Volume-13 , Issue-4 , Apr -2025, E-ISSN: 2347-2693

Breast cancer remains a leading global health challenge, demanding early, accurate, and interpretable diagnostic tools. This study presents a comprehensive evaluation of five pretrained convolutional neural networks—DenseNet121, InceptionV3, VGG19, EfficientNetB4, and MobileNetV3—for classifying breast ultrasound images from the BUSI dataset into Normal, Benign, and Malignant categories. The proposed framework integrates transfer learning, advanced preprocessing techniques, and class-weighted optimization to enhance model generalization and address data imbalance. Unlike prior studies, this work introduces a multi-model statistical comparison using Paired T-test, Wilcoxon Signed-Rank, and Cohen’s d, along with real-time inference benchmarking and a deployment-ready performance dashboard. Among the evaluated models, DenseNet121 demonstrated superior performance with an accuracy of 89.92% and an AUC-ROC of 0.95, outperforming existing state-of-the-art methods on the BUSI dataset. InceptionV3 also achieved strong results with 87.84% accuracy and notable inference speed. The findings confirm the clinical viability of integrating statistical rigor, inference-time awareness, and visual interpretability into deep learning pipelines for breast cancer detection. This framework lays the groundwork for scalable, explainable, and deployment-focused diagnostic AI systems in medical imaging.

Breast Cancer, Deep Learning, Ultrasound Imaging, Transfer Learning, DenseNet121, InceptionV3.

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Ahmed Wagdy, "Unveiling Model Superiority: A Comprehensive Analysis of Deep Learning Architectures for Robust Breast Cancer Prediction and Generalization," International Journal of Computer Sciences and Engineering, Vol.13, Issue.4, pp.1-14, 2025.

Soil classification is a crucial step in agricultural and environmental planning. Current innovations in computer vision and deep learning have enabled automatic soil classification using image-based approaches. This paper, explore comparative analysis of two popular convolutional neural network architectures, VGG16 and VGG19, for soil image classification. A use of soil image dataset containing various soil types used to evaluate the performance of both models. These models fine-tuned using transfer learning, and performance determined using metrics such as accuracy, precision, recall, F1-score, and training time. The result shows that both VGG16 and VGG19 achieve high classification accuracy, with VGG19 slightly outperforming than VGG16 in terms of accuracy but requiring more computational resources and time. This paper demonstrates the effectiveness of deep learning models in soil image classification and provides understandings into their comparative performance.

Agriculture, Soil, Image Classification, VGG16, VGG19

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Girish D. Chate, S.S. Bhamare, "Comparative Analysis of Deep Learning Techniques for Soil Image Classification," International Journal of Computer Sciences and Engineering, Vol.13, Issue.4, pp.15-22, 2025.

With the rapid digitization of finance, Central Bank Digital Currencies (CBDCs) and Bitcoin represent two distinct approaches to digital money. CBDCs are state-backed and regulated, whereas Bitcoin is decentralized and independent of government control. Understanding their interaction is crucial for policymakers, economists, and investors. This paper conducts a comprehensive SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of both CBDCs and Bitcoin to evaluate their impact on the global financial landscape. The study explores the fundamental differences between these digital currencies, covering aspects such as monetary control, technological frameworks, regulatory challenges, and economic implications. The paper begins with an overview of related work, detailing various forms of money and existing comparative studies between CBDCs and Bitcoin. It then examines the structure, benefits, risks, and development of CBDCs, followed by an in-depth discussion on Bitcoin, including blockchain architecture, cryptographic protocols, and consensus mechanisms. Through a systematic SWOT analysis, the strengths of CBDCs—such as financial inclusion and transaction efficiency—are contrasted with their weaknesses, including privacy concerns and implementation costs. Similarly, Bitcoin’s decentralized nature and transparency are weighed against its volatility and regulatory uncertainty. Findings from this study highlight the need for balanced regulatory frameworks and technological innovations to maximize the benefits of both CBDCs and Bitcoin while mitigating associated risks. These insights contribute to ongoing discussions on the role of digital currencies in shaping the future of finance. The paper concludes with future research directions, emphasizing the importance of interoperability, scalability, and evolving security measures in the digital currency ecosystem. This research article will contribute to the ongoing debate on digital currencies, providing a balanced perspective on whether CBDCs and Bitcoin are rivals or complementary elements in the evolving financial landscape.

