2026-07-28T17:40:55Z https://ijrems.org/index.php/files/oai

oai:ojs.ijrems.org:article/1 2026-01-06T11:15:58Z files:ART

Data Privacy in the Age of Blockchain: Balancing Transparency and Confidentiality in Financial Transactions P. Nethrasri Regulatory Compliance, Cryptographic Techniques, Immutable Records, Data Encryption, Tokenization, Blockchain Auditing, Privacy-Preserving Blockchain, Distributed Ledger Technology (DLT), Financial Privacy. The blockchain technology is effective, safe and transparent regarding the financial transactions, but the confidentiality of the information is under risk. The presented paper deals with the problem of the compatibility of blockchain transparency and confidentiality of financial transactions. It examines solutions, including privately funded blockchains, zeroknowledge proofs, or cryptography to maintain privacy and also offer regulatory certainty. The study emerges with the threat to privacy, ethical issues and need of effective regulatory frameworks. Finally, it gives its recommendations on how the privacy features of blockchain can be improved without interfering with its core idea of transparency in the financial sector. Kaleido Research Publications LLC 2025-06-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/1 10.65477/ijrems.v1.i1.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 1, June 2025; 1-5 3107-7439 eng https://ijrems.org/index.php/files/article/view/1/1

oai:ojs.ijrems.org:article/2 2026-01-06T11:19:23Z files:ART

The Intersection of AI and Blockchain in Digital Identity Verification Systems Dr. N. Swapna Artificial Intelligence, Blockchain, Digital Identity, Verification Systems, Security This paper is dedicated to the Artificial Intelligence (AI) and Blockchain-based solution in digital identity verification. Blockchain offers secure and immutable storage of any data, and AI enhances accuracy and efficiency because of more advanced data analysis and detection of abnormalities. They are combinedly employed to eliminate central points of failure, improve authentication and protect privacy. However, concerns like regulatory challenges, interoperability of technology and ethics are prevailing. In the conclusion of the paper, it is stated that AI and Blockchain can change a lot but certain further developments and consideration of privacy and regulatory challenges need to be implemented until such technologies can become mainstream Kaleido Research Publications LLC 2025-06-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/2 10.65477/ijrems.v1.i1.02 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 1, June 2025; 6-12 3107-7439 eng https://ijrems.org/index.php/files/article/view/2/2

oai:ojs.ijrems.org:article/3 2026-01-06T11:20:58Z files:ART

Enhancing IoT Security with Blockchain Technology: A Scalable Solution for Device Authentication Dr. Venkateswarlu B. Blockchain, IoT Security, Device Authentication, Decentralization, Scalability Internet of Things (IoT) has overwhelmed the industries creating interdependence among the devices, automation of the system, and data sharing. Security issues have however come into place and in most cases the traditional measures might not be sufficient. The decentralized and transparent manner that is one of the blockchain technology applications might hold the answer in ensuring security of IoT systems, as it has been used in device authentication. With the help of an immutable ledger, offered by blockchain, the IoT devices can be registered, authenticated, and verified in a secure way, without involving central authorities, which limits the chances of a cyberattack. The concept of blockchain and IoT combination to offer scalable, secure, and efficient authentication system and its opportunities, disadvantages, and advantages in ensuring the data integrity of interconnected devices is described in the paper. Kaleido Research Publications LLC 2025-06-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/3 10.65477/ijrems.v1.i1.03 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 1, June 2025; 13-18 3107-7439 eng https://ijrems.org/index.php/files/article/view/3/3

oai:ojs.ijrems.org:article/4 2026-01-06T11:24:30Z files:ART

Understanding the Legal Implications of Blockchain Forensics: Regulatory Challenges and Policy Development Dr. Anjaiah Adepu Blockchain, Forensics, Legal Implications, Regulatory Challenges, Policy Development The fact that blockchain technology is decentralized, transparent, and immutable is transforming the face of such industries as finance, healthcare, and logistics. It is nonetheless, difficult in regulatory compliance, data privacy, and law enforcement, specifically blockchain forensics. Blockchain forensics is an activity of tracking transactions and members of illegal organizations like money laundering and cybercrime. Whereas the traceability of the blockchain technology with the transparency it possesses raises no more concerns on the legal issues, the pseudonymity of its participants, on the other hand, makes it quite difficult to identify them, respectively, creating issues within the scope of the data protection, as well as financial regulations. The paper will touch on the practice today of forensics, the regulation and morality of the balance that is there between privacy and criminal investigation. It ends with suggestions of a joint effort in creation of efficient legal frameworks to govern the same, and promotion of innovation. Kaleido Research Publications LLC 2025-06-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/4 10.65477/ijrems.v1.i1.04 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 1, June 2025; 19-24 3107-7439 eng https://ijrems.org/index.php/files/article/view/4/4

oai:ojs.ijrems.org:article/5 2026-01-06T11:25:40Z files:ART

Decentralized Finance (DeFi) and Its Impact on Traditional Financial Systems: A Comparative Analysis Dr. Aakunuri Manjula Financial Systems, Smart Contract, TF, DeFi, Blockchain It is stated that the Decentralized Finance (DeFi) is transforming the financial industry because it provides its users with such services as lending, borrowing, trading, and insurance on decentralized terms using blockchain technology. DeFi is cheaper, transparent, and secure because it is developed on decentralized platforms, including Ethereum. But it is also encircled by such threats as regulatory risk, security risk and market risk. In this paper, the comparison of the DeFi and traditional financial systems will be presented involving such central capabilities of the DeFi as decentralized exchanges, liquidity pools, and lending platforms. It also mentions the discrepancies in rules, the positives of financial inclusions and the DeFi negatives such as volatility and absence of consumer protections. The paper gives a concluding look at what traditional institutions and regulators can do to react and cooperate with DeFi. Kaleido Research Publications LLC 2025-06-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/5 10.65477/ijrems.v1.i1.05 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 1, June 2025; 25-31 3107-7439 eng https://ijrems.org/index.php/files/article/view/5/5

oai:ojs.ijrems.org:article/7 2026-01-07T05:16:35Z files:ART

Exploring AI-Driven Approaches to Enhance Blockchain Forensics in Cryptocurrency Fraud Detection Dr. K. Vaishali Blockchain Forensics, Cryptocurrency Fraud, Artificial Intelligence, Machine Learning, Fraud Detection In this paper, the author would elaborate on how Artificial Intelligence (AI) and machine learning (ML) may be utilized to improve blockchain forensics in detecting cryptocurrency frauds. Although blockchain provides a safe platform to carry out transactions using cryptocurrencies, fraudster transactions, including double-spending and money laundering, are major demerits. Scalability and efficiency are two downsides of the conventional approaches to blockchain forensics. According to the paper, AI-based (supervised and unsupervised) machine learning models may be applied to process blockchain information and better identify suspicious transactions. Their outcomes have revealed that the AI models including the decision trees, neural networks, and support vector machines are efficient in identifying complex fraud patterns compared to the traditional approaches. The paper has clarified that both AI and ML solutions will find their applicability in making the blockchain more secure and countering the intelligence of cryptocurrency frauds that is so far on the rising trend. Kaleido Research Publications LLC 2025-07-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/7 10.65477/ijrems.v1.i2.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 2, July 2025; 1-7 3107-7439 eng https://ijrems.org/index.php/files/article/view/7/6

