Arvind W. Kiwelekar

Arvind W. Kiwelekar, Shweta Tembe, Uzma G. A. Munde et al.

Quantum software development has largely focused on algorithms, with limited attention to software architecture. As computing moves toward hybrid quantum-classical systems, this gap limits scalability, reusability, and engineering rigor. This study introduces a component-based quantum software architecture framework (QSAF) for hybrid quantum-classical software systems, enabling developers to transition from circuit-level design to system-level reasoning. We identified 34 reusable quantum circuit primitives across seven functional categories and reinterpreted them as architectural components with explicit interfaces and design-relevant constraints. These components are further characterized using non-functional dimensions such as circuit depth, error sensitivity, and information flow, enabling a structured analysis of design trade-offs. The proposed QSAF framework establishes a multi-level abstraction hierarchy linking quantum gates, circuit primitives, algorithmic structures, and hybrid system architectures. Through this approach, common workflows, particularly hybrid quantum-classical workflows such as variational quantum algorithms, can be systematically decomposed, compared, and optimized. By making the architectural structure and trade-offs explicit, this study provides a foundation for quantum software engineering, supporting modular design, reuse, and informed architectural decision-making in quantum application development.

Manjushree D. Laddha, Varsha T. Lokare, Arvind W. Kiwelekar et al.

Education is the basic step of understanding the truth and the preparation of the intelligence to reflect. Focused on the rational capacity of the human being the Cognitive process and knowledge dimensions of Revised Blooms Taxonomy helps to differentiate the procedure of studying into six types of various cognitive processes and four types of knowledge dimensions. These types are synchronized in the increasing level of difficulty. In this paper Software Engineering courses of B.Tech Computer Engineering and Information Technology offered by various Universities and Educational Institutes have been investigated for Revised Blooms Taxonomy RBT. Questions are a very useful constituent. Knowledge intelligence and strength of the learners can be tested by applying questions.The fundamental goal of this paper is to create a relative study of the classification of the summative assessment based on Revised Blooms Taxonomy using the Convolutional Neural Networks CNN Long Short-Term Memory LSTM of Deep Learning techniques in an endeavor to attain significant accomplishment and elevated precision levels.

Arvind W. Kiwelekar, Pramod Patil, Laxman D. Netak et al.

Fog computing is a paradigm for distributed computing that enables sharing of resources such as computing, storage and network services. Unlike cloud computing, fog computing platforms primarily support {\em non-functional properties} such as location awareness, mobility and reduced latency. This emerging paradigm has many potential applications in domains such as smart grids, smart cities, and transport management. Most of these domains collect and monitor personal information through edge devices to offer personalized services. A {\em centralized} server either at the level of cloud or fog, has been found ineffective to provide a high degree of security and privacy-preserving services. Blockchain technology supports the development of {\em decentralized} applications designed around the principles of immutability, cryptography, consistency preserving consensus protocols and smart contracts. Hence blockchain technology has emerged as a preferred technology in recent times to build trustworthy distributed applications. The chapter describes the potential of blockchain technology to realize security services such as authentication, secured communication, availability, privacy and trust management to support the development of dependable fog services.

Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak et al.

Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.