đź”” Announcements
Bi-Monthly Journal (2 monthly)
đź“Š ISSN NO: 3108-1312
Subject: Multidisciplinary
đź—ž News & Updates
✍️ Editorial Message
Welcome to the International Journal of Convergent Technologies and Future Systems (IJCTFS). Our journal aims to provide a platform for innovative research in AI, Machine Learning, Cloud, Cybersecurity, IoT, and other emerging technologies. We encourage interdisciplinary collaboration and the sharing of ideas that can shape the future of technology. IJCTFS is committed to publishing high-quality research that bridges theory with practical impact. We thank our contributors, reviewers, and readers for supporting this journey of innovation and knowledge dissemination.
With best regards,
Dr. D. M. Marathe
Founder & Editor-in-Chief
International Journal of Convergent Technologies and Future Systems (IJCTFS)
MauliKavyanshu Foundation
International Journal of Convergent Technologies and Future Systems (IJCTFS)
Papers (March - April 2026)
đź“š Submitted Research Papers
1. Blockchain-Based Electronic Voting System Using Smart Contracts, Cryptographic Security, and Biometric Authentication: A Review
Author: Ganesh Patil, Tushar Bhoi, Vaibhav Girase, Mahesh Chandode (Guide- Assit. Prof. Kalpesh Marathe)
Email: gp4292829@gmail.com
Abstract: Modern democracies face growing threats to electoral integrity, including vote manipulation, identity fraud, and erosion of public trust in centralised voting authorities. In response, researchers have increasingly explored blockchain technology as a foundation for next-generation electronic voting systems. This paper presents a comprehensive review of blockchain-based e-voting architectures that integrate smart contract logic, layered cryptographic protection, and biometric voter authentication. Synthesising findings from over forty peer-reviewed studies published between 2018 and 2026, we analyse systems built on platforms such as Ethereum, Hyperledger Fabric, and cross-chain frameworks. Core technical areas examined include zero-knowledge proof construction for ballot privacy, elliptic curve and post-quantum cryptographic schemes for data integrity, threshold-based key distribution for decentralised authority, and multimodal biometric fusion for robust identity assurance. System performance is evaluated across metrics including transaction throughput, confirmation delay, computational cost, and scalability under load. The review also maps unresolved challenges spanning coercion resistance in remote voting, digital accessibility barriers, legal compliance requirements, and national-scale deployment feasibility, concluding with a proposed five-layer reference architecture to guide practical system development.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-05-24 12:12:49
2. Secure Runtime Software Protection Tool (SRSPT): A Block-Level AES-256 Runtime Decryption Framework for Anti-Piracy and Tamper-Resistant Software Execution
Author: Milind R. Garge, Ayush K. Malwatkar, Ayush S. Kapure, Guide Assist. Prof Kalpesh Marathe
Email: milindgarge07@gmail.com
Abstract: Proprietary software products are constantly threatened by memory-based binary analysis, software piracy, and unlawful reverse engineering. A basic flaw of both whole-binary encryption and static obfuscation is that the full decrypted program is eventually put into process memory, creating a predictable and exploitable attack surface. In order to overcome this restriction, this study introduces the Secure Runtime Software Protection Tool (SRSPT), a conceptual cybersecurity protection framework that suggests a runtime block decryption architecture. A built binary is theoretically divided into discrete, individually encrypted logical chunks using AES-256-CBC [1] and RSA-2048 asymmetric key wrapping [2]. Each block has a unique key. Each block's integrity is checked using SHA-256 hashing prior to execution [3]. Only the block that is now running is decrypted during runtime into a controlled memory space, which is then safely zeroed before the subsequent block is loaded. By ensuring that the entire plaintext binary is never concurrently present in RAM, this sequential single-block-in-memory discipline significantly reduces the window of opportunity for memory-scraping and dumping assaults. On an x86-64 architecture with AES-NI acceleration, an analytically predicted runtime overhead of 4–7% is presented. Conceptually, the design is contrasted with the function-level runtime encryption method that served as its inspiration [7]. Inspired by well-established runtime protection, trusted execution, and ephemeral decryption principles, this work is an implementation-oriented conceptual research study [4][5][6][7]. AES-256 block decryption, RSA-2048 key wrapping, SHA-256 integrity verification, anti-piracy, prevention of reverse engineering, secure loader, memory cleanup, software IP protection, ephemeral decryption, block-level encryption, binary protection, threat model, and conceptual framework are among the index terms.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-05-26 11:22:27
3. Cloud Computing Based on Big Data Technology Architecture, Opportunities and Challenges