oai:ojs.ijrems.org:article/9 2026-01-07T05:17:54Z files:ART

The Role of Quantum Computing in Strengthening Blockchain Security and Privacy Protocols Dr. Padmaja Pulicherla Quantum Computing, Blockchain Security, Cryptography, Quantum-Resistant Algorithms, Privacy Protocols Blockchain is a huge component of decentralized systems, and it provides a safe and transparent way of carrying out transactions. It is though susceptible to quantum computing that could crack the RSA and ECC traditional encryption algorithms. This paper explains the risk of quantum computers and specifically of Shor Algorithm and the study of quantumresistant cryptography, such as lattice-based cryptography, to secure blockchain systems. It also investigates quantum-enhanced encryption, e.g., quantum key distribution (QKD) which can make blockchain unbreakable encryption. The article substantiates the relevance of quantum-resistant solutions, which are being elaborated today to ensure the safety and confidentiality of blockchain in the future. Kaleido Research Publications LLC 2025-07-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/9 10.65477/ijrems.v1.i2.02 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 2, July 2025; 8-12 3107-7439 eng https://ijrems.org/index.php/files/article/view/9/8

oai:ojs.ijrems.org:article/10 2026-01-07T05:19:15Z files:ART

Towards Autonomous Blockchain Governance: Decentralized Systems and the Future of Smart Contracts Swamy Akunoori Autonomous Governance, Blockchain, Decentralized Systems, Smart Contracts, Decentralized Autonomous Organizations (DAOs). Finance, supply chain, and decentralized applications are some of the industries that have undergone a revolution in relation to blockchain technology, and smart contracts are at the center of this revolution. Smart contracts are computer protocols that are programmed on blockchain systems and which allow transparency, immutability, and decentralization. Nonetheless, governance in a blockchain is a problem area, because the conventional centralized systems are inconsistent with its decentralised characteristic. This article discusses self-governance of blockchain whereby decision making is computerized using smart contracts to achieve decentralized regulations. It reviews the prevailing conditions in blockchain governance, issues and the way smart contracts would enhance transparency, efficiency and security. Also provided in the study are the advantages and drawbacks of decentralized governance, which includes issues of scalability and security, and the möbius strip connection between autonomous governance and blockchain platforms. Moreover, it assesses the place of decentralized autonomous organizations (DAOs) in blockchain governance and the issues of their implementation. Kaleido Research Publications LLC 2025-07-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/10 10.65477/ijrems.v1.i2.03 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 2, July 2025; 13-17 3107-7439 eng https://ijrems.org/index.php/files/article/view/10/9

oai:ojs.ijrems.org:article/11 2026-01-07T05:20:11Z files:ART

AI and Blockchain Integration for Sustainable Supply Chain Transparency: Challenges and Opportunities Vaskula Srikanth Artificial intelligence, Blockchain, Supply chain transparency, Sustainability, Integration of technology The global supply chain is a complicated chain and it has the suppliers, manufactures, distributors and the consumers. The old systems have been associated with inefficiencies, lack of real time information, visibility that leads to fraud, delays and environmental wastages. AI and Blockchain technologies provide the opportunities to make the supply chains more visible and sustainable. blockchain-based decentralized immutable ledger can be used to provide secure verifiable data along the chain and AI can be used to optimise the processes and predict demand and inefficiencies. When the latter arrives in combination, traceability is boosted, fraud is driven to minimum and ethical sourcing plus sustainability are encouraged. The given paper establishes AIs and Blockchain combination and mentions such advantages of the partnership as real-time monitoring or predictive analytics. Nevertheless, the issue of data confidentiality, size and legal issues need to be taken care of in order to have a successful implementation. The industry has case studies and examples of their application that can give some idea of how they have been applied in practice and some idea of how the obstacles to adoption can be lessened and how sustainable practice can be achieved. Kaleido Research Publications LLC 2025-07-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/11 10.65477/ijrems.v1.i2.04 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 2, July 2025; 18-23 3107-7439 eng https://ijrems.org/index.php/files/article/view/11/11

oai:ojs.ijrems.org:article/12 2026-01-07T05:21:40Z files:ART

Pattern Recognition in Blockchain Networks: Using Deep Learning for Fraud Detection and Prevention Pulagara Madhumitha Artificial Intelligence, Pattern Recognition, Fraud Detection, Deep Learning, Blockchain; Blockchain technology has decentralized, secure and transparent systems to deal with digital transaction frauds or any theft like a double-spending attack and phishing attack but it is prone to fraud. With the increase in the use of blockchain, these fraudulent activities are difficult to mention. It encompasses deep learning, primarily convolutional neural networks (CNNs), recurrent neural networks (RNNs) and autoencoders, which could be used in detecting fraud based on transaction data to detect abnormalities. According to the paper, deep learning for fraud detection in blockchain networks has been explained and its efficiency in terms of security and implementation challenges were known. It also covers the prospects of the deep learning in future to revamp the block chain security. Kaleido Research Publications LLC 2025-07-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/12 10.65477/ijrems.v1.i2.05 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 2, July 2025; 24-29 3107-7439 eng https://ijrems.org/index.php/files/article/view/12/10

oai:ojs2.ijrems.edutechy.xyz:article/13 2025-10-08T06:11:05Z files:ART

oai:ojs2.ijrems.edutechy.xyz:article/15 2025-10-08T06:12:54Z files:ART

oai:ojs2.ijrems.edutechy.xyz:article/16 2025-10-13T06:37:46Z files:ART

oai:ojs.ijrems.org:article/17 2026-01-07T05:38:48Z files:ART

Blockchain–AI Hybrid Models for Supply Chain Security: A Secondary Data Synthesis Dr. Syed Hassan Imam Gardezi Regulatory Compliance, Cryptographic Techniques, Immutable Records, Data Encryption, Tokenization, Blockchain Auditing, Privacy-Preserving Blockchain, Distributed Ledger Technology (DLT), Financial Privacy Supply chains all over the world become more computerized, which subjects them to cyberattacks, fraud, counterfeiting, and data manipulations. Distributed and tamper-evident ledgers are available through blockchain, and predictive analytics, anomaly recognition, and intelligent decision-making is provided by Artificial Intelligence (AI). Hybrid models through the combination of such technologies provide secure, transparent, and adaptive supply chains. The current paper is a synthesis of secondary data (20192025) of scholarly journals, industry reports, and international bodies in order to analyze the value of blockchain-AI hybrid systems in improving the security of the supply chain. We overview the use cases in manufacturing, logistics, pharmaceuticals, and food industries and extract the major areas of integration: blockchain to be more data integrity and provenance, and AI to be more analytics and forecast, risk detection. We introduce a comparative table of blockchain-only, AI-only and hybrid models, conceptual hybrid architecture figure, flow diagram of information flow. The results show that the hybrid systems enhance resilience, detecting fraud, and traceability through the connection between unaltered records and adaptive intelligence. Scalability, interoperability, governance and data quality continue to be problematic. The way forward in work should be the standardization of interfaces, guarantee privacy, and create cross-sector models of reliable hybrid supply chain. Kaleido Research Publications LLC 2025-11-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/17 10.65477/ijrems.v1.i5.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 5, October 2025; 1-7 3107-7439 eng https://ijrems.org/index.php/files/article/view/17/14