Bitcoin, Blockchain, Central Bank Digital Currency, Cryptocurrency, Decentralized Finance, Digital Currencies, Fiat-backed Digital Currency, Financial Inclusion

[1] M. Laboure, M. H.-P. Müller, G. Heinz, S. Singh and S. Köhling, “Cryptocurrencies and CBDC: The Route Ahead,” Global Policy, Vol.12, pp.663-676, 2021.
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[8] D. W. Walumbe and J. G. Ndia, “A Systematic Literature Review of Proof of Work and Proof of Activity: Privacy and Performance,” International Journal of Computer Sciences and Engineering, Vol.11, No.10, pp.37-44, 2023.
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Brahmaleen K. Sidhu, "Central Bank Digital Currencies vs. Bitcoin: Competing or Complementary Digital Currencies?," International Journal of Computer Sciences and Engineering, Vol.13, Issue.4, pp.23-33, 2025.

AI Diffusion is an Android-supported text-to-image synthesis application fusing cutting-edge generative artificial intelligence and the pervasiveness of Android platforms. The vision is to provide users with a simple yet incredibly powerful tool with which they can produce realistic and imaginative images from text-based descriptions. Application architecture features an Android frontend developed with Kotlin and Python-based backend authored in FastAPI. Upon entering a prompt by a user, the backend interacts with a pre-trained generative AI model hosted via API to create an associated image, which is then rendered inside the app. The system is optimized to be lean and responsive to support real-time interaction even on resource-constrained devices. Comprehensive testing shows that the app works seamlessly with minimal latency and creates contextually accurate images for all types of prompts. The project bridged the distance between powerful AI functionality and genuine mobile usability, bringing more individuals access to creative tools and enabling them to access them with more ease. The project also enables future upgrade prospects, such as customization options and offline model suitability, to add more feasibility to mobile AI solutions.

Text-to-Image Generation, Generative AI, Android Application, FastAPI, Latent Diffusion Models, Mobile AI, AI-Powered Creativity, Prompt-Based Generation

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Anant Agrawal, Vedant Vardhan Rathour, Babeetha S., "AI Diffusion: An Android Application for Text-to-Image Generation Using Generative AI Models," International Journal of Computer Sciences and Engineering, Vol.13, Issue.4, pp.34-40, 2025.

In the evolving landscape of home automation, security remains a top priority. This paper presents "Bell Buddy," a dual-mode smart doorbell system designed to enhance residential safety through real-time image processing and IoT integration. The system operates in two distinct modes: Doorbell Mode and Intruder Detection Mode. In Doorbell Mode, when the bell is pressed, a notification is instantly sent to the user`s mobile device, along with a captured image of the visitor for manual verification and remote access control. In Intruder Detection Mode, the system actively monitors for motion near the entrance and uses facial recognition algorithms to determine whether the detected individual is authorized or not. If an unknown face is identified, alerts are dispatched to both the user and predefined emergency contacts, while an audible alarm is triggered. The proposed system combines ESP32, Raspberry Pi, and cloud-based services for seamless real-time communication. The user interface is developed using React Native, while machine learning models trained using TensorFlow ensure accurate intruder detection. With end-to-end encryption and database integration, Bell Buddy offers an intelligent, efficient, and scalable solution for modern home security. The system has been evaluated on parameters such as recognition accuracy, notification speed, and reliability under varying environmental conditions.

