International Journal of Engineering Works

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Detetion and Prevention of Irrelevent Data Through Data Cleansing

Published online https://doi.org/

Abstract

A file oriented unstructured data collected and transformed into the data warehouse .Two or more records identified separately actually represent same real world entity, detection and prevention to improve data quality. The proposed technique introduces smart tokens of most representative attributes by sorting those tokens identical records are bring into close neighborhood, record duplicates are identified and removed from the data. Clean consistent and non duplicated data loaded into warehouse. The technique is a mile stone for cleaning data as with the explosive amount of data recording it is the need of time that more corrected data to be provided to the data mangers for effective decisions making.

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Current Issue - 2025

Reinforcement Learning-Driven Dynamic Investment Strategy Optimization Using Cloud-Based Simulation Frameworks

Vol. 12, Issue 11, PP. 207-223, November 2025

Published online https://doi.org/10.5281/zenodo.17662007

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Abstract

This study presents a new framework of cloud-based multi-agent reinforcement learning an active dynamic portfolio optimization framework, which overcomes the inherent issues of adaptive asset allocation in changing market environment. The proposed architecture works with dedicated agents which are trained through Proximal Policy Optimization used to identify market regimes in real-time on the basis of which agent contributions are weighted by an attention-based meta-controller. The distributed cloud infrastructure provides the ability to perform simultaneously with experience collection and release asynchronous gradient updates and converges 87% faster than single agent baselines. Detailed analysis on empirical S&P 500 and global ETF indexes data over a series of market cycles indicates significant performance benefits: 21.4% annualized returns and Sharpe ratio of 1.57, or 35.3% better than the same idea using state-of-the-art single-agent deep-reinforcement learning algorithms and 118% compared to conventional mean-variance optimization. The model has strong risk control strength that has a maximum drawdown of 11.8% as opposed to the 24.3% in buy-and-hold models but has high returns in both bullish and high volatility bear markets. The important roles of multi-agent specialization, attention mechanisms and cloud-based scalability are authenticated by ablation studies. These results form a huge breakthrough that can be seen in autonomous portfolio management systems that can dynamically adjust to changing financial environments in real-time.

Author

Shahzad Anwar

Cite

Shahzad Anwar “Reinforcement Learning-Driven Dynamic Investment Strategy Optimization Using Cloud-, Vol. 12, Issue 11, PP. 207-233, November 2025. https://doi.org/10.5281/zenodo.17662007, ,

References

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[2]. Cao, L. Lei, Y. Liu, Z. Chen, S. Shi, B. Li, W. Xu, and Z.-X. Yang, “Skeleton information-driven reinforcement learning framework for robust and natural motion of quadruped robots,” Symmetry, vol. 17, no. 11, p. 1787, Oct. 2025. DOI: 10.3390/sym17111787

[3]. Zhou, L. Dong, and Y. Wang, “Prediction and control of hovercraft cushion pressure based on deep reinforcement learning,” J. Mar. Sci. Eng., vol. 13, no. 11, p. 2058, Oct. 2025. DOI: 10.3390/jmse13112058

[4]. S. Li, M. López-Benítez, E. G. Lim, F. Ma, M. Cao, L. Yu, and X. Qin, “Enabling cooperative autonomy in UUV clusters: A survey of robust state estimation and information fusion techniques,” Drones, vol. 9, no. 11, p. 752, Oct. 2025. DOI: 10.3390/drones9110752

[5]. T. Dieguez and S. Gomes, “Bridging intention and action in sustainable university entrepreneurship: The role of motivation and institutional support,” Adm. Sci., vol. 15, no. 11, p. 422, Oct. 2025. DOI: 10.3390/admsci15110422

[6]. Nguyen, O. Mayet, and S. Desai, “Operational and supply chain growth trends in basic apparel distribution centers: A comprehensive review,” Logistics, vol. 9, no. 4, p. 154, Oct. 2025. DOI: 10.3390/logistics9040154

[7]. L. AlTerkawi and M. AlTarawneh, “Federated decision transformers for scalable reinforcement learning in smart city IoT systems,” Future Internet, vol. 17, no. 11, p. 492, Oct. 2025. DOI: 10.3390/fi17110492

