i-manager Publications

i-manager's Journal on Data Science & Big Data Analytics (JDS)

Current Issue Vol. 2 Issue 2

Most Cited

Volume 2 Issue 2 July - December 2024

Research Paper

Breast Cancer Diagnosis Model Based on Convolutional Neural Networks' Multiple Architectures

Ayobami Ekundayo* , Enesi Femi Aminu, Winifred Omone Saliu, Oluwaseun Adeniyi Ojerinde, Uchenna Cosmas Ugwuoke

*-**** Department of Computer Science, Federal University of Technology, Minna, Nigeria.

Ekundayo, A., Aminu, E. F., Saliu, W. O., Ojerinde, O. A., and Ugwuoke, U. C. (2024). Breast Cancer Diagnosis Model Based on Convolutional Neural Networks' Multiple Architectures. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 1-19.

Abstract

In 2020, the World Health Organization (WHO) estimated that 2.3 million women worldwide were diagnosed with breast cancer, which resulted in 685,000 deaths. According to the projections, the number of women who have been diagnosed with breast cancer over the last five years before and by the end of 2020 was expected to reach 7.8 million, making it the most common type of cancer worldwide. Early diagnosis could prevent the ailment, however, lack of availability of health facilities and the cost of accessing treatment, especially in developing nations, are among the challenges confronting the solution. With the advent of artificial intelligence and machine learning models, specifically Convolutional Neural Networks (CNNs), considering their multiple architectures is highly promising to address the challenge of early diagnosis. Therefore, this study aims to propose an architecture of CNNs that gives the best accuracy, F1 score, and Cohen Kappa score among the Custom Optimized CNN, ResNet, and EfficientNet architectures. From the results, ResNet's performance across the five metrics outweighs the other two architectures. While ResNet reported an accuracy, precision, and F1 score of 0.9987, 0.9934, and 0.9950, respectively, EfficientNet, which has the second performance, reported 0.9977, 0.9914, and 0.9939 as accuracy, precision, and F1 score, respectively. Therefore, the best-performing architecture can be deployed for other available breast cancer datasets in order to ensure its total efficiency.

References

[1]. Abdelli, A., Saouli, R., Djemal, K., & Youkana, I. (2020, November). Combined datasets for breast cancer grading based on multi-CNN architectures. In 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-7). IEEE.

[2]. Alanazi, S. A., Kamruzzaman, M. M., Islam Sarker, M. N., Alruwaili, M., Alhwaiti, Y., Alshammari, N., & Siddiqi, M. H. (2021). Boosting breast cancer detection using convolutional neural network. Journal of Healthcare Engineering, 2021(1), 5528622.

[3]. Altan, G. (2020). Deep learning-based mammogram classification for breast cancer. International Journal of Intelligent Systems and Applications in Engineering, 8(4), 171–176.

[4]. Burçak, K. C., Baykan, Ö. K., & Uğuz, H. (2021). A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model. The Journal of Supercomputing, 77(1), 973-989.

[5]. Das, H. S., Das, A., Neog, A., Mallik, S., Bora, K., & Zhao, Z. (2023). Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach. Frontiers in Genetics, 13, 1097207.

[6]. El Adoui, M., Mahmoudi, S. A., Larhmam, M. A., & Benjelloun, M. (2019). MRI breast tumor segmentation using different encoder and decoder CNN architectures. Computers, 8(3), 52.

[7]. Ereken, O. F., & Tarhan, C. (2020). Breast cancer detection using convolutional neural networks. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT).

[8]. Houssein, E. H., Emam, M. M., & Ali, A. A. (2022). An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm. Neural Computing and Applications, 34(20), 18015-18033.

[9]. Khan, M. H., Boodoo-Jahangeer, N., Dullull, W., Nathire, S., Gao, X., Sinha, G. R., & Nagwanshi, K. K. (2021). Multi-class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN). Plos One, 16(8), e0256500.

[10]. Mahmood, T., Li, J., Pei, Y., Akhtar, F., Rehman, M. U., & Wasti, S. H. (2022). Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach. Plos One, 17(1), e0263126.

[11]. Nawaz, M., Sewissy, A. A., & Soliman, T. H. A. (2018). Multi-class breast cancer classification using deep learning convolutional neural network. International Journal of Advanced Computer Science and Applications, 9(6), 316-332.

[12]. Niu, J., Li, H., Zhang, C., & Li, D. (2021). Multi scale attention based convolutional neural network for classification of breast masses in mammograms. Medical Physics, 48(7), 3878-3892.