Smart Doorbell, IoT Security, Intruder Detection, Facial Recognition, Mobile Notification, ESP32, Raspberry Pi, Real-Time Image Processing, Home Automation, Deep Learning

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Sivakami T.S., Jasmin Maria Binoy, Alphia Jose, Hredya Vijay, "Bell Buddy: A Dual-Mode IoT-Based Smart Doorbell with Real-Time Facial Recognition and Intruder Alert System," International Journal of Computer Sciences and Engineering, Vol.13, Issue.4, pp.41-46, 2025.

Cryptography, the practice of concealing messages for private exchange, has evolved significantly to address the challenges posed by new communication methods. In today`s digital world, cryptography has become the cornerstone of cybersecurity, using advanced mathematics like number theory and computational complexity to protect digital information through algorithms. The upcoming arrival of quantum computers poses a serious threat to traditional cryptographic methods, especially those based on asymmetric key cryptography. Quantum Safe Cryptography (QSC) represents the next step in information security, designed to develop cryptographic systems that can withstand attacks from both quantum and classical computers. As quantum computing moves from theory to reality, it`s becoming clear that current cryptographic methods based on integer factorization and discrete logarithm problems are vulnerable to quantum algorithms like Shor`s algorithm. This study highlights the security risks that quantum computing poses to existing encryption methods and proposes a comprehensive approach to quantum-safe cryptography. The transition to quantum-resistant algorithms involves replacing vulnerable cryptographic systems with alternatives that can resist quantum computational attacks. Current analysis shows that while asymmetric key cryptography faces immediate vulnerability, symmetric key algorithms and hash functions remain relatively secure in the near term with appropriate adjustments. Quantum cryptography, which directly uses quantum mechanical principles, offers highly secure encryption mechanisms, notably Quantum Key Distribution (QKD). This research aims to enhance the security of digital infrastructure against quantum threats by evaluating hybrid cryptographic implementations that combine classical and quantum-resistant approaches for optimal security.

Hybrid Cryptosystems, Advanced Encryption Standard, Symmetric Key Encryption, Asymmetric Key Encryption, Quantum Safe Cryptography, Quantum Key Distribution, Post-Quantum Cryptography, Lattice-Based Cryptography

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[83] S. Singh and G. S. Bhathal, "Improving Security and Data Protection of Serverless Computing in the Cloud Environment", International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.19-27, 2024.
[84] J. Singh and G. S. Bhathal, "Securing Multi-Cloud Environment: An Automated Data Deletion System with Integrated Intrusion Detection System Over Multi-Cloud Platforms", International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.33-40, 2024.

Gurjit Singh Bhathal, Udaibir Singh Bhathal, "Quantum Safe Cryptography using Modern Hybrid Cryptography Techniques to Secure Data," International Journal of Computer Sciences and Engineering, Vol.13, Issue.4, pp.47-58, 2025.

Diabetic retinopathy (DR) is an extreme complication of diabetes and a main reason of vision impairment worldwide. Early detection is essential for powerful treatment and prevention of blindness. In this paper, we gift a deep learning approach for the automatic detection of DR the use of ResNet, a convolutional neural network (CNN) architecture known for its intensity and excessive performance in photo reputation obligations. Our examine utilizes a big dataset of retinal fundus pictures, which undergo preprocessing and augmentation to beautify the version’s robustness. The ResNet model is exceptional-tuned to classify specific stages of DR with an excessive diploma of accuracy. The consequences exhibit that our version achieves a class accuracy of 94.3%, appreciably enhancing detection competencies as compared to standard techniques. This paper explores using switch getting to know and optimization techniques to cope with demanding situations along with overfitting and dataset imbalance, in the end offering a green, scalable solution for computerized diabetic retinopathy screening.

Diabetic Retinopathy, Deep Learning, ResNet-50, Retinal Fundus Images, Classification, Convolutional Neural Network, Medical Imaging, Early Diagnosis, Feature Extraction, Automated Detection.