[8]. Nucci and G. Papadia, “Hybrid genetic algorithm and deep reinforcement learning framework for IoT-enabled healthcare equipment maintenance scheduling,” Electronics, vol. 14, no. 21, p. 4160, Oct. 2025. DOI: 10.3390/electronics14214160

[9]. X. Hao, S. Wang, X. Liu, T. Wang, G. Qiu, and Z. Zeng, “Q-learning-based multi-strategy topology particle swarm optimization algorithm,” Algorithms, vol. 18, no. 11, p. 672, Oct. 2025. DOI: 10.3390/a18110672

[10]. Fulgione, S. Palladino, L. Esposito, S. Sarfarazi, and M. Modano, “A multi-stage framework combining experimental testing, numerical calibration, and AI surrogates for composite panel characterization,” Buildings, vol. 15, no. 21, p. 3900, Oct. 2025. DOI: 10.3390/buildings1521390

Generative AI-Driven Financial Market Simulation and Forecasting Using Cloud-Based Computational Frameworks

Vol. 12, Issue 11, PP. 197-206, November 2025

Published online https://doi.org/10.5281/zenodo.17661001

ePub PDF

Abstract

The article presents a novel artificial intelligence based computational cloud-based financial market simulator and prediction model. The proposed system incorporates the state-of-the-art generative models, including the Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusions alongside combinations of hybrid time-series forecasting networks, including Prophet-LSTM or Transformer-based architectures. The framework operates at scale on the distributed cloud platforms (aws, Azure, GCP) that help to train and serve in parallel and provide training and real-time inference. According to the provided experimental analysis of the use of the S and P 500 and cryptocurrency as models reveals a higher accuracy of 94.7% in prediction, a lower mean error of 15.3% and a reduced time of 3.2 longer to train when trained on the basis of parallelization in a cloud-based environment. The system exhibits high scenarios bearing capabilities which are 10,000 synthetics produced in a second at statistical accuracy up to the past tendencies. The research contributes to the creation of financial technology through synergistic integration of generative AI, cloud computing, and quantitative finance methods.

Author

Shahzad Anwar

Cite

Shahzad Anwar “Generative AI-Driven Financial Market Simulation and Forecasting Using Cloud-Based , Vol. 12, Issue 11, PP. 197-206, November 2025. https://doi.org/10.5281/zenodo.17661001., ,

References

[1] J. Li, Y. Liu, W. Liu, S. Fang, L. Wang, C. Xu, and J. Bian, “MarS: A financial market simulation engine powered by generative foundation model,” arXiv preprint arXiv:2409.07486, 2024. DOI: 10.48550/arXiv.2409.07486.

[2] D. Patel, G. Raut, S. N. Cheetirala, G. N. Nadkarni, R. Freeman, B. S. Glicksberg, E. Klang, and P. Timsina, “Cloud platforms for developing generative AI solutions,” arXiv preprint arXiv:2412.06044, 2024. DOI: 10.48550/arXiv.2412.06044.

[3] M. Chen, S. Mei, J. Fan, and M. Wang, “Opportunities and challenges of diffusion models for generative AI,” National Science Review, vol. 11, no. 12, p. nwae348, 2024. DOI: 10.1093/nsr/nwae348.

[4] S. Kwon and Y. Lee, “Can GANs learn the stylized facts of financial time series?” in Proc. 5th ACM Int. Conf. AI Finance (ICAIF ’24), 2024, pp. 1–8. DOI: 10.1145/3677052.3698661.

[5] Z. Wang and C. Ventre, “A financial time series denoiser based on diffusion models,” in Proc. 5th ACM Int. Conf. AI Finance (ICAIF ’24), 2024, pp. 1–9. DOI: 10.1145/3677052.3698649.

[6] S. S. Dubey, V. Astvansh, and P. K. Kopalle, “Generative AI solutions to empower financial firms,” Journal of Public Policy & Marketing, vol. 44, no. 3, pp. 411–435, 2025. DOI: 10.1177/07439156241311300.

[7] K. M. Antony, “An empirical analysis of the impact of cloud computing and distributed systems on corporate finance decision-making, risk management, and financial performance in a digitally transformed economy,” International Journal of Finance, vol. 38, no. 2, pp. 1–25, 2025. DOI: 10.34218/IJFIN.38.2.001.

[8] S. K. Mogali, “Transforming digital banking with AI-enhanced cloud infrastructure: A strategic perspective on risk management,” Economic Sciences, vol. 21, no. 1, pp. 396–407, 2025. DOI: 10.69889/j7sgrw39.