oai:ojs.ijrems.org:article/18 2026-01-07T05:36:34Z files:ART

Digital Twins with AI for Predictive Maintenance: A Secondary Data Synthesis Dr. Syed Hassan Imam Gardezi Digital Twin; Artificial Intelligence; Predictive Maintenance; IoT; Condition Monitoring; Secondary Data Synthesis Digital Twin (DT) technology, when combined with Artificial Intelligence (AI), has emerged as a transformative approach for predictive maintenance in modern industries. This paper synthesizes secondary evidence from academic literature, industry reports, and international standards (2018–2025) to examine how AI-enhanced digital twins are enabling predictive maintenance across manufacturing, energy, and transportation. Digital twins replicate physical assets virtually, while AI models analyze real-time sensor data to detect anomalies, forecast failures, and optimize maintenance schedules. Using structured analysis of ISO standards, Gartner, McKinsey, and IEEE literature, we map how digital twins integrate with IoT data streams, machine learning models, and maintenance workflows. The study identifies key enabling technologies (IoT, cloud computing, ML/DL), common architectural layers, and documented benefits, such as reduction in unplanned downtime (30–50%) and improved asset life cycles. A conceptual flowchart illustrates the AI–DT predictive maintenance loop, and a table compares sectoral adoption patterns. Findings highlight the convergence between AI analytics and DT simulations, enabling data-driven, condition-based maintenance strategies. Challenges remain in data interoperability, model updating, and cybersecurity. Future work should focus on standardized frameworks and cost-benefit models for broader adoption. Kaleido Research Publications LLC 2025-11-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/18 10.65477/ijrems.v1.i4.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 4, September 2025; 1-5 3107-7439 eng https://ijrems.org/index.php/files/article/view/18/15

oai:ojs.ijrems.org:article/20 2026-06-30T08:31:16Z files:ART

The Impact Of Employee Welfare Initiatives On Job Satisfaction Within Manufacturing Companies Radha Dr.Sathyanarayana Employee welfare measures, Job satisfaction, Link between Employee welfare measures, Employee turnover. Despite the fact that manufacturing is essential to global economies, the nature of the work usually causes emotional, mental, and physical stress for employees. Given the high demands of the sector, employee welfare measures are crucial for ensuring worker well-being and job satisfaction. This study looks at the relationship between work satisfaction and employee welfare measures in manufacturing businesses to better understand how various welfare initiatives could improve organizational performance, reduce turnover, and promote employee satisfaction. The study highlights how comprehensive welfare policies affect workers' job satisfaction by examining the functions of work-life balance, financial benefits, professional growth possibilities, social security, and health and safety initiatives. The results of the study show that employee welfare programs are crucial to establishing a positive work environment. Kaleido Research Publications LLC 2025-12-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/20 10.65477/ijrems.v1.i6.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 6, November 2025; 1-9 3107-7439 eng https://ijrems.org/index.php/files/article/view/20/19 Copyright (c) 2025 International Journal of Research in Engineering and Management Sciences

oai:ojs.ijrems.org:article/21 2026-01-06T11:33:33Z files:ART

Ethical AI Implementation in Corporate Decision-Making Dr. Syed Hassan Imam Gardezi Ethical AI, corporate decision-making, AI governance, Algorithmic accountability, Responsible innovation. AI has become a part of the corporate decision-making process and has contributed to strategic planning, risk evaluation, human resource management, customer analytics, and financial predictions. Although the AI systems have strong efficiency, accuracy, and scalability benefits, they are becoming more autonomous, which leads to important ethical issues of fairness, transparency, accountability, and trust. This paper is an analysis of the ethical application of artificial intelligence into corporate decision-making, including the framework of governance, ethics and organizational practices that guarantee responsible adoption of artificial intelligence. The paper combines information in academic literature and international policy guidelines, and corporate governance reports by utilizing a descriptive and analytical research design based on secondary data. The results show that the ethical application of AI can substantially improve the quality of decisions, stakeholder trust, and the sustainability of the organization in the long term in case it is supported by ethical principles (transparency, explainability, accountability, and human control). Nevertheless, there are still difficulties such as the possibility of algorithmic bias, data privacy risks, uninterpretability, and uncertainty in regulation that do not promote successful ethical integration. The research emphasizes that leadership plays a significant strategic role, ethical governance arrangements, and cross-functionality in entrenching ethical AI in the business decision making. This study presents the current discussion on responsible AI by combining theoretical knowledge about ethics with practical business concerns and offers an idea of what organizations need to do to find a compromise between innovation and ethical accountability. Kaleido Research Publications LLC 2025-06-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/21 10.65477/ijrems.v1.i1.06 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 1, June 2025; 32-36 3107-7439 eng https://ijrems.org/index.php/files/article/view/21/16

oai:ojs.ijrems.org:article/22 2026-01-07T05:26:08Z files:ART

E-Learning Entrepreneurship: A Global Economic Perspective Dr. Syed Hassan Imam Gardezi E-learning, Entrepreneurship, Digital Economy, Global Education Market, Economic Development. The rapid development of the digital technologies has changed the face of education in the world, creating the e-learning entrepreneurship as one of the contributing factors to the economic growth, innovation, and the creation of job opportunities. E-learning entrepreneurship describes the formation and growth of online and technology-based digital education projects, which provide learning content, platforms, and services. This paper analyzes the e-learning entrepreneurship in the context of the global economy with focus on the role it plays in knowledge economies, human capital development and inclusive growth. The paper presents a synthesis of secondary data through international reports, peer-reviewed literature, and global market analysis in the e-learning ecosystem utilizing a descriptive research design and analytical research design to identify the major trends, economic effects, and entrepreneurship models. The results show that e-learning projects are critical in democratizing education, minimizing skill discrepancies, innovative advancements, and sustainable economic growth especially in the emergent economies. Nonetheless, the issues of digital inequality, regulation, quality assurance, and financial sustainability continue to exist. The research points out policy implications, business opportunities, and strategic focus areas to boost e-learning economy in the world. This study brings together entrepreneurship, education and economic development lenses to the expanding literature on digital entrepreneurship and provides clues to policymakers, educators and entrepreneurs wishing to use e-learning to have a long term economic effects. Kaleido Research Publications LLC 2025-07-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/22 10.65477/ijrems.v1.i2.06 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 2, July 2025; 30-35 3107-7439 eng https://ijrems.org/index.php/files/article/view/22/17

oai:ojs.ijrems.org:article/23 2026-01-07T05:34:22Z files:ART

Digital Transformation Strategies in Post-Pandemic Enterprises Dr. Syed Hassan Imam Gardezi Digital Transformation, Post-Pandemic Enterprises, Business Resilience, Digital Strategy, Organization agility, Industry 4.0. The COVID19 pandemic served as a digital transformation catalyst as it has never been seen before in the global enterprise arena. What started as an emergency measure to guarantee business continuity has since become a long-term competitive, resilience, and innovation measure. The critical analysis of the digital transformation strategies embraced by the post-pandemic business presented in this research paper focuses on the aspects of the organizational agility, the integration of technology, and the decision-making process based on data and the transformation of the workforce. The paper combines findings of enterprise practices, developing technologies, and strategic management literature to offer a synthesis using an integrative analytical approach that is based on the recent digital strategy frameworks. The article suggests an abstract framework that connects digital capabilities and organizational performance as well as sustainable growth in the post-pandemic economy. The evidence indicates that effective digital transformation goes beyond implementation of technologies into the company, but it involves profound cultural adjustment, executive dedication, and sustained development of capabilities. Kaleido Research Publications LLC 2026-01-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/23 10.65477/ijrems.v1.i3.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 3, August 2025; 1-7 3107-7439 eng https://ijrems.org/index.php/files/article/view/23/18