Author: Priyanka Borse, Akshata Patil, Komal Patil
Email: priyankaborse160@gmail.com
Abstract: The convergence of Cloud Computing and Big Data technology has revolutionized the modern digital ecosystem. While Big Data introduces unprecedented challenge regarding data volume, velocity, variety, and veracity, Cloud Computing provides the necessary elastic infrastructure, scalable storage, and high-performance parallel computing capabilities to process this information. This paper examines the symbiotic relationship between cloud computing and big data systems. We analyze core architectural frameworks (such as Apache Hadoop, Spark, and HDFS deployed on virtualized cloud nodes), evaluate data staging and storage methodologies across structured and unstructured paradigms, and discuss real-world industrial efficiencies. Finally, we highlight critical bottlenecks—specifically security, multi-tenant privacy, data governance, and real-time stream processing—and map out future research directions in context-aware processing and decentralized cryptographic protocol.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-05-26 11:24:47
4. Trapdoor Privacy In Public Key Encryption With Keyword Search: A Review
Author: Yogita Patil , Himani Girase, Divya Girase (Guide: Kalpesh Marathe Sir)
Email: divyapgirase@gmail.com
Abstract: Public Key Encryption with Keyword Search (PEKS) is a basic cryptographic tool that allows a semi-honest server to look for information in encrypted data on behalf of a data owner, without learning the actual content of the data. This technique was first introduced by Boneh et al. in 2004 and has become a key technology for ensuring privacy in cloud storage and systems that outsource data management. One important but often overlooked security aspect of PEKS is trapdoor privacy, which ensures that a keyword trapdoor, which a designated user gives to the server, does not reveal more about the keyword than what can be known from the binary search result. Even though PEKS is widely used, trapdoor privacy has not been studied as thoroughly as other security features such as ciphertext indistinguishability and keyword secrecy. In this paper, we offer a detailed and formal analysis of trapdoor privacy in PEKS systems. We review and improve existing definitions of trapdoor indistinguishability, creating a formal security model that covers both selective and adaptive attack scenarios. We also uncover weaknesses in several commonly used PEKS designs that do not provide sufficient trapdoor privacy, making it possible for a server that is honest but curious to gain information about the keywords through trapdoor analysis. We explore how trapdoor privacy connects with other standard security features in PEKS, such as ciphertext indistinguishability against chosen keyword attacks (IND-CKA) and consistency. We show that trapdoor privacy and ciphertext indistinguishability are separate security features—neither one necessarily leads to the other—and that achieving both requires additional assumptions about the cryptographic groups or hash functions used.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-05-29 08:40:09
5. The Impact of Generative Artificial Intelligence on Higher Education Learning and Teaching
Author: Dhanashri S. Bhavsar, Dhanshri A. Girase Sapana S. Chavhan (Guide: Kalpesh Marathe)
Email: girasedhanshri1@gmail.com
Abstract: The rapid proliferation of generative Artificial Intelligence (GAI) tools, most notably ChatGPT, has introduced profound and far-reaching consequences for Higher Education (HE) institutions worldwide. This qualitative and quantitative study investigates the perspectives of university educators regarding the impact of generative AI on learning and teaching practices. A mixed-methods design was employed, incorporating structured online surveys and semi-structured interviews with academic staff drawn from multiple faculties at a research-intensive Australian university. Thematic analysis of the collected data revealed three principal themes: the overwhelming dominance of ChatGPT as the generative AI tool of choice among educators; an urgent and largely unmet demand for institutional training and support; and the critical importance of transparent student engagement with AI technologies. Key findings indicate that while nearly half of participants had adopted AI within their teaching roles—predominantly through assessment redesign—fewer than a quarter felt adequately equipped by their institutions. Concerns surrounding academic integrity, though widespread, appeared to be partially overstated relative to actual student usage patterns. Participants unanimously anticipated continued advancement in AI capabilities, while expressing significant uncertainty regarding its long-term pedagogical implications. The study concludes with a call for sustained institutional commitment to AI literacy, policy development, and collaborative research, emphasizing universities' core obligation to prepare graduates for an AI-integrated professional landscape.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-05-29 08:42:45
6. Impact of Social Media on Students