[9] H. Takahashi and T. Mizuno, “Generation of synthetic financial time series by diffusion models,” Quantitative Finance, vol. 25, no. 3, pp. 1–20, 2025. DOI: 10.1080/14697688.2025.2528697.

[10] C. Sai, V. Kumar, and P. Sharma, “Generative AI for finance: Applications, case studies and challenges,” Expert Systems, vol. 42, no. 2, p. e13760, 2025. DOI: 10.1111/exsy.70018

Modeling and Analysis of Tumor Growth under Virotherapy, Chemotherapy, and Immune System Dynamics

Vol. 12, Issue 10, PP. 191-196, October 2025

Published online https://doi.org/10.5281/zenodo.17360342

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Abstract

This study presents a nonlinear mathematical model describing the interaction of uninfected cells, infected cells, viral particles, immune cells (T-cells), and chemotherapy. The analytical results establish that the system preserves biological feasibility, with all state variables remaining positive and bounded over time. Numerical simulations are carried out using biologically motivated parameters, and the results provide insights into the balance between viral replication, immune clearance, and chemotherapy dynamics. The findings highlight the critical role of immune T-cells and chemotherapy factors in shaping the infection outcome and suggest possible directions for improving therapeutic strategies through mathematical analysis.

Author

Siraj Ahmad, Zia Ullah Afridi, Bibi Shabana

Cite

Siraj Ahmad, Zia Ullah Afridi, Bibi Shabana “Modeling and Analysis of Tumor Growth under Virotherapy, Chemotherapy, and Immune System Dynamics” International Journal of Engineering Works, Vol. 12, Issue 10, PP. 191-196, October 2025. https:// doi.org/10.5281/zenodo.17360342.

References

[1]. Jenner, A. L. (2020). Applications of mathematical modelling in oncolytic virotherapy and immunotherapy. Bulletin of the Australian Mathematical Society, 101(3), 522-524.

[2]. Jenner, A. L., Yun, C. O., Kim, P. S., & Coster, A. C. (2018). Mathematical modelling of the interaction between cancer cells and an oncolytic virus: insights into the effects of treatment protocols. Bulletin of mathematical biology, 80(6), 1615-1629.

[3]. Jenner, A. L., Coster, A. C., Kim, P. S., & Frascoli, F. (2018). Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy.

[4]. Jenner, A. L., Kim, P. S., & Frascoli, F. (2019). Oncolytic virotherapy for tumours following a Gompertz growth law. Journal of theoretical biology, 480, 129-140.

[5]. Jenner, A. L., Frascoli, F., Coster, A. C., & Kim, P. S. (2020). Enhancing oncolytic virotherapy: Observations from a Voronoi Cell-Based model. Journal of Theoretical Biology, 485, 110052.

[6]. Jenner, A. L., Yun, C. O., Yoon, A., Coster, A. C., & Kim, P. S. (2018). Modelling combined virotherapy and immunotherapy: strengthening the antitumour immune response mediated by IL-12 and GM-CSF expression. Letters in Biomathematics, 5(sup1), S99-S116.

[7]. Yadav, M., & Singh, P. (2025). Stability analysis and numerical simulations for interactions of tumor-immune system with drug delivery. Chaos, Solitons & Fractals, 192, 115971.

[8]. Kim, P. S., Crivelli, J. J., Choi, I. K., Yun, C. O., & Wares, J. R. (2015). Quantitative impact of immunomodulation versus oncolysis with cytokine-expressing virus therapeutics. Mathematical biosciences and engineering, 12(4), 841-858.

[9]. Norton, L. (1988). A Gompertzian model of human breast cancer growth. Cancer research, 48(24_Part_1), 7067-7071.

[10]. Diebner, H. H., Zerjatke, T., Griehl, M., & Roeder, I. (2018). Metabolism is the tie: The Bertalanffy-type cancer growth model as common denominator of various modelling approaches. Biosystems, 167, 1-23.

[11]. Murphy, H., Jaafari, H., & Dobrovolny, H. M. (2016). Differences in predictions of ODE models of tumor growth: a cautionary example. BMC cancer, 16(1), 163.

