and manufacturing. The chapter stresses the importance of having an judgment and management system that can accustom to changing operations preparation, production configurations, and manufacturing arrangement development. The authors suggest that established static production preparation methods are not enough for this purpose and that dynamic and correct prototypes of production are essential, which can be attained through simulation models that incorporate legitimate shop floor and production network dossier.This article highlights the benefits of using simulation and forming for data analytics in smart production. Simulation models can aid in data analytics for differing industries, containing logistics, management, conveyance, health systems, and production. It emphasizes the importance of bearing an evaluation and management structure that can adapt to changing movements planning, production configurations, and production system development. Traditional motionless production planning plans are insufficient for this purpose, and dynamic and correct prototypes of result are necessary. Simulation models can incorporate physical shop floor and production network dossier, providing accurate and real-occasion estimations of process efficiency. For organizations that use Setup sheets create by Mastercam, managing the large amounts of dossier for all the machining processes can be a challenge. However, alter the machining data can help programmers appreciate and perform job scene and operations, while inspectors can improve product flow adeptness by using minimal proof. Customized setup sheets can also lower the time required each operation and provide correct real-time and supposed time comparisons for correct analysis of process efficiency. In general, task setup and tool introduction are defined in a convenient manner, ensuring veracity and ease of use. By reducing the amount of sheets while maintaining essential data, institutions can effectively manage revisions of the table with optimum exertions. The customization of machining data can correct process efficiency and accuracy while lowering the time required each operation. The article decides that utilizing imitation and modeling for data data in smart manufacturing can provide many benefits, including improved process adeptness, accuracy, and real-opportunity estimations. Customizing machining data and arrangement sheets can reduce the time necessary for each operation and aid in resolving proper working of processes. These sciences can provide a competitive edge for arrangings and contribute to their gain.

Author(s) Details:

V. V. Shukla,
Shri Ramdeobaba College of Engineering and Management, Nagpur-440013, India.

P. V. Sawalakhe,
Shri Ramdeobaba College of Engineering and Management, Nagpur-440013, India.

J. A. Shaaikh,
Shri Ramdeobaba College of Engineering and Management, Nagpur-440013, India.

M. G. Trivedi,
Shri Ramdeobaba College of Engineering and Management, Nagpur-440013, India.

N. P. Gudadhe,
Shri Ramdeobaba College of Engineering and Management, Nagpur-440013, India.

Please see the link here: https://stm.bookpi.org/RADER-V2/article/view/10437

Study on Artificial Intelligence, Analytics and Agile: Transforming Project Management in the 21st Century| Chapter 4 | Recent Progress in Science and Technology Vol. 1

 There are any of issues that have arisen on account of the introduction of intricate and progressive technology, processes, and mechanization. Agile, analytics, and machine intelligence all work together to improve project administration as a discipline through continuous development and trustworthy execution. This article expands concerning this by demonstrating by what method project management is being enhanced through the use of AI-supported forms, substantial data science of logical analysis, and the Agile methodology. Although electronics is revolutionizing project administration, project managers' importance cannot be understated cause they are the project's main drivers and deliverers very important.

Author(s) Details:

Shivani Gupta,
PMP- Project Management Institute, USA.

Please see the link here: https://stm.bookpi.org/RPST-V1/article/view/9089

Anomaly Detection of Outlier Features from Spatio-temporal Databases of Landsat-8 Sensor, using Cloud Computing Platform | Chapter 12 | Current Topics on Mathematics and Computer Science Vol. 2

 There has recently been a surge in the number of research articles published in peer-reviewed journals about machine learning and specialized algorithms for feature identification, feature selection, and feature extraction studies. This article distinguishes proof-of-concept applications from domains such as computer vision, remote sensing, image processing, and geospatial database technology. Using satellite imagery from the Landsat-8 sensor for rendering in multimedia and scalable vector processing modes, The article validates the fundamentals and principles of digital image analysis. The article describes the RSVM and DAFE scientific methods in the cloud computing platform in detail using a user-defined algorithm and a scientific approach. It is proposed to introduce data analytics nuances in distributed computing and parallel databases.

Author (S) Details

C. Rajabhushanam
Department of Computer Science Engineering, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, Tamilnadu, India.

View Book :- https://stm.bookpi.org/CTMCS-V2/article/view/1784