oai:ojs.ijrems.org:article/24 2026-01-07T05:51:25Z files:ART

Data-Driven Marketing: Predicting Consumer Behavior Using Artificial Intelligence Dr. Syed Hassan Imam Gardezi Data-driven marketing, Artificial intelligence, Consumer behavior, Predictive analytics, Digital marketing. The proliferation of digital platforms and consumer data has turned the current state of marketing into a data-driven practice in which predictive analytics and artificial intelligence (AI) are key. Data-driven marketing is a marketing approach that uses market data, in both structured and unstructured forms, to predict customer preferences, maximize customer experiences, and streamline decision-making. This research paper discusses the application of artificial intelligence in consumer behavior prediction with respect to economic, strategic, and technological implication. The paper synthesizes findings on AI-based marketing models by using an analytical and descriptive research design using secondary data sources. The results indicate that AI will improve consumer behavior prediction based on machine learning algorithms, natural language processes, and real-time analytics, which will improve consumer engagement, conversion rates, and marketing effectiveness. Nevertheless, there are still issues associated with data privacy, algorithmic bias, ethical governance, and model interpretability. The research points out the role that organizations can play in integrating AI in marketing functions in a strategic way without transparency and consumer trust. This study advances the body of knowledge in marketing and offers practical implications to businesses aiming to achieve a competitive advantage through data-driven and AI-enables marketing practices because it presents a detailed conceptual framework. Kaleido Research Publications LLC 2026-01-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/24 10.65477/ijrems.v1.i7.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 7, December 2025; 1-6 3107-7439 eng https://ijrems.org/index.php/files/article/view/24/20

oai:ojs.ijrems.org:article/25 2026-06-30T08:35:09Z files:ART

Image Upscaling Using AI Dr. P. Padmaja Kaushik Pendem Image Upscaling, Diffusion Models, Super-Resolution, Stable Diffusion ×4, Tile-Based Processing, VRAM Optimization, Deep Learning, Image Enhancement. Through creating an AI-driven picture upscaling solution using diffusion models, the study tackles the increasing need for improved visual material. Using a VRAM-efficient tile-based processing method, the system improves low-resolution pictures, allowing for super-resolution on regular GPU hardware.To overcome memory limitations that might occur while processing huge photos, the suggested approach uses Stable Diffusion ×4 upscaling technology in conjunction with sophisticated tiling methods. By reducing pictures to smaller tiles and using diffusion-based enhancement approaches, the method significantly increases detail while keeping computation efficient.The main characteristics of the system are its ability to use advanced diffusion models to drive upscaling, its compatibility with common GPU setups, its tile-based processing for optimizing VRAM, and its increased accessibility to low-hardware super-resolution technologies.Prototype successfully improves picture quality, lowering the barrier to entry for sophisticated AI upscaling methods that need expensive HPC infrastructure. Kaleido Research Publications LLC 2026-04-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/25 10.65477/ijrems.v2.i4.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2 , Issue 4, April 2026; 1-6 3107-7439 eng https://ijrems.org/index.php/files/article/view/25/21 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/27 2026-06-05T10:26:04Z files:ART

Blockchain for Sustainable Fisheries Management and Traceability Systems Anamika Srivastava Blockchain for traceability, Smart Contracts for mitigating IUU fishing, Internet of Things (IoT) in the seafood supply chain. The world's fisheries play a significant role in food security, livelihood, and world trade. However, governance, biodiversity and economic sustainability are compromised by overfishing, illegal, unreported, and unregulated (IUU) fishing, and murky supply chains. Traditional traceability tools lack transparency and real-time verification, which slows enforcement and makes it difficult to effectively enforce policy and build consumer confidence. New technologies such as blockchain hold promise for transforming the seafood supply chain, providing a decentralized, transparent, and blockchain-based way to share data across the value chain. This study aims to explore the potential of using blockchain technology, sensors, and smart contracts in the Internet of Things (IoT) to improve fisheries management and traceability. The results show that blockchain has the potential to greatly enhance traceability, compliance, and stakeholder trust when tested through prototype simulations, stakeholder interviews, and technology evaluations. Standardization, cost, scalability, and uptake issues still exist. The measures needed to address these challenges are discussed, and a roadmap for the wider application of blockchain for sustainable fisheries is provided. Kaleido Research Publications LLC 2026-05-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/27 10.65477/ijrems.v2.i5.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2, Issue 5, May 2026; 1-9 3107-7439 eng https://ijrems.org/index.php/files/article/view/27/22 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/28 2026-06-05T10:26:04Z files:ART

Circular Economy Models For Sustainable Industrial Growth Dr. Muneerahamad Hunagund Circular Economy, Sustainable Growth, Industrial Development, Resource Efficiency, Recycling, Closed-loop Systems, Economic Benefits, Challenges The Circular Economy (CE) has been gaining attention as a key model to encourage sustainable industrial development, aimed at minimizing waste, using resources in closed-loop systems, and reducing resource consumption. This paper examines the potential applications of circular economy models to improve industrial development and mitigate environmental issues. In this study, the key drivers, challenges, and opportunities for implementing the circular economy across different industries were identified through data analysis, industry case studies, and expert interviews. In addition, the paper presents results from a survey of 50 industrial companies and offers a detailed overview of the advantages and challenges associated with the transition to a more circular economy. The study indicates that the economic and environmental impacts of transitioning to a circular economy may be large, but there are also technical, financial and regulatory challenges to be overcome. Kaleido Research Publications LLC 2026-05-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/28 10.65477/ijrems.v2.i5.02 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2, Issue 5, May 2026; 10-14 3107-7439 eng https://ijrems.org/index.php/files/article/view/28/23 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/29 2026-06-05T10:26:04Z files:ART

Nanomaterial-Based Sensors For Environmental Monitoring: A Review Anirudh Gupta Archita Sen Shweta Chandel Nikita Choudhary Indu Sharma Nanomaterials, Environmental monitoring, Sensors, Metal nanoparticles, Carbon nanotubes, Graphene, Metal-Organic Frameworks (MOFs), Quantum dots, Electrochemical sensors Environmental monitoring has become a major focus for nanomaterial sensors due to their high sensitivity, selectivity, and flexibility in detecting pollutants and contaminants. These sensors exploit the distinctive characteristics of nanomaterials such as metal nanoparticles, carbon-based nanomaterials, metal-organic frameworks (MOFs), quantum dots, and nanocomposites to improve sensing performance in air, water, soil, and biological systems. This paper examines the various nanomaterials used in sensors, their sensing mechanisms, and their applications in environmental monitoring with emphasis on real-time, portable, and low-cost sensors. It also includes challenges related to sensitivity, stability, scalability, and environmental compatibility, as well as recent developments and future prospects in nanomaterial-based sensor technologies. The potential of integrating nanomaterials with the Internet of Things (IoT) for smart, connected monitoring is also discussed. Kaleido Research Publications LLC 2026-05-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/29 10.65477/ijrems.v2.i5.03 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2, Issue 5, May 2026; 15-24 3107-7439 eng https://ijrems.org/index.php/files/article/view/29/24 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/31 2026-06-30T08:34:07Z files:ART