[12]. L. G. de Pillis and A. Radunskaya, “A mathematical model of immune response to tumor invasion,” in Computational fluid and solid mechanics 2003, Elsevier, 2003, pp. 1661–1668

[13]. P. Unni and P. Seshaiyer, “Mathematical modeling, analysis, and simulation of tumor dynamics with drug interventions,” Comput. Math. Methods Med., vol. 2019, no. 1, p. 4079298, 2019.

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[15]. L. G. de Pillis, W. Gu, and A. E. Radunskaya, “Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations,” J. Theor. Biol., vol. 238, no. 4, pp. 841–862, 2006.

Effect of Binder on The Performance of SnO2 Based ETL Perovskite Solar Cell

Vol. 12, Issue 09, PP. 186-190, September 2025

Published online https://doi.org/10.5281/zenodo.17200834

ePub PDF

Abstract

FTO (fluorine-doped tin oxide) based perovskite solar cell using SnO2 as ETL which is a low temperature material has gained significant interest because of its wide band gap, high electron mobility, high chemical stability and good antireflective properties. However, other metallic oxide materials like TiO2 have low charge mobility, high charge recombination rate, miss match of band gap to the perovskite layer, low compatibility with perovskite layer and cause more degradation when exposed to light. Due to their outstanding optical, electrical, mechanical qualities, low temperature synthesis and good compatibility with the PSC layer SnO2 material have been widely employed in PSCs to address these difficulties. Due to its many advantageous characteristics, SnO2 is one of the most potential materials for high-performance PSC modules with high efficiency in the future. This study will demonstrate how we form SnO2 solution and how we utilize SnO2 to work as a ETL efficiently for that we Examined how the addition of binder affect the optical, morphological, and structural characteristics of SnO2 in PSC based on FTO. We use Terpineol as a binder using ethanol as a solvent with some additives like HCL to increase stability. The measurements taken 2.7g of Sncl2.2H2O, 10ml HCL and 10ml of ethanol. Stirred the mixture using magnetic stirrer at room temperature for 1hr with 1000 rpm on Hot plate. Utilizing the spin coating process, deposit the solution for 35 seconds at 2500 RPM. The morphology, crystallinity and transmittance of all the samples were characterized using atomic force microscope AFM, X-ray diffraction spectrometer and UV-VIS.

Author

Ziaullah, Dr.Adnan Daud, Noor us Sabah, Umar Zargham

Cite

Ziaullah, Dr.Adnan Daud, Noor us Sabah, Umar Zargham “Effect of Binder on The Performance of SnO2 Based ETL Perovskite Solar Cell ” , Vol. 12, Issue 09, PP. 186-190, September 2025. https:// doi.org/ 10.5281/zenodo.17200834., , , ,

References

[1] J. Peng, L. Lu, and H. Yang, "Review on life cycle assessment of energy payback and greenhouse gas emission of solar photovoltaic systems," Renewable and sustainable energy reviews, vol. 19, pp. 255-274, 2013.

[2] R. Pulselli, E. Simoncini, F. Pulselli, and S. Bastianoni, "Emergy analysis of building manufacturing, maintenance and use: Em-building indices to evaluate housing sustainability," Energy and buildings, vol. 39, pp. 620-628, 2007.

[3] V. Tyagi, N. A. Rahim, N. Rahim, A. Jeyraj, and L. Selvaraj, "Progress in solar PV technology: Research and achievement," Renewable and sustainable energy reviews, vol. 20, pp. 443-461, 2013.

[4] Y. Chu and P. Meisen, "Review and comparison of different solar energy technologies," Global Energy Network Institute (GENI), San Diego, CA, vol. 1, pp. 1-52, 2011.

[5] M. M. Tavakoli, P. Yadav, R. Tavakoli, and J. Kong, "Surface engineering of TiO2 ETL for highly efficient and hysteresis‐less planar perovskite solar cell (21.4%) with enhanced open‐circuit voltage and stability," Advanced Energy Materials, vol. 8, p. 1800794, 2018.

[6] D. Prochowicz, M. M. Tavakoli, M. Wolska-Pietkiewicz, M. Jędrzejewska, S. Trivedi, M. Kumar, et al., "Suppressing recombination in perovskite solar cells via surface engineering of TiO2 ETL," Solar Energy, vol. 197, pp. 50-57, 2020.