[1] Neha Tamboli, G.S. Malande, “Proliferative Diabetic Retinopathy Detection Using Machine Learning”, International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.106-111, 2020.
[2] Saurabh. S. Athalye, Gaurav Vijay, “Survey of Automatic Detection of Diabetic Retinopathy using digital image processing”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.352-355, 2019.
[3] K.K. Faisal , C.M. Deepa, S.M. Nisha, G. Gopi, “Study on Diabetic Retinopathy Detection Techniques”, International Journal of Computer Sciences and Engineering, Vol.4 , Issue.11, pp.137-140, 2016.
[4] Rajesh I.S., Bharathi Malakreddy A., Maithri C., Manjunath Sargur Krishnamurthy, Shashidhara M.S, “Automatic Detection of Optic Disc and Its Center in Color Retinal Images: A Review”, International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.81-85, 2024.
[5] Kaveri Devi, Arshdeep Kaur, “MSVM Based Technique Used To Detect Diabetic Retinopathy at Early Stage” , International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.34-40, 2021.
[6] Ruthran Baskar, Emmanuel Sabu, Claudia Mazo, “Deep CNNs for Diabetic Retinopathy Classification: A Transfer Learning Perspective”, 2024 IEEE International Symposium on Biomedical Imaging, 2024.
[7] Aryan Kokane,Gourhari Sharma, Akash Raina, Shubham Narole, Prof. Pramila M. Chawan. “Detection of Diabetic Retinopathy using Machine Learning”, International Research Journal of Engineering and Technology, Vol.7, Issue.11, pp.663-637, 2020.
[8] R. Gargeya and T. Leng, “Automated Identi fi cation of Diabetic Retinopathy Using Deep Learning,” Ophthalmology, pp.1–8, 2017.
[9] M. D. Abr„amoff, M. K. Garvin, and M. Sonka, “Retinal imaging and picture analysis,” IEEE Reviews in Medicine Engineering, Vol.3, pp.169–208, 2010
[10] V. Ramya, “SVM Based Detection for Diabetic Retinopathy,” Iccad, Vol.5, No.1, pp.11–13, 2018.
[11] MN. Abrmoff, M. Niemeijer, MS. Suttorp-Schulten, MA. Viergever, SR. Russell, B. Van Ginneken , “Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes”, Diabetes Care; Vol.31, Issue.2, pp.193–205, 2008.
[12] J. Nayak, PS. Bhat, R. Acharya, CM. Lim, M. Kagathi, “Automated identification of different stages of diabetic retinopathy using digital fundus images”, J Med Syst; Vol.32, Issue.2, pp.107–115, 2008.

Tansu Gangopadhyay, Saptaparno Patra, Deepajothi S., "Enhancing Diabetic Retinopathy Detection Using Optimized Deep Learning Techniques with ResNet," International Journal of Computer Sciences and Engineering, Vol.13, Issue.4, pp.59-67, 2025.

K-Means is a popular algorithm used in unsupervised machine learning for clustering tasks, especially when working with data that lacks predefined labels. It is widely employed to divide such data into meaningful groups. This research presents an improved version of the traditional K-Means algorithm, adapting it for use on labeled datasets to evaluate its effectiveness in segmentation. The study compares this modified version with the standard K-Means method, focusing on aspects like accuracy, efficiency, and computational demand. A dataset containing more than 3,000 records is used for experimentation. The standard approach starts with K=2, randomly selecting initial centroids and refining them through iterations until results stabilize. This is repeated up to K=9. In the revised method, however, a top-down approach is implemented. Instead of selecting centroids randomly, the algorithm uses a density-based technique to place initial centroids in densely populated data regions. Clusters are formed based on these regions and refined iteratively. After each convergence, the process continues by further dividing the clusters, up to K=9. Results from the study reveal that the new approach improves performance by speeding up convergence—reducing iterations by over 20%—and lowering computational costs, while also boosting overall clustering accuracy and efficiency.