Study on Impact of Employee Engagement on Productivity In Manufacturing Sectors Munimada S Radha R Career contentment, Extra-role behaviour, Employee commitment, employee engagement and employee motivation. In the manufacturing industry, where accuracy and efficiency are critical, employee engagement is especially important for boosting production and creating a competitive edge. This research investigates the connection between employee engagement and productivity, focusing on how engaged employees contribute to operational excellence, reduced turnover, and enhanced innovation. Through a review of existing literature and analysis of case studies from leading manufacturing organizations, the study identifies key engagement drivers such as effective communication, recognition, professional development, and workplace safety. The findings suggest that organizations with high levels of involvement among employees experience significant improvements in production efficiency, quality control, and employee morale. Conversely, low engagement often correlates with higher absenteeism, increased errors, and reduced output. By addressing barriers such as repetitive tasks and limited growth opportunities, manufacturing firms can leverage engagement strategies to optimize productivity. This study concludes by recommending actionable strategies for fostering a culture of engagement, underscoring its transformative potential for manufacturing operations. Kaleido Research Publications LLC 2026-01-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/31 10.65477/jrems.v2.i1.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2, Issue 1, January 2026; 01-08 3107-7439 eng https://ijrems.org/index.php/files/article/view/31/25 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/32 2026-06-30T08:34:07Z files:ART

Transforming Oncology Imaging with Foundation Models for Intelligent Radiology Report Generation Dr. Sneha T. Waghmare Dr. Meera N. Nair Dr. Arjun K. Verma Mr. Shatrughna U. Nagrik Foundation models; Automated radiology report generation; Oncology imaging; Large language models; Vision-language models; Artificial intelligence; Medical imaging; Precision oncology. Radiology reports are the primary means of communication between radiologists and referring clinicians, providing essential information for disease diagnosis, treatment planning, therapeutic response assessment, and longitudinal patient management. In oncology, the increasing volume, complexity, and multimodal nature of imaging examinations have created a growing demand for reporting systems that are both accurate and efficient. Recent advances in artificial intelligence (AI), particularly the emergence of foundation models, have significantly transformed automated radiology report generation. Trained on large-scale multimodal datasets, foundation models learn generalized representations that can be adapted to a wide range of clinical tasks, enabling the generation of coherent, context-aware, and clinically meaningful radiology reports through the integration of medical imaging and natural language processing. This review explores the evolution of automated radiology report generation, tracing its progression from traditional rule-based systems and convolutional neural network (CNN)-based approaches to transformer architectures, vision-language models, and multimodal large language models (LLMs). Particular emphasis is placed on their applications in oncology imaging, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and hybrid imaging modalities. The review further examines major foundation model architectures, publicly available datasets, evaluation benchmarks, clinical applications, and the advantages and limitations of current methodologies. In addition, key challenges associated with clinical implementation—including model hallucination, limited explainability, data heterogeneity, privacy protection, regulatory compliance, and the need for rigorous prospective validation—are critically discussed. Current evidence indicates that foundation models outperform earlier AI-based approaches by improving report quality, contextual understanding, linguistic coherence, and cross-domain generalizability. Nevertheless, several technical and clinical challenges must be addressed before these systems can be safely integrated into routine oncology practice. Future research should prioritize the development of domain-specific multimodal foundation models, federated learning frameworks for privacy-preserving collaboration, explainable AI techniques to enhance clinical trust, and large-scale prospective validation studies. As these technologies continue to mature, foundation models have the potential to transform radiology workflows by improving reporting efficiency, consistency, diagnostic accuracy, and clinical decision support, ultimately contributing to more precise and personalized cancer care. Kaleido Research Publications LLC 2026-01-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/32 10.65477/ijrems.v2.i1.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2, Issue 1, January 2026; 09-18 3107-7439 eng https://ijrems.org/index.php/files/article/view/32/26 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/33 2026-06-30T08:34:33Z files:ART

Deep Learning–Driven Integration of Radiological and Genomic Data for Precision Cancer Care Dr. Jayanthi Kanaka Ram Precision oncology; Multimodal deep learning; Radiogenomics; Artificial intelligence; Medical imaging; Genomics; Multimodal data fusion; Foundation models. Precision oncology aims to deliver personalized cancer diagnosis, prognosis, and treatment by integrating the molecular and phenotypic characteristics of individual tumors. Rapid advances in high-throughput genomic sequencing and medical imaging technologies have generated vast volumes of heterogeneous data, offering unprecedented opportunities to characterize tumor biology comprehensively. Radiological imaging provides non-invasive insights into tumor morphology, spatial heterogeneity, and therapeutic response, while genomic profiling reveals the molecular alterations underlying tumor initiation, progression, metastasis, and treatment resistance. However, conventional analytical methods are often limited in their ability to capture the complex, nonlinear relationships that exist across these complementary data modalities. Multimodal deep learning has emerged as a transformative computational framework for integrating radiological and genomic information, enabling more comprehensive and accurate clinical decision-making in precision oncology. Leveraging advanced neural network architectures—including convolutional neural networks, transformers, graph neural networks, and multimodal fusion models—these approaches can identify latent associations between imaging phenotypes and genomic signatures that are not readily discernible through traditional analyses. Recent studies have demonstrated that multimodal integration consistently outperforms unimodal models across a range of applications, including cancer detection, molecular subtyping, prognostic prediction, treatment response assessment, biomarker discovery, and patient risk stratification. Despite these promising advances, several challenges continue to hinder widespread clinical adoption. Limited availability of large, well-annotated multimodal datasets, data heterogeneity across institutions, model interpretability, privacy and security concerns, computational complexity, and regulatory considerations remain significant barriers to implementation. At the same time, the emergence of foundation models, self-supervised learning, and large-scale multimodal pretraining is reshaping the field by enabling robust representation learning from extensive unlabeled medical datasets and improving model generalizability across diverse clinical settings. This review provides a comprehensive overview of recent advances in multimodal deep learning for integrating radiological imaging and genomic data in precision oncology. It discusses key methodological developments, radiogenomic integration strategies, multimodal fusion architectures, clinical applications, current limitations, and emerging trends, with particular emphasis on foundation models and next-generation artificial intelligence frameworks that have the potential to accelerate the translation of precision oncology into routine clinical practice. Kaleido Research Publications LLC 2026-02-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/33 10.65477/ijrems.v2.i2.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2, Issue 2, February 2026; 01-12 3107-7439 eng https://ijrems.org/index.php/files/article/view/33/27 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/34 2026-06-30T08:34:48Z files:ART