[7] Y. Wang, J. Wan, J. Ding, J. S. Hu, and D. Wang, "A rutile TiO2 electron transport layer for the enhancement of charge collection for efficient perovskite solar cells," Angewandte Chemie International Edition, vol. 58, pp. 9414-9418, 2019.

[8] Z. Arshad, S. Shakir, A. H. Khoja, A. H. Javed, M. Anwar, A. Rehman, et al., "Performance analysis of calcium-doped titania (TiO2) as an effective electron transport layer (ETL) for perovskite solar cells," Energies, vol. 15, p. 1408, 2022.

[9] J. Song, E. Zheng, J. Bian, X.-F. Wang, W. Tian, Y. Sanehira, et al., "Low-temperature SnO 2-based electron selective contact for efficient and stable perovskite solar cells," Journal of Materials Chemistry A, vol. 3, pp. 10837-10844, 2015.

[10] J. Barbé, M. L. Tietze, M. Neophytou, B. Murali, E. Alarousu, A. E. Labban, et al., "Amorphous tin oxide as a low-temperature-processed electron-transport layer for organic and hybrid perovskite solar cells," ACS applied materials & interfaces, vol. 9, pp. 11828-11836, 2017

Boosting Perovskite Tandem Solar Cell Efficiency

Vol. 12, Issue 09, PP. 183-185, September 2025

Published online https://doi.org/10.5281/zenodo.17200296

ePub PDF

Abstract

To minimize losses in perovskite-silicon tandem solar cells, we apply optical modeling techniques that require precise optical constants obtained through methods like spectrophotometry and ellipsometry. Our research focuses on validating optical properties to reduce losses, thereby improving overall efficiency compared to single-junction cells. Recent developments have highlighted perovskites with wide bandgaps. Achieving current matching in two-terminal devices is essential due to interference effects in multi-layered tandems. Extensive modeling investigated varying bandgaps and pyramid heights to attain current matching, demonstrating that a 1.7 eV bandgap perovskite can achieve high efficiency. Reducing losses can further enhance performance.

Author

Sundas Khan

Cite

Sundas Khan “Boosting Perovskite Tandem Solar Cell Efficiency” International Journal of Engineering Works, Vol. 12, Issue 09, PP. 183-185, September 2025. https:// doi.org/10.5281/zenodo.17200296.

References

[1] Z. J. Yu, M. Leilaeioun, and Z. Holman, “Selecting tandem partners for silicon solar cells,” Nature Energy, vol. 1, p. 16137, 2016.

[2] D. P. McMeekin et al., “A mixed-cation lead mixedhalide perovskite absorber for tandem solar cells,” Science, vol. 351, no. 6269, pp. 151-155, 2016.

[3] S. Manzoor et al., “Improved light management In planner silicon And perovskite Solar cell Using PDMS scattering layer” 2017.

[4] K. A. Bush et al., “Compositional Engineering for Efficient Wide Band Gap Perovskites with Improved Stability to Photoinduced Phase Segregation,” ACS Energy Letters, 2018.

[5] S. Manzoor et al., “Optical modeling of widebandgap perovskite solar cells,” in preparation, 2018.

[6] Kevin A Bush et al., “Minimizing Current and Voltage Losses to Reach 25% Efficient Monolithic Two-Terminal Perovskite–Silicon Tandem Solar Cells” 2018

[7] Z. Yu and Z. Holman, "Predicting the Efficiency of the Silicon Bottom Cell in a Two-Terminal Tandem Solar Cell," IEEE PVSC, 2017.

[8] Z. J. Yu, M. Leilaeioun, and Z. Holman, "Selecting tandem partners for silicon solar cells," Nature Energy, vol. 1, p. 16137, 2016.

[9] D. P. McMeekin et al., "A mixed-cation lead mixedhalide perovskite absorber for tandem solar cells," Science, vol. 351, no. 6269, pp. 151-155, 2016.

[10] K. A. Bush et al., "Compositional Engineering for Efficient Wide Band Gap Perovskites with Improved Stability to Photoinduced Phase Segregation," ACS Energy Letters, 2018.