Author: Kailas S. Khairnar, Swamiraj S. Gosavi, Dnyaneshwar K. Shelar (Guide: Kalpesh Marathe Sir)
Email: demo@gmail.com
Abstract: Social media has emerged as one of the most transformative technologies of the 21st century, fundamentally altering the way individuals — especially students — communicate, learn, and engage with the world. Platforms such as Facebook, Instagram, YouTube, WhatsApp, Snapchat, Twitter (now X), and TikTok are used daily by hundreds of millions of students worldwide. In India alone, students aged 15 to 24 represent the fastest-growing social media demographic, with average daily usage exceeding three to five hours. This research paper undertakes a comprehensive examination of the multidimensional impact of social media on students, encompassing academic performance, mental health, communication behaviors, sleep patterns, and social relationships. The study synthesizes evidence from international and Indian research, government reports, and psychological studies to present a balanced and nuanced understanding of both the opportunities and risks that social media presents to the student community. The findings reveal that while social media offers meaningful benefits — including improved access to educational resources, enhanced peer collaboration, creative expression, and global awareness — it simultaneously poses serious threats such as academic distraction, sleep deprivation, cyberbullying, anxiety, depression, and exposure to misinformation. The paper concludes with evidence-based recommendations for students, parents, educators, and policymakers aimed at promoting healthy, productive engagement with social media.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-05-29 08:46:48
7. Econometric Yield and Strategic Efficacy: A Descriptive Review of Influencer Marketing and Programmatic Digital Advertising
Author: Gaurav S. Girase, Virendra S. Mali, Nandraj M. Bagal, Prem S. Borse (Guide: Mrs. Deepika Wadile)
Email: demo1@gmail.com
Abstract: Digital customer acquisition is increasingly shaped by the complex, multi-touchpoint nature of modern consumer journeys, making accurate attribution a persistent challenge for marketers. Traditional single-channel metrics often fail to capture the behavioral dynamics that emerge when early-funnel creator engagement combines with late-funnel programmatic conversion. This paper adopts a secondary, descriptive research approach to assess how influencer marketing and automated programmatic advertising can be integrated into a cohesive operational framework. Drawing on industry benchmarks, established cognitive theories — including Source Credibility Theory and the Persuasion Knowledge Model — and enterprise implementation data, the study develops a practical hybrid marketing model. Key areas examined include the mechanics of creator allowlisting, the economic consequences of ad fraud, and principles for optimizing budget allocation across channels. The findings offer actionable guidance for brand managers and marketing agencies seeking to balance programmatic scale with the authenticity of creator-led content.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-05-30 07:40:34
8. AI Chatbot Support for Students
Author: Sanjana Gosavi, Monika Bhoi, Vaishnavi Patil (Guide: Assit. Prof. Kalpesh Marathe)
Email: demo2@gmail.com
Abstract: Artificial Intelligence (AI) is orchestrating an unprecedented paradigm shift across contemporary global educational models, moving foundational instruction away from static frameworks into fully unified, algorithmic architectures. This comprehensive review paper provides a meticulous analysis of the deployment methodologies, architectural layers, operational impacts, and socio-technical hurdles of intelligent educational conversational agents (chatbots). By operating at the intersection of complex Natural Language Processing pipelines, Machine Learning algorithms, deep generative neural networks, and scalable cloud ecosystems, these support tools provide students with instant, 24/7 technical and instructional triage. This paper evaluates how these automated systems reduce administrative workloads, balance academic resource constraints, and increase baseline user engagement. Additionally, we dissect operational workfiows, underlying tech stacks, and inherent challenges—such as factual hallucinations, algorithmic data biases, and emotional disconnects—to outline future development trajectories for autonomous pedagogical virtual instruction platforms.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-05-30 07:43:27
9. Wireless Sensor Networks: A Comprehensive Review of Architectures, Protocols, and Emerging SOTA Technologies
Author: Mr. .Rohit Patil, Mr. Mayur Sonawane & Assit. Prof. Vipul Girase Sir
Email: demo3@gmail.com
