Authors: Ms. Roshni Shailesh Gupta

Abstract: Machine learning (ML) models are being widely adopted in high-stakes sectors such as healthcare and finance due to their ability to uncover patterns in data and produce predictive insights. However, many of these models function as opaque "black boxes," making it difficult for end-users and stakeholders to understand how specific decisions are derived. This lack of interpretability can erode trust, hinder adoption, and raise ethical and regulatory concerns, particularly when decisions affect individuals' health or financial well-being. Explainable Machine Learning (XML) aims to mitigate these issues by introducing methods that make ML models more transparent and understandable. This paper presents a comprehensive examination of XML techniques, evaluates their implementation across healthcare and finance, and proposes a methodological framework to enhance both accuracy and interpretability in ML systems. The findings highlight that XML is not merely a technical enhancement but a critical enabler of trustworthy, fair, and responsible artificial intelligence (AI).

Published by: vikaspatanker

Authors: Hemant Iklodiya, Madhvi singh Bhanwar

Abstract: Due to massive connectivity and increasing demands of various services and data hungry applications, a full-scale implementation of the fifth generation (5G) wireless systems requires more effective radio access techniques. In this regard, non-orthogonal multiple access (NOMA) has recently gained ever-growing attention from both academia and industry. Compared to orthogonal multiple access (OMA) techniques, NOMA is superior in terms of spectral efficiency and is thus appropriate for 5G and beyond. In this article, we provide an overview of NOMA principles and applications.

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Authors: Seyyed Moslem Mousavi Khademi, Ali Shafiee, Abbas Najafizadeh

Abstract: In this paper, the dissimilar welding microstructure of the duplex stainless steel SFA 2205 with the high strength low alloy A378 Gr.11 was studied.The microstructure investigations indicated that the weld obtained has a two-phase structure, including dendritic and interdendritic areas. A high hardness transition area was detected in the interface of the A378 low alloy steel and ER 309L metal filler. An unmixed area was observable at the melting boundary of SFA2205 duplex steel and both austenitic and duplex filler metals. The results showed that for joining the two-phase stainless steel SFA2205 with the high strength low alloy A378 Gr.11, using the metal filler ER2209 is more appropriate as a result of forming a more suitable properties microstructure.

DOI: https://doi.org/10.5281/zenodo.16266378

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Authors: Dr. Neha Bhat, Mr. Amit Punia

Abstract: The convergence of Customer Relationship Management (CRM) systems with digital marketing techniques has significantly transformed how organizations interact with their customers. In today’s digital economy, data-driven decision-making is essential. By integrating CRM with technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Cloud Computing, businesses can enhance personalization, accurately segment customers, and foster greater loyalty. This paper adopts a computer science-centric approach to examine the architecture, intelligent algorithms, and system integration techniques that enable CRM to serve as an effective digital marketing tool. Real-world case studies from Amazon, Salesforce, and Zoho demonstrate how CRM systems contribute to operational efficiency, improved conversion rates, and long-term customer engagement. A technical framework for AI-enhanced CRM in omnichannel environments is also proposed.

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Authors: Mr. Ankit Sethi, Abhishek

Abstract: The construction industry contributes approximately 39% of global CO₂ emissions, with embodied carbon—emissions from material extraction, manufacturing, and transportation—accounting for 11%. Traditional life cycle assessment (LCA) tools for estimating embodied carbon are often disconnected from Building Information Modeling (BIM) environments and require manual input, limiting their usability during early design stages. This study presents an AI-integrated BIM framework that enables real-time embodied carbon estimation directly within Autodesk Revit. Using Python-based machine learning models—Random Forest, Gradient Boosting, and Support Vector Regression—trained on structural data extracted via Dynamo, the system predicts carbon values and visualizes results through heatmaps in the Revit model. The Random Forest model achieved the highest accuracy (MAE: 5.4 kg CO₂, R²: 0.93) and outperformed traditional tools like One Click LCA in both speed and precision. The framework enhances decision-making during the design phase and demonstrates strong potential for scalable, automated, and sustainable design practices in the built environment

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Authors: Vivek Choudhary, Rahul Kumar Satbhaiya

Abstract: The safety of people inside a building depends on its ability to withstand seismic waves and survive an earthquake with minimal damage and repairs, and without collapsing easily. Various systems are used to absorb seismic energy, including dampers, seismic isolation devices, earthquake-resistant walls, and underground water tanks. The effectiveness of these systems depends on their type and location. In this study, the seismic analysis of a 16-story G+ residential building is carried out based on the analysis of the dynamics of floor shear stress and overturning moment. The ground motion dynamics data are taken from the PEER database. Based on the maximum floor shear stress and maximum overturning moment, the performance of transverse bracing and seismic isolation structures is compared with that of conventional moment structures. By placing these elements at the corners of the building in different models, an efficient and adequate model is obtained.