[11] M. Filipič et al., "CH 3 NH 3 PbI 3 perovskite/silicon tandem solar cells: characterization based optical simulations," Optics express, vol. 23, no. 7, pp. A263-A278, 2015

Brain Tumor Segmentation via Efficient Thresholding and Binary K-Means Integration

Vol. 12, Issue 09, PP. 175-182, September 2025

Published online https://doi.org/10.5281/zenodo.17054880

ePub PDF

Abstract

In medical image segmentation, the identification, characterization, and visualization of a tumor’s dimension and region are considered to be very crucial, tedious, and time-consuming tasks. In spite of intensive research, segmentation is still one of the most challenging problems in the medical field due to the variety of image content. In this paper, we propose a new hybrid method for detecting and segmenting tumors in T2-weighted magnetic resonance imaging (MRI) brain scans. The approach begins with an efficient thresholding technique, followed by conventional morphological filtering, and then applies the binary K-means clustering algorithm. Experimental results and performance metrics demonstrate that the proposed method effectively identifies and segments tumors in MRI brain scans with significant accuracy.

Author

Muhammad Saad

Cite

Muhammad Saad “Brain Tumor Segmentation via Efficient Thresholding and Binary K-Means Integration” International Journal of Engineering Works, Vol. 12, Issue 09, PP. 175-182, September 2025. https:// doi.org/10.5281/zenodo.17054880

References

Y. Tan, GPU-based Parallel Implementation of Swarm Intelligence Algorithms. Morgan Kaufmann, 2016.
E. Abdel-Maksoud, M. Elmogy, and R. Al-Awadi, "Brain tumor segmentation based on a hybrid clustering technique," Egyptian Informatics Journal, vol. 16, no. 1, pp. 71–81, 2015.
National Cancer Institute, "Tumor definition," [Online]. Available: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/tumor. [Accessed: Aug. 2025].
M. A. Haidekker, Medical Imaging Technology. Springer, 2013.
A. Mehndiratta and F. L. Giesel, Brain Tumor Imaging, Diagnostic Techniques and Surgical Management of Brain Tumors. InTech Publishers, 2011, ISBN: 978-953-307-589-1.
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M.-N. Wu, C.-C. Lin, and C.-C. Chang, "Brain tumor detection using color-based k-means clustering segmentation," in Proceedings of the 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), vol. 2, pp. 245–250, IEEE, 2007.
V. S. Dessai, M. P. Arakeri, and G. R. M. Reddy, "A parallel segmentation of brain tumor from magnetic resonance images," in Proceedings of the 3rd International Conference on Computing, Communication and Networking Technologies (ICCCNT 2012), pp. 1–6, IEEE, 2012.
J. Subhashini and J. Vijay, "An efficient brain tumor detection methodology using k-means clustering algorithm," in Proceedings of the International Conference on Communication and Signal Processing, 2013.
K. Sinha and G. R. Sinha, "Efficient segmentation methods for tumor detection in MRI images," in Proceedings of the IEEE Students’ Conference on Electrical, Electronics and Computer Science, pp. 1–6, IEEE, 2014.

An Intelligent Model for Detection of Breast Cancer based on Convolutional Neural Network

Vol. 12, Issue 08, PP. 170-174, August 2025

Published online https://doi.org/10.5281/zenodo.16908308

ePub PDF

Abstract

Cancer is among the deadliest diseases afflicting humanity. At present, there exists no successful therapy. Breast cancer is among the most common kinds of cancer. In 2020, the National Breast Cancer Foundation projected that approximately 276,000 fresh patients of invasive breast cancer and 48,000 fresh patients of non-invasive breast cancer were diagnosed in the USA. The patients have a 99% survival rate, as 64% of these cases are detected in initial stage of the disease. Artificial intelligence (AI) has been utilized to detect deadly diseases, which has enhanced the patient likelihood of survival by enabling early diagnosis and treatment. This research presented convolutional neural network (CNN) for the diagnosis of breast cancer disease automatically. The analysis has been carried out on a real-time invasive ductal carcinoma (IDC) dataset available at Kaggle. The dataset is preprocessed before being fed to CNN. The images is normalized to achieve a better accuracy. The developed model has an accuracy of 90% that is improved by 3% from the previous research paper. Different performances metrics are graphically represented in result section to analyze the model efficiency.