Abstract: Wireless Sensor Networks (WSNs) represent a foundational milestone in edge intelligence, environmental telemetry, and the broader Internet of Things (IoT) landscape. Consisting of spatially distributed, autonomous, resource-constrained devices equipped with sensory instruments, WSNs collect, process, and forward environmental metrics to centralized base stations. This comprehensive review paper dissects the architectural paradigms, critical design requirements, and operational boundaries of contemporary WSN structures. We outline the core optimization challenges—primarily battery-longevity, fault-tolerant routing, and scalability constraints. A meticulous literature review charts the transition from historical single-path heuristics to recent 2026 state-of-the-art (SOTA) AI-driven and multi-hop topologies. Furthermore, we construct an analytical methodology evaluating network lifespans, packet delivery rates, and latency characteristics under high-density scenarios. Our evaluation proves that integrated cross-layer routing models achieve superior resilience and power equilibrium. Finally, we highlight unresolved vulnerabilities and define actionable future pathways for edge intelligence integration.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-06-01 16:38:07
10. GREEN COMPUTING A Comprehensive Review of Sustainable Computing Technologies, Energy-Efficient Data Centers and Environmental Impact Reduction Strategies
Author: Ms.Rohini Badgujar, Punam Patil, Puja Nikam (Guide: Kalpesh Marathe Sir)
Email: nikampooja1119@gmail.com
Abstract: The rapid advancement of information and communication technologies has revolutionized modern society by improving communication, automation, data processing, and digital services. However, the continuous growth of computing infrastructures has also resulted in increased energy consumption, rising carbon emissions, and a significant accumulation of electronic waste. Data centers, cloud computing platforms, networking equipment, and computing devices collectively contribute to environmental challenges that require immediate attention from researchers, industries, and policymakers. Green computing has emerged as a sustainable approach that aims to reduce the ecological footprint of information technology systems while maintaining operational efficiency and technological innovation. The concept promotes environmentally responsible practices throughout the lifecycle of computing resources, including design, manufacturing, operation, maintenance, and disposal. Modern green computing techniques involve energy-efficient hardware, virtualization, cloud-based resource optimization, intelligent power management systems, renewable energy integration, and environmentally safe recycling mechanisms. This review paper presents a comprehensive analysis of green computing principles, technologies, applications, benefits, challenges, and future opportunities. The study investigates various research contributions related to sustainable computing, energy-aware data centers, cloud computing environments, virtualization technologies, eco-friendly hardware design, and electronic waste management. Furthermore, a comparative analysis of existing research studies is conducted to identify strengths, limitations, and research gaps within the field. The paper also discusses emerging technologies such as artificial intelligence-driven energy optimization, green Internet of Things (IoT), smart energy monitoring systems, and carbon-neutral computing infrastructures. The findings indicate that green computing is not merely an environmental initiative but a strategic necessity for achieving sustainable digital transformation. The adoption of green technologies can significantly reduce operational costs, improve resource utilization, and support long-term environmental sustainability.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-06-02 12:00:31
11. Data Science Techniques for Predictive Analytics: A Comprehensive Review
Author: Mr. Jayesh K. Dixit & Mr. Tushar H. Ahire (Guide: Dr S. N. Vende
Email: tusharahire459@gmail.com
Abstract: The exponential growth of data in modern organizations has elevated predictive analytics to a critical function across industries including healthcare, finance, retail, and manufacturing. This paper presents a comprehensive review of data science techniques employed in predictive analytics, with focused emphasis on machine learning (ML) algorithms and deep learning (DL) architectures. We systematically survey foundational ML models — including linear and logistic regression, support vector machines, decision trees, random forests, and gradient boosting methods — alongside advanced DL architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformer-based models. The review analyses each technique with respect to predictive performance, scalability, interpretability, and domain applicability. A comparative literature analysis of 18 key studies is presented, identifying research gaps and emerging directions. Findings indicate that ensemble ML methods and hybrid DL architectures consistently achieve superior performance, while transformer-based models represent the frontier for sequential and textual prediction tasks. The paper concludes with a discussion of open challenges including data quality, class imbalance, model explainability, and real-time inference.
🔗 Download Paper🎓 Download CertificateSubmitted on: 2026-06-03 11:21:09
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