Clustering, K-Means Algorithm, Density-Based Centroid Selection, Top-Down Approach, Segmentation Efficiency

[1] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient k-means clustering algorithm: analysis and implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, pp.881–892, 2002. DOI:10.1109/TPAMI.2002.1017616
[2] K. Wagstaff, C. Cardie, S. Rogers, and S. Schrödl, “Constrained k-means clustering with background knowledge,” Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA, USA, vol. 1, pp. 577–584, 2001. DOI:10.5555/645530.655646
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[5] S. Singh and N. S. Gill, “Analysis and study of k-means clustering algorithm,” International Journal of Engineering Research & Technology (IJERT)*, vol. 2, issue 7, pp. 2546–2551, 2013.
[6] F. Yuan, Z. H. Meng, H. X. Zhang, and C. R. Dong, “A new algorithm to get the initial centroids,” Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, pp. 26–29, 2004.
[7] K. Arai and A. R. Barakbah, “Hierarchical K-means: An algorithm for centroids initialization for k-means,” International Journal of Computer Science, vol. 36, no. 1, pp. 1–6, 2007.
[8] P. Gupta and R. Sharma, “Adaptive K-means clustering for dynamic data,” Journal of Machine Learning Research, vol.18, no. 1, pp. 230–242, 2017.
[9] J. Kim, H. Park, and S. Lee, “A spatially aware K-means algorithm for image segmentation,” International Journal of Computer Vision and Image Processing, vol. 9, no. 4, pp. 145–158, 2018.
[10] A. Kumar and V. Singh, “Parallelized K-means for large-scale data clustering,” Journal of Computational Science, vol. 23, no. 2, pp.305–318, 2017.
[11] Y. Lee, S. Park, and J. Choi, “Hybrid K-means clustering with decision trees for automated cluster determination,” Pattern Recognition Letters, vol. 45, no. 6, pp. 74–86, 2019.
[12] L. Zhang, Y. Xu, and Z. Li, “A hybrid K-means algorithm based on density clustering,” Journal of Computational Mathematics, vol. 34, no. 8, pp. 1013–1025, 2016.
[13] Z. Zhao, S. Yang, and X. Liu, “Outlier-resistant K-means clustering based on data preprocessing,” Computational Statistics & Data Analysis, vol. 100, no. 3, pp. 65–80, 2016.
[14] M. H. Ali and A. Zubair, “K-means clustering for high-dimensional data: A performance evaluation,” International Journal of Computer Science and Information Security, vol. 16, no. 2, pp. 112–121, 2018.
[15] A. Banerjee and S. Ghosh, “Enhanced K-means clustering algorithm for anomaly detection in big data,” Journal of Big Data, vol. 5, no. 1, pp. 35–47, 2017.
[16] L. Yang and R. Zhang, “A comparative study of K-means clustering algorithms in data mining,” International Journal of Data Mining and Knowledge Discovery, vol. 27, no.4, pp.456–467, 2019.
[17] J. Wang and C. Li, “Improving K-means clustering through density-based initialization,” Journal of Applied Mathematical Modeling, vol. 40, no. 9, pp.5631–5638, 2016.