Artificial Intelligence for Automated Tumor Segmentation and Treatment Response Assessment Dr. Jayanthi Kanaka Ram Dr. Karan A. Bhattacharya Dr. Shalini R. Nair Dr. Vivek P. Rao, Artificial intelligence; Tumor segmentation; Treatment response assessment; Deep learning; Radiomics; Precision oncology; Medical imaging; Foundation models Cancer remains a leading cause of mortality worldwide, underscoring the urgent need for continuous innovation in diagnostic and therapeutic strategies. Medical imaging is a cornerstone of modern oncology, supporting tumor detection, delineation, staging, treatment planning, and longitudinal assessment of therapeutic response. Accurate tumor segmentation and treatment response evaluation are critical for effective clinical decision-making; however, conventional manual methods are often labor-intensive, time-consuming, and prone to considerable interobserver variability. The rapid advancement of artificial intelligence (AI), particularly machine learning and deep learning, has revolutionized medical image analysis by enabling automated, reproducible, and highly accurate interpretation of complex imaging datasets. Recent progress in convolutional neural networks, transformer-based architectures, and foundation models has substantially improved the accuracy and robustness of tumor segmentation across diverse imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and hybrid imaging techniques. Concurrently, AI-powered methodologies have enhanced treatment response assessment through radiomics, longitudinal image analysis, and predictive modeling, facilitating earlier detection of therapeutic effectiveness, disease progression, and treatment resistance. Furthermore, emerging multimodal AI frameworks that integrate imaging, digital pathology, genomic information, and clinical data are advancing precision oncology by providing comprehensive, patient-specific insights for personalized cancer management. Despite these significant achievements, several challenges continue to limit the widespread clinical adoption of AI technologies. Data heterogeneity, limited availability of high-quality annotated datasets, model interpretability, regulatory and ethical considerations, and integration into existing clinical workflows remain important obstacles. Nevertheless, emerging paradigms—including foundation models, self-supervised learning, federated learning, and explainable artificial intelligence—offer promising solutions to improve model robustness, generalizability, transparency, and scalability across diverse healthcare environments. This review provides a comprehensive overview of recent advances in AI-driven automated tumor segmentation and treatment response assessment. It examines current methodological developments, summarizes major clinical applications, critically discusses existing limitations, and highlights future research directions for successfully integrating artificial intelligence technologies into routine oncology practice, ultimately supporting more precise, efficient, and personalized cancer care. Kaleido Research Publications LLC 2026-03-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/34 10.65477/jrems.v2.i3.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2, Issue 3, March 2026; 01-14 3107-7439 eng https://ijrems.org/index.php/files/article/view/34/28 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/35 2026-06-30T08:35:09Z files:ART

Predicting Immunotherapy Response in Solid Tumors Using Radiomics and Machine Learning Dr. Jayanthi Kanaka Ram Mr. Shatrughna U. Nagrik Dr. Priya M. Krishnan Dr. Rohit T. Chatterjee Radiomics; Machine learning; Immunotherapy; Immune checkpoint inhibitors; Precision oncology; Radiogenomics; Artificial intelligence; Solid tumors. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of multiple solid tumors, including non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma, hepatocellular carcinoma, and urothelial carcinoma. Despite substantial clinical advances, durable therapeutic responses are observed in only a subset of patients, underscoring the need for accurate and reliable predictive biomarkers. Conventional biomarkers, including programmed death-ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI), demonstrate limited predictive performance owing to spatial and temporal tumor heterogeneity and the dynamic nature of the tumor microenvironment. Radiomics has emerged as a promising non-invasive approach that extracts high-dimensional quantitative imaging features capable of characterizing tumor phenotype, intratumoral heterogeneity, and the surrounding microenvironment. Simultaneously, advances in machine learning have enabled the development of robust predictive models that integrate radiomic, clinical, pathological, and molecular data to improve individualized prediction of immunotherapy response. Accumulating evidence suggests that radiomics-based machine learning models can accurately predict treatment response, durable clinical benefit, progression-free survival, and overall survival across a broad spectrum of solid malignancies. Recent innovations, including delta-radiomics, radiogenomics, deep learning, multimodal artificial intelligence, and foundation models, are further enhancing the predictive capabilities of imaging biomarkers and facilitating more comprehensive patient stratification. Nevertheless, several barriers continue to impede clinical implementation, including the lack of standardized imaging protocols, limited external validation, concerns regarding reproducibility and generalizability, small and heterogeneous datasets, and evolving regulatory requirements. This review summarizes the biological basis of immunotherapy response prediction, outlines the methodological principles of radiomics and machine learning, critically evaluates current evidence across major solid tumors, and discusses emerging directions involving federated learning, foundation models, explainable artificial intelligence, and multi-omics integration. The convergence of radiomics, artificial intelligence, and precision oncology holds significant promise for optimizing patient selection, improving therapeutic decision-making, and advancing personalized immunotherapy in solid tumors. Kaleido Research Publications LLC 2026-04-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/35 10.65477/ijrems.v2.i4.02 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2 , Issue 4, April 2026; 07-18 3107-7439 eng https://ijrems.org/index.php/files/article/view/35/29 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/36 2026-06-30T08:17:59Z files:ART

Digital Twin Architectures for Personalized Cancer Imaging, Disease Progression Modeling, and Precision Oncology Dr. Ananya P. Deshmukh Mr. Kunal M. Shah Dr. Jayanthi Kanaka Ram Digital twins; Precision oncology; Medical imaging; Artificial intelligence; Machine learning; Radiomics; Disease progression modeling; Personalized medicine; Computational oncology; Treatment response prediction. Digital twin technology is emerging as a transformative paradigm in precision oncology by enabling the creation of dynamic, patient-specific virtual representations that continuously integrate multimodal imaging, molecular profiles, pathological findings, and longitudinal clinical data. Originally developed for the aerospace and manufacturing industries, digital twins have rapidly evolved into a promising healthcare technology capable of simulating disease progression, predicting treatment outcomes, and supporting individualized clinical decision-making. In oncology, digital twin frameworks combine advanced medical imaging, radiomics, artificial intelligence (AI), machine learning, computational modeling, and mechanistic biological simulations to generate comprehensive virtual models that accurately reflect tumor behavior and patient-specific disease evolution. Recent advances have expanded the application of digital twins across the cancer care continuum, including tumor detection and segmentation, disease progression modeling, treatment response prediction, radiotherapy planning, surgical guidance, immunotherapy optimization, and longitudinal monitoring of therapeutic outcomes. The integration of multimodal imaging with genomics, pathology, and electronic health records is further enhancing the accuracy and clinical utility of these predictive models, enabling increasingly personalized approaches to cancer management. Emerging technologies such as deep learning, foundation models, federated learning, and multi-omics integration are expected to further accelerate the development of clinically deployable digital twin ecosystems. Despite their considerable promise, several challenges continue to limit widespread clinical implementation, including limited data interoperability, lack of standardized data acquisition and validation protocols, concerns regarding model interpretability and reproducibility, computational complexity, regulatory uncertainty, and ethical issues related to privacy and data governance. Addressing these challenges will be essential for translating digital twin technologies from research environments into routine clinical practice. This review provides a comprehensive overview of digital twin architectures for personalized cancer imaging and disease progression modeling, summarizes current applications across major malignancies, discusses enabling technologies and methodological frameworks, critically evaluates existing limitations, and highlights future directions toward intelligent, continuously learning digital twin ecosystems for precision oncology. The convergence of digital twins, artificial intelligence, multimodal imaging, and computational oncology has the potential to fundamentally transform personalized cancer diagnosis, therapeutic planning, response assessment, and long-term disease management. Kaleido Research Publications LLC 2026-05-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/36 10.65477/jrems.v2.i5.03 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2, Issue 5, May 2026; 25-38 3107-7439 eng https://ijrems.org/index.php/files/article/view/36/30 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/37 2026-07-16T07:20:37Z files:ART