[13]. Oyelade, O. N., & Ezugwu, A. E. (2021). A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images. Scientific Reports, 11(1), 19940.

[14]. Raza, A., Ullah, N., Khan, J. A., Assam, M., Guzzo, A., & Aljuaid, H. (2023). DeepBreastCancerNet: A novel deep learning model for breast cancer detection using ultrasound images. Applied Sciences, 13(4), 2082.

[15]. Rehman, K. U., Li, J., Pei, Y., Yasin, A., Ali, S., & Saeed, Y. (2021). Architectural distortion-based digital mammograms classification using depth wise convolutional neural network. Biology, 11(1), 15.

[16]. Samala, R. K., Chan, H. P., Hadjiiski, L., Helvie, M. A., Richter, C. D., & Cha, K. H. (2018). Breast cancer diagnosis in digital breast tomosynthesis: Effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Transactions on Medical Imaging, 38(3), 686-696.

[17]. Samee, N. A., Atteia, G., Meshoul, S., Al-antari, M. A., & Kadah, Y. M. (2022). Deep learning cascaded feature selection framework for breast cancer classification: Hybrid CNN with univariate-based approach. Mathematics, 10(19), 3631.

[18]. Shivhare, E., & Saxena, V. (2021). Breast cancer diagnosis from mammographic images using optimized feature selection and neural network architecture. International Journal of Imaging Systems and Technology, 31(1), 253-269.

[19]. Tasnim, Z., Shamrat, F. J. M., Islam, M. S., Rahman, M. T., Aronya, B. S., Muna, J. N., & Billah, M. M. (2021). Classification of breast cancer cell images using multiple convolution neural network architectures. International Journal of Advanced Computer Science and Applications, 12(9), 308–315.

[20]. Vijayakumar, K., Kadam, V. J., & Sharma, S. K. (2021). Breast cancer diagnosis using multiple activation deep neural network. Concurrent Engineering, 29(3), 275-284.

[21]. Xie, L., Zhang, L., Hu, T., Huang, H., & Yi, Z. (2020). Neural networks model based on an automated multi-scale method for mammogram classification. Knowledge-Based Systems, 208, 106465.

[22]. Yektaei, H., Manthouri, M., & Farivar, F. (2019). Diagnosis of breast cancer using multiscale convolutional neural network. Biomedical Engineering: Applications, Basis and Communications, 31(5), 1950034.

[23]. Yurttakal, A. H., Erbay, H., İkizceli, T., & Karaçavuş, S. (2020). Detection of breast cancer via deep convolution neural networks using MRI images. Multimedia Tools and Applications, 79(21), 15555-15573.

[24]. Zhang, C., Zhao, J., Niu, J., & Li, D. (2020). New convolutional neural network model for screening and diagnosis of mammograms. PLoS One, 15(8), e0237674.

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Research Paper

Impact of Artificial Intelligence on Cyber Shopping in Kanniyakumari District

Goldie Sheila Jesolit C. S.* , Chithra James**

*-** Department of Commerce, Women's Christian College, Nagercoil, Tamil Nadu, India.

Jesolit, C. S. G. S., and James, C. (2024). Impact of Artificial Intelligence on Cyber Shopping in Kanniyakumari District. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 20-27.

Abstract

Cyber shopping worldwide has developed significantly, especially after the COVID-19 pandemic. Due to rapid technological advances, Artificial Intelligence (AI) plays an increasingly dynamic role in cyber shopping. AI in cyber shopping tracks customers' choices, buying patterns, preferences, purchase frequency, and spending on products, helping meet the diverse needs of cyber shoppers. The study aims to examine the impact of AI on cybershoppers in Kanniyakumari District of Tamil Nadu, India. The primary data collection was through the convenience sampling method, and the sample size is 200. Secondary data collection was from journals, books, websites, and databases. Age plays a substantial role, as age-related differences explain why younger and older consumers display distinct preferences and levels of comfort with these technologies. Amazon is the most frequently used AI-based cyber shopping platform by customers, while Indiamart is the least used. The findings suggest that demographic factors are crucial in shaping customer preferences and engagement with online shopping platforms.

References

[1]. Jangra, G., & Jangra, M. (2022, September). Role of Artificial Intelligence in online shopping and its impact on consumer purchasing behaviour and decision. In 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-7). IEEE.

[2]. Gil de Zúñiga, H., Goyanes, M., & Durotoye, T. (2024). A scholarly definition of artificial intelligence (AI): Advancing AI as a conceptual framework in communication research. Political Communication, 41(2), 317-334.

[3]. Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial Intelligence in E-Commerce: A bibliometric study and literature review. Electronic Markets, 32(1), 297-338.