[18] G. Kaur and V. Kumar, “An improved K-means clustering approach for bioinformatics data analysis,” Journal of Bioinformatics and Computational Biology, vol. 15, no.1, pp.21–32, 2017.
centroid selection for large-scale datasets,” Data Science and Engineering, vol. 4, no. 3, pp.112–121, 2019.
[20] R. Smith, K. Johnson, and M. Patel, “A study on K-means clustering for high-dimensional datasets,” Journal of Data Science and Machine Learning, vol. 15, no. 3, pp. 45–60, 2018.
[21] Zhou, H., & Yang, S., Density-based k-means clustering for imbalanced datasets. International Journal of Computational Intelligence, 9(2), pp.122-130, 2017.
[22] Garcia, A., Lopez, J., & Singh, A., Hybrid approach of k-means clustering and machine learning models for improved classification accuracy. Journal of Computational Methods in Applied Sciences, 21(4), pp.275-289, 2019.
[23] Wang, Q., & Li, J., Adaptive clustering for dynamic selection of k in k-means algorithm. Journal of Computational Intelligence and Data Mining, Vol.25, Issue.6, pp.90-102, 2017.
[24] Patel, A., Kumar, V., & Singh, P., Influence of initialization methods on k-means clustering performance. International Journal of Data Analysis and Applications, Vol.14, Issue.1, pp.55-72, 2018.
[25] Tan, Y., & Zhang, Z., GA-K-means: A genetic algorithm-based optimization of k-means clustering. Journal of Evolutionary Computing and Data Science, Vol.11, Issue.3, pp.129-145, 2019.
[26] D. Saidulu, V. Devasekhar, and V. Swathi, "Secured MapReduce Based K-Means Clustering in Big Data Framework," International Journal of Computer Sciences and Engineering, Vol.7, No.5, pp.1427–1430, 2019.
[27] K. Gandhimathi and N. Umadevi, "Prediction of Type 2 Diabetics Based on Clustering Algorithm," International Journal of Computer Sciences and Engineering, Vol.8, No.11, pp.72–78, 2020.
[28] K. Sarkar and R. K. Mudi, "Fuzzy Clustering Exploiting Neighbourhood Information for Non-image Data," International Journal of Computer Sciences and Engineering, vol.12, No.2, pp.1–8, 2024.
[29] M. Kasthuri, S. Kanchana, and R. Hemalatha, "Comparative Study on Various Clustering Techniques in Data Mining," International Journal of Computer Sciences and Engineering, Vol.6, No.11, pp.1–8, 2018.
[30] B. Bhawna and A. Asha, "Study of Clustering Algorithm for Student Analysis," International Journal of Computer Sciences and Engineering, Vol.7, No.6, pp.937–940, 2019.
[31] A. Jawale and G. Magar, "Survey of Clustering Methods for Large Scale Dataset," International Journal of Computer Sciences and Engineering, Vol.7, No.5, pp.1338–1344, 2019.
[32] B. Janghel and A. Ambhaikar, "Study of Clustering Algorithm for Student Analysis," International Journal of Computer Sciences and Engineering, Vol.7, No.6, pp.937–940, 2019.
[33] D. Saidulu, V. Devasekhar, and V. Swathi, "Secured MapReduce Based K-Means Clustering in Big Data Framework," International Journal of Computer Sciences and Engineering, Vol.7, No.5, pp.1427–1430, 2019.

Snehal K Joshi, "Enhanced K-Means Clustering through Density-Based Inter-Centroid Distance Optimization," International Journal of Computer Sciences and Engineering, Vol.13, Issue.4, pp.68-77, 2025.

This research paper examines the technical architecture, performance characteristics, and development ecosystems of leading blockchain platforms. The research elaborates the different aspects of blockchain technology through comparative analysis of Ethereum, Hyperledger Fabric, Solana, Polkadot, Cosmos, and Corda and hence, we evaluate their distinct approaches to consensus, scalability, security, and programmability. The study reveals significant trade-offs between decentralization, performance, and developer accessibility across platforms. The smart contract development paradigm is described in the study. We identify emerging trends including modular blockchain architectures, application-specific chains, and cross-chain interoperability solutions. The performance of developer ecosystems and tooling security models is illustrated. The study also reveals real world applications and its use case alignment. The different emerging trends in blockchain technology development in the use of real world technology is discussed in the study. This comprehensive assessment provides guidance for organizations selecting blockchain platforms based on specific use case requirements, technical constraints, and strategic objectives.

Block Chain, Bitcoin, Ethereum, Hyperledger Fabric, Solana, Polkadot, Cosmos, Corda

[1] G. Wood, “Ethereum: A secure decentralised generalised transaction ledger,” Ethereum Project Yellow Paper, Vol.151, pp.1-32, 2014.
[2] E. Androulaki et al., “Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains,” In Proceedings of the Thirteenth EuroSys Conference (EuroSys `18), New York, USA, 30, pp.1-15, 2018.
[3] A. Yakovenko, “Solana: A new architecture for a high performance blockchain v0. 8.13,” 2018.
[4] G. Wood, “Polkadot: Vision for a heterogeneous multi-chain framework,” 2016.
[5] J. Kwon, E. Buchman, “Cosmos: A Network of Distributed Ledgers,” 2016.
[6] R.G. Brown et al., “Corda: An Introduction,” 2016.
[7] V. Buterin, “Endgame,” Ethereum Foundation Blog, 2021.
[8] A. Zamyatin et al., “SoK: Communication Across Distributed Ledgers,” 2021.
[9] L. Gudgeon et al., “SoK: Layer-Two Blockchain Protocols,” 2020.
[10] A. Tomescu, et al., “ZK-Bridges: Trustless Cross-Chain Transfers,” 2022.
[11] V. Buterin et al., “Rollup-centric Ethereum roadmap,” 2020.
[12] J. Benet, N. Greco, “Filecoin: A Decentralized Storage Network,” 2018.
[13] “The Merge,” Ethereum Foundation, 2022.
[14] “Solana Performance Report,” Solana Labs, 2022.
[15] “Tezos: Technical Overview,” Tezos Foundation, 2021.
[16] A. Kedar, K. Waje, S. Ghuge, P. Badgujar, P. Lahare, "A Review Paper on Blockchain for Supply Chain Anti-Counterfeiting," International Journal of Computer Sciences and Engineering, Vol.2, Issue.0, pp.51-55, 2024.
[17] C. Pradhan, A. Trehan, "Integration of Blockchain Technology in Secure Data Engineering Workflows," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.1-7, 2025.

Divyakant Meva, Anand John, "Comprehensive Study of Various Blockchain Development Platforms," International Journal of Computer Sciences and Engineering, Vol.13, Issue.4, pp.78-83, 2025.

Air pollution is becoming a serious problem in cities, with direct impacts on both public health and the environment. Being able to predict the Air Quality Index (AQI) accurately and on time is important for taking steps to prevent or reduce pollution. This study explores the use of IoT-based gas sensors to help forecast AQI, focusing on four main pollutants: Carbon Monoxide (CO), Sulfur Dioxide (SO?), Nitrogen Dioxide (NO?), and Ammonia (NH?). Three different sensors were tested for each gas to see how well they performed. After calibrating the sensors, their readings were converted to parts per million (ppm), and artificial data was created to represent three months of half-hourly readings. A machine learning method, Random Forest Regressor, was used to check how accurate each sensor was, based on performance measures like MAE, RMSE, and R² Score. Sensor, referred as Sensor S1, gave the best results across all gases, showing better accuracy and reliability than the others. This research shows how important it is to choose and calibrate the right sensors for monitoring air quality and could help build better systems for predicting AQI in real time. The findings offer useful information for improving environmental monitoring with smart technology.

Air Quality Index (AQI), IoT-based Gas Sensors, Machine Learning Calibration, Sensor Performance Evaluation, Random Forest Regressor

[1] Central Pollution Control Board, “National Air Quality Index (NAQI),” Ministry of Environment, Forest and Climate Change, Govt. of India, 2014.
[2] World Health Organization, “Air pollution,” 2023.
[3] S. Kumar, V. Singh, and A. K. Sharma, “Air quality prediction using machine learning techniques: A review,” Environmental Science and Pollution Research, Vol.29, pp.4259–4278, 2022.
[4] CPCB, “National Ambient Air Quality Standards (NAAQS),” 2009.
[5] J. Kim, S. Jang, and H. Park, “IoT-based air quality monitoring system using low-cost sensors,” in Proc. IEEE Sensors, pp.1–4, 2020.
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[7] A. Hasenfratz et al., “Deriving high-resolution urban air pollution maps using mobile sensor nodes,” Pervasive and Mobile Computing, Vol.16, pp.268–285, 2015.
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