Generative Artificial Intelligence for Clinical Decision Support in Oncology: Current Applications, Challenges, and Future Directions Dr. Sanjay K. Deshpande Dr. Harish V. Reddy Dr. Asha N. Kapoor Generative artificial intelligence; Large language models; Clinical decision support; Oncology; Precision medicine; Transformer architecture; Multimodal artificial intelligence; Foundation models; Clinical natural language processing. Cancer remains one of the leading causes of morbidity and mortality worldwide, creating an urgent need for intelligent clinical decision-support systems capable of delivering personalized, evidence-based cancer care. The rapid expansion of clinical, imaging, molecular, genomic, and radiomics datasets has accelerated the development of generative artificial intelligence (GenAI), enabling advanced computational models to support complex clinical decision-making throughout the oncology care continuum. Powered by transformer-based architectures and large language models (LLMs), GenAI has emerged as a transformative technology with the potential to enhance diagnostic accuracy, optimize treatment selection, facilitate precision oncology, and accelerate translational cancer research. This review provides a comprehensive overview of recent advances in generative artificial intelligence for clinical decision support in oncology, with particular emphasis on large language models, multimodal foundation models, retrieval-augmented generation, and clinical natural language processing. We summarize the underlying computational architectures and examine their applications across cancer screening, diagnosis, radiology, pathology, treatment planning, precision oncology, clinical trial matching, prognostic modeling, patient communication, and multidisciplinary decision-making. Current evidence indicates that GenAI can improve workflow efficiency, automate clinical documentation, synthesize large volumes of biomedical information, support personalized therapeutic recommendations, and facilitate integration of multimodal clinical data into real-time decision-support systems. However, several important barriers remain before widespread clinical implementation can be achieved, including hallucination, limited generalizability across healthcare settings, algorithmic bias, insufficient explainability, privacy and cybersecurity concerns, regulatory uncertainty, and the need for rigorous prospective clinical validation. Future developments are expected to focus on multimodal artificial intelligence, human–AI collaboration, federated learning, foundation models, explainable AI, and continuously learning clinical decision-support systems capable of integrating imaging, pathology, genomics, electronic health records, and real-world patient data. The convergence of generative artificial intelligence and precision oncology has the potential to fundamentally transform clinical decision-making, improve treatment personalization, enhance healthcare efficiency, and ultimately improve outcomes for patients with cancer. Kaleido Research Publications LLC 2026-06-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/37 10.65477/jrems.v2.i6.01 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 2, Issue 6, June 2026; 01-13 3107-7439 eng https://ijrems.org/index.php/files/article/view/37/31 Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0

oai:ojs.ijrems.org:article/38 2026-07-10T08:20:17Z files:ART

oai:ojs.ijrems.org:article/39 2026-07-10T08:31:59Z files:ART

oai:ojs.ijrems.org:article/41 2026-07-16T07:20:49Z files:ART

oai:ojs.ijrems.org:article/43 2026-07-23T16:31:13Z files:ART

Digital Twins in Cancer Care: Building Virtual Patients for Personalized Medicine Dr. Ananya Sethi Dr. Vivek Anand Dr. Ruchi Sharma Mr. Kunal Arora Digital twins, Precision oncology, Artificial intelligence, Personalized medicine, Computational oncology, Machine learning, Multimodal learning, Digital pathology, Clinical decision support, Virtual patients. Cancer care is undergoing a major transformation through the convergence of artificial intelligence (AI), computational modeling, systems biology, and precision medicine. Among the most promising innovations is the development of digital twins—continuously evolving virtual representations of individual patients that integrate clinical, radiological, pathological, genomic, molecular, physiological, and longitudinal health information to simulate disease progression and therapeutic response. Unlike conventional predictive models that analyze isolated datasets, digital twins continuously incorporate newly acquired patient information, allowing personalized prediction, adaptive treatment optimization, toxicity assessment, and long-term survivorship planning. Recent advances in machine learning, deep learning, multimodal learning, transformer architectures, graph neural networks, foundation models, and generative AI have significantly enhanced the construction and clinical applicability of oncology digital twins. These intelligent computational systems are increasingly being investigated for tumor diagnosis, radiogenomics, computational pathology, immunotherapy prediction, adaptive radiation planning, surgical simulation, drug discovery, and clinical decision support. Furthermore, integration with wearable technologies, cloud computing, digital pathology, and electronic health records enables continuous updating of virtual patient models throughout the cancer journey. Despite remarkable progress, significant challenges remain regarding data standardization, interoperability, computational complexity, explainability, cybersecurity, ethical governance, regulatory validation, and clinical implementation. This review provides a comprehensive overview of digital twin technologies in cancer care, emphasizing their computational foundations, clinical applications, emerging innovations, and future role in advancing personalized medicine.[1] Kaleido Research Publications LLC 2025-12-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/43 10.65477/ijrems.v1.i7.02 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 7, December 2025; 7-15 3107-7439 eng https://ijrems.org/index.php/files/article/view/43/35 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by-nc/4.0/

oai:ojs.ijrems.org:article/44 2026-07-23T16:37:41Z files:ART

Clinical Digital Twins in Oncology: From Concept to Clinical Translation Dr. Meenakshi Rao Dr. Harish Bhat Dr. Sonia Kapoor Mrs. Neha Menon Dr. Akhil Nair Clinical digital twins, Precision oncology, Artificial intelligence, Personalized medicine, Computational oncology, Machine learning, Clinical decision support, Digital pathology, Radiogenomics, Cancer informatics. Clinical digital twins have emerged as one of the most promising innovations in precision oncology, offering a transformative approach to individualized cancer diagnosis, treatment planning, disease monitoring, and survivorship care. A clinical digital twin is a continuously evolving virtual representation of an individual patient that integrates multimodal biomedical information—including clinical records, radiological imaging, digital pathology, genomic sequencing, molecular profiling, laboratory biomarkers, physiological monitoring, and longitudinal health data—to simulate disease progression and therapeutic response. Unlike conventional predictive models that analyze isolated datasets, digital twins continuously update patient-specific computational models as new clinical information becomes available, enabling adaptive and personalized clinical decision-making. Recent advances in artificial intelligence (AI), machine learning, deep learning, multimodal learning, graph neural networks, transformer architectures, foundation models, and generative AI have significantly accelerated the development of clinically applicable oncology digital twins. These intelligent systems demonstrate considerable potential in cancer diagnosis, radiogenomics, computational pathology, immunotherapy prediction, adaptive radiation therapy, surgical planning, drug discovery, clinical trial optimization, and real-time clinical decision support. Furthermore, integration with cloud computing, wearable technologies, federated learning, and electronic health records enables continuously learning healthcare ecosystems capable of supporting precision medicine throughout the cancer care continuum. Despite substantial progress, important challenges remain regarding data interoperability, computational complexity, explainability, cybersecurity, regulatory approval, ethical governance, and large-scale clinical implementation. This review discusses the evolution of clinical digital twins in oncology, emphasizing their computational architecture, current clinical applications, translational opportunities, and future role in personalized cancer care.[1] Kaleido Research Publications LLC 2025-12-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/44 10.65477/ijrems.v1.i7.03 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 7, December 2025; 16-23 3107-7439 eng https://ijrems.org/index.php/files/article/view/44/36 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by-nc/4.0/