[4]. Lari, H. A., Vaishnava, K., & Manu, K. S. (2022). Artifical Intelligence in E-commerce: Applications, implications and challenges. Asian Journal of Management, 13(3), 235-244.

[5]. Pillarisetty, R., & Mishra, P. (2022). A review of AI (Artificial Intelligence) tools and customer experience in online fashion retail. International Journal of E-Business Research (IJEBR), 18(2), 1-12.

[6]. Singh, N., Chaturvedi, D. D., Mittal, A., & Mittal, A. (2023). Impact of Artificial Intelligence in online customer satisfaction: An empirical study using multiple regression analysis. European Economic Letters, 13 (1), 396-409.

[7]. Subbaiah, P. V., Jyothsna, M., Manjushree, p., & Kondamudi, S. G. (2024). Exploring the influence of Artificial Intelligence (AI) on online purchase decisions: In case of consumer's prospective. International Journal of Intelligent Systems and Applications in Engineering, 12 (10), 13-20.

[8]. Kumar, C. M., & Senthilmurugan, T. (2024). Impact on online purchasing behavior: The influence of Artificial Intelligence. Educational Administration: Theory and Practice, 30(5), 2292-2295.

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Research Paper

An Interactive Visualization and Data Analysis of Supermarket Store

Likita H. L.* , Hiresh Yadav, Anjali Hedaoo, Swagota Bera, Siddhartha Choubey

*-***** Shri Shankaracharya Engineering College, Bhilai, Chhattisgarh, India.

Likita, H. L., Yadav, H., Hedaoo, A., Bera, S., and Choubey, S. (2024). An Interactive Visualization and Data Analysis of Supermarket Store. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 28-34.

Abstract

In the ever-evolving landscape of retail, the utilization of data-driven insights plays a pivotal role in enhancing decision- making processes. This paper presents an advanced approach to the development and implementation of an interactive visualization system tailored specifically for the analysis of data within a supermarket store environment. In order to predict the sales of a business, an intelligent model was built using Linear Regression, LASSO Regression, and XGBoost techniques, which have been shown to be more effective than existing models. The proposed system integrates data pre-processing, feature engineering, and algorithmic enhancements to analyze customer behavior, sales trends, and inventory efficiency. Key performance metrics like RMSE and R-squared validate the efficacy of the models, highlighting the XGBoost algorithm's exceptional performance. This study aims to optimize decision-making in inventory management, marketing strategies, and sales forecasting, ensuring data-driven insights for improved supermarket operations.

References

[1]. Abhinaya, C. S. D., Lahari, B., Priya, C. D., Anjali, D., Navya, B. S., & Jyothi, B. S. (2023). Supermarket sales prediction using machine learning. EPRA International Journal of Research and Development (IJRD), 8(11), 1-6.

[2]. Chu, C. W., & Zhang, G. P. (2003). A comparative study of linear and nonlinear models for aggregate retail sales forecasting. International Journal of Production Economics, 86(3), 217-231.

[3]. Cumby, C., Fano, A., Ghani, R., & Krema, M. (2004, August). Predicting customer shopping lists from point-of-sale purchase data. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 402-409).

[4]. Isaac, O. A., Adedeji, A. A., & Ismail, I. R. (2012). Polynomial regression model of making cost prediction in mixed cost analysis. International Journal on Mathematical Theory and Modeling, 2(2), 14-23.

[5]. Kuo, R. J., Hu, T. L., & Chen, Z. Y. (2009, February). Application of radial basis function neural network for sales forecasting. In 2009 International Asia Conference on Informatics in Control, Automation and Robotics (pp. 325-328). IEEE.

[6]. Niu, Y. (2020, October). Walmart sales forecasting using XGBoost algorithm and feature engineering. In 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) (pp. 458-461). IEEE.

[7]. Nunnari, G., & Nunnari, V. (2017, July). Forecasting monthly sales retail time series: A case study. In 2017 IEEE 19th Conference on Business Informatics (CBI) (Vol. 1, pp. 1-6). IEEE.

[8]. Odegua, R. (2020). Applied Machine Learning for Supermarket Sales Prediction. Project: Predictive Machine Learning in Industry.

[9]. Raizada, S., & Saini, J. R. (2021). Comparative analysis of supervised machine learning techniques for sales forecasting. International Journal of Advanced Computer Science and Applications, 12(11), 102-110.

[10]. Ranjitha, P., & Spandana, M. (2021, May). Predictive analysis for big mart sales using machine learning algorithms. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1416-1421). IEEE.

[11]. Silva, A. L