DOI: https://doi.org/10.5281/zenodo.16410753

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Authors: Vivek Choudhary, Rahul Kumar Satbhaiya

Abstract: The growing demand for resilient high-rise structures in seismic-prone regions has led to the widespread adoption of seismic isolation and bracing systems. This review paper presents a comprehensive evaluation of the structural performance of tall buildings equipped with bracing and base isolation techniques, focusing on their seismic response. Emphasis is placed on research conducted using ETABS software, which provides advanced modeling and analysis tools for evaluating high-rise buildings under seismic loads. Key parameters such as story drift, base shear, lateral displacement, and floor acceleration are examined in the context of various isolation and bracing configurations. The review integrates findings from multiple studies, highlighting the effectiveness of lead-rubber bearings, friction pendulum systems, and bracing systems (X, V, and Z-bracing) in enhancing the lateral stability and energy dissipation capacity of structures. Comparative analyses demonstrate that the combination of base isolation and bracing significantly improves performance compared to conventional fixed-base models. Moreover, the role of soil-structure interaction and building geometry is also discussed to understand their influence on the overall response. This paper concludes that selecting an appropriate seismic control system based on building height, seismic zone, and soil type is critical for optimizing performance. The findings support the continued use of ETABS as a powerful tool for analyzing and designing seismically resistant tall buildings. This review aims to guide engineers, researchers, and designers in selecting efficient seismic mitigation strategies for modern structural systems.

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Authors: Dr. Alla Srivani Professor

Abstract: The InP₁₋ₓAsₓ (Indium Phosphide-Arsenide) ternary semiconductor alloy plays a significant role in quantum computing and optoelectronics, especially in the context of quantum communication, qubit systems, and quantum dot structures. An essential issue in developing semiconductor devices for photo-voltaics and thermo electric is to design materials with appropriate band gaps plus the proper positioning of do pant levels relative to the bands. Ternary Semiconductor alloys provide a natural means of tuning the magnitude of the forbidden gap for wide Application of Semiconductor devices. The need to provide materials for applications in the long-wavelength range for infrared detectors has led to the development of III-V Ternary alloys of InP₁₋ₓAsx Ternary Semiconductor. InP₁₋ₓAsx III-V Ternary semiconductor is very important as an x of a constituent in the semiconductor is going to have significant changes in calculating Physical Property like Band Energy Gap. These Ternary Compounds can be derived from binary compounds InAs and InP by replacing one half of the atoms in one sub lattice by lower valence atoms, the other half by higher valence atoms and maintaining average number of valence electrons per atom. The subscript X refers to the alloy content or concentration of the material, which describes proportion of the material added and replaced by alloy material. This paper represents the InP₁₋ₓAsx III-V Ternary Semiconductor Band Energy Gap values. Our results agree well with the Available data in the literature.

DOI: https://doi.org/10.5281/zenodo.16258617

 

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Authors: Dr. Debasis Ghosh

Abstract: In the evolving landscape of education, digital literacy and e-pedagogy have emerged as critical competencies for both educators and learners. Digital literacy refers to the ability to effectively and critically navigate, evaluate, and create information using a range of digital technologies. E-pedagogy, on the other hand, encompasses the theories and practices of teaching and learning through digital means, emphasizing interactive, student-centered, and technology-integrated approaches. This paper explores the interplay between digital literacy and e-pedagogy, highlighting their role in enhancing educational access, engagement, and outcomes. It examines how educators can develop digital competencies to design inclusive, flexible, and effective digital learning environments. Furthermore, the study investigates challenges such as digital divide, infrastructure limitations, and the need for ongoing professional development. Ultimately, the paper underscores the importance of institutional support, policy initiatives, and pedagogical innovation in fostering a digitally literate and pedagogically adept teaching community capable of meeting 21st-century educational demands.

DOI: https://doi.org/10.5281/zenodo.16257149

 

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Authors: Yashaswini.P, Yathiraj M N

Abstract: This paper provides a comprehensive overview of blockchain technology and explores its application in developing a robust, transparent, and tamper-resistant system to combat the growing issue of counterfeit products in the pharmaceutical industry. Over the past decade, pharmaceutical companies around the world have been grappling with significant challenges in monitoring and tracking their products across the supply chain. These vulnerabilities have created opportunities for counterfeiters to infiltrate the market with fake or substandard medicines, posing serious risks to public health and causing substantial economic losses to legitimate manufacturers. Counterfeit drugs represent a critical global challenge, undermining the integrity of healthcare systems and endangering the lives of millions. These illegitimate products often contain incorrect dosages, harmful ingredients, or no active pharmaceutical ingredients at all, leading to ineffective treatment, prolonged illness, and in some cases, fatal consequences. According to industry statistics, counterfeit drugs are responsible for an estimated $200 billion in annual losses to pharmaceutical companies in the United States alone. Moreover, a World Health Organization (WHO) survey report reveals that in many underdeveloped countries, approximately one out of every ten medicines consumed by patients is counterfeit or of low quality—highlighting the urgent need for a reliable and tamper-proof solution. In response to this pressing issue, our research proposes and implements a blockchain-based drug supply chain management system that leverages the core features of blockchain technology—immutability, decentralization, transparency, and traceability. In conclusion, this research highlights the transformative potential of blockchain technology in securing pharmaceutical supply chains.

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