oai:ojs.ijrems.org:article/45 2026-07-23T16:43:54Z files:ART

Digital Twin Technology: The Next Frontier in Personalized Oncology Dr. Rajeev Malhotra Dr. Pooja Bansal Dr. Deepika Verma Digital twins, Artificial intelligence, Precision oncology, Personalized medicine, Computational oncology, Digital pathology, Radiogenomics, Clinical decision support, Machine learning, Systems biology. Cancer remains one of the most complex diseases confronting modern medicine because of its remarkable biological heterogeneity, dynamic evolution, and highly individualized therapeutic responses. Despite substantial advances in molecular diagnostics, targeted therapies, immunotherapy, and precision medicine, considerable variability in clinical outcomes continues to challenge conventional treatment strategies. Recent developments in artificial intelligence (AI), computational biology, systems medicine, mathematical modeling, and multimodal biomedical data integration have introduced digital twin technology as one of the most promising innovations in personalized oncology. A digital twin is a continuously evolving virtual representation of an individual patient that integrates radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, physiological monitoring, treatment history, and longitudinal clinical data to simulate disease progression and therapeutic response. Unlike traditional predictive models that analyze isolated datasets, digital twins provide dynamic, patient-specific computational ecosystems capable of supporting diagnosis, prognostic prediction, treatment optimization, toxicity assessment, adaptive therapy, and survivorship planning. Advances in machine learning, deep learning, transformer architectures, graph neural networks, reinforcement learning, generative AI, and foundation models have significantly accelerated the development of oncology digital twins by enabling continuous integration of heterogeneous biomedical information. These intelligent systems have demonstrated growing potential across tumor detection, radiogenomics, computational pathology, immunotherapy prediction, adaptive radiation therapy, surgical planning, drug discovery, and clinical decision support. Nevertheless, challenges remain regarding data harmonization, interoperability, computational complexity, cybersecurity, explainability, ethical governance, regulatory validation, and widespread clinical implementation. This review provides a comprehensive overview of digital twin technology in oncology, emphasizing its computational foundations, current clinical applications, emerging innovations, and future role in transforming personalized cancer care.[1] Kaleido Research Publications LLC 2025-12-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/45 10.65477/ijrems.v1.i7.04 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 7, December 2025; 24-31 3107-7439 eng https://ijrems.org/index.php/files/article/view/45/37 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by-nc/4.0/

oai:ojs.ijrems.org:article/47 2026-07-23T16:51:03Z files:ART

AI-Driven Digital Twins for Predictive Oncology and Precision Therapeutics Dr. Ashwin Prasad Dr. Lavanya Iyer Mrs. Shruti Nanda Dr. Karthik Raman Dr. Faisa Rahman Digital twins, Artificial intelligence, Predictive oncology, Precision therapeutics, Machine learning, Deep learning, Precision medicine, Computational oncology, Multimodal learning, Clinical decision support. Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, targeted therapies, immunotherapy, and precision medicine. The extraordinary biological heterogeneity of tumors, coupled with dynamic interactions among malignant cells, the immune system, and the tumor microenvironment, continues to challenge conventional treatment strategies. Recent advances in artificial intelligence (AI), computational biology, systems medicine, and multimodal biomedical data integration have introduced AI-driven digital twins as a transformative framework for predictive oncology and precision therapeutics. A digital twin is a continuously evolving virtual representation of an individual patient that integrates radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, physiological monitoring, electronic health records, and longitudinal clinical information to simulate disease progression and therapeutic response. Unlike traditional predictive models that analyze isolated datasets, AI-driven digital twins continuously learn from multimodal patient data, enabling dynamic prediction of disease evolution, treatment efficacy, toxicity risk, and resistance mechanisms. Advances in machine learning, deep learning, transformer architectures, graph neural networks, reinforcement learning, generative AI, and foundation models have substantially enhanced the development of intelligent digital twin ecosystems capable of supporting diagnosis, prognostic prediction, adaptive treatment planning, immunotherapy optimization, radiation therapy, surgical planning, and precision drug development. Although significant challenges remain regarding interoperability, data harmonization, explainability, cybersecurity, regulatory validation, and ethical governance, AI-powered digital twins are expected to become central components of future precision oncology. This review presents an overview of the computational foundations, clinical applications, technological innovations, and future directions of AI-driven digital twins in predictive oncology and personalized cancer therapeutics.[1] Kaleido Research Publications LLC 2025-12-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/47 10.65477/ijrems.v1.i7.05 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 7, December 2025; 32-39 3107-7439 eng https://ijrems.org/index.php/files/article/view/47/38 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by-nc/4.0/

oai:ojs.ijrems.org:article/48 2026-07-23T16:57:19Z files:ART

Digital Twins and Systems Oncology: Revolutionizing Cancer Management Dr. Nitin Kulkarni Dr. Bhavana Singh Mr. Arvind Joshi Dr. Snehal Patwardhan Dr. Manoj Kale Mrs. Asha Nair Digital twins, Systems oncology, Artificial intelligence, Precision medicine, Computational oncology, Machine learning, Multimodal learning, Digital pathology, Radiogenomics, Personalized cancer care. Cancer remains one of the most biologically complex diseases, characterized by remarkable genomic diversity, dynamic tumor evolution, immune heterogeneity, and continuous interactions between malignant cells and the surrounding microenvironment. Despite significant advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine, considerable variability in treatment response and clinical outcomes persists among patients with apparently similar tumor characteristics. Recent advances in artificial intelligence (AI), systems biology, computational oncology, mathematical modeling, and multimodal biomedical data integration have introduced digital twin technology as a transformative approach for personalized cancer management. A digital twin is a continuously evolving computational representation of an individual patient that integrates radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, physiological monitoring, electronic health records, and longitudinal clinical data to simulate disease progression and therapeutic response. Unlike conventional predictive models that analyze isolated datasets, digital twins continuously update through real-time patient information, enabling dynamic prediction of tumor evolution, treatment efficacy, toxicity, resistance mechanisms, and long-term clinical outcomes. Artificial intelligence technologies including machine learning, deep learning, transformer architectures, graph neural networks, reinforcement learning, multimodal learning, and foundation models have substantially accelerated digital twin development within oncology. These intelligent systems have demonstrated promising applications across cancer diagnosis, radiogenomics, computational pathology, immunotherapy, adaptive radiation therapy, surgical planning, drug discovery, clinical trial optimization, and precision therapeutics. Nevertheless, challenges remain regarding data standardization, interoperability, computational complexity, explainability, cybersecurity, ethical governance, regulatory validation, and widespread clinical implementation. This review explores the integration of digital twins and systems oncology, emphasizing computational foundations, clinical applications, emerging innovations, and future perspectives for revolutionizing personalized cancer management.[1] Kaleido Research Publications LLC 2025-12-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijrems.org/index.php/files/article/view/48 10.65477/ijrems.v1.i7.06 International Journal of Research in Engineering and Management Sciences; IJREMS: Vol 1 , Issue 7, December 2025; 40-47 3107-7439 eng https://ijrems.org/index.php/files/article/view/48/39 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by-nc/4.0/