Author

Hussain Ahmad, Muhammad Zeb, Khalil-ur-Rahman

Cite

Hussain Ahmad, Muhammad Zeb, Khalil-ur-Rahman“An Intelligent Model for Detection of Breast Cancer based on Convolutional Neura, Vol. 12, Issue 08, PP. 170-174, August 2025. https:// doi.org/10.5281/zenodo.16908308.,

References

[1] Y. Qasim, H. Al-Sameai, O. Ali, and A. Hassan, "Convolutional neural networks for automatic detection of colon adenocarcinoma based on histopathological images," in International Conference of Reliable Information and Communication Technology, 2020: Springer, pp. 19-28.

[2] A. S. Sakr, "Automatic Detection of Various Types of Lung Cancer Based on Histopathological Images Using a Lightweight End-to-End CNN Approach," in 2022 20th International Conference on Language Engineering (ESOLEC), 2022, vol. 20: IEEE, pp. 141-146.

[3] N. A. Abujabal and A. B. Nassif, "Meta-heuristic algorithms-based feature selection for breast cancer diagnosis: A systematic review," in 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022: IEEE, pp. 1-6.

[4] X. Zhou et al., "A comprehensive review for breast histopathology image analysis using classical and deep neural networks," IEEE Access, vol. 8, pp. 90931-90956, 2020.

[5] D. H. Sutanto and M. Abd Ghani, "A Benchmark Feature Selection Framework for Non Communicable Disease Prediction Model," Advanced Science Letters, vol. 21, no. 10, pp. 3409-3416, 2015

[6] R. Gautam, P. Kaur, and M. Sharma, "A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings," Progress in Artificial Intelligence, vol. 8, no. 4, pp. 401-424, 2019

[7] M. Mahmood, B. Al-Khateeb, and W. M. Alwash, "A review on neural networks approach on classifying cancers," IAES International Journal of Artificial Intelligence, vol. 9, no. 2, p. 317, 2020.

[8] N. Fatima, L. Liu, S. Hong, and H. Ahmed, "Prediction of breast cancer, comparative review of machine learning techniques, and their analysis," IEEE Access, vol. 8, pp. 150360-150376, 2020.

[9] R. Kaur, H. GholamHosseini, R. Sinha, and M. Lindén, "Melanoma classification using a novel deep convolutional neural network with dermoscopic images," Sensors, vol. 22, no. 3, p. 1134, 2022.

[10] I. Elansary, A. Ismail, and W. Awad, "Efficient classification model for melanoma based on convolutional neural networks," in Medical Informatics and Bioimaging Using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments: Springer, 2021, pp. 15-27

Geo-Polymerization of Marble Sludge: A Way Forward for its Eco-friendly Utilization

Vol. 12, Issue 08, PP. 150-169, August 2025

Published online https://doi.org/10.5281/zenodo.16758040

ePub PDF

Abstract

Pakistan boasts abundant marble reserves, primarily concentrated in provinces like Khyber Pakhtunkhwa and Baluchistan. The extraction of marble blocks in these regions leads to the generation of marble sludge, comprising water and marble powder, which presents significant environmental challenges by contaminating water bodies, infiltrating groundwater, and posing health risks due to airborne particles. Given the pressing climate situation, it is imperative to address these concerns. To mitigate the environmental impact, we propose the application of a geo-polymerization technique. This method leverages marble powder, fly ash, and blast furnace slag to substitute cement in concrete production. Various mixtures were prepared, utilizing different proportions of these components and diverse liquid media. Particularly noteworthy was the use of a Na2SiO3-8M NaOH solution, which yielded concrete samples with significantly higher compressive strength compared to other media. Upon analyzing the results, it has been concluded that replacing 50% of cement with a combination of 25% marble powder and 25% fly ash, using this solution, resulted in an impressive 143% increase in strength compared to standard concrete (M20 grade) and other geo-polymer concretes. This innovative approach not only mitigates the environmental impact of marble sludge but also contributes to a circular economy by producing high-strength geo-polymer concrete suitable for a wide range of applications.

Author

Muhammad Irshad, Salim Raza, Zahid Ur Rehman, Sajjad Hussain, Ishaq Ahmad, Saira Sherin

Cite

Muhammad Irshad, Salim Raza, Zahid Ur Rehman, Sajjad Hussain, Ishaq Ahmad, Saira Sherin “Geo-Polymerization of Marble Sludge: A Way Forward for its Eco-friendly Utilization, Vol. 12, Issue 08, PP. 151-169, August 2025. https:// doi.org/10.5281/zenodo.16758040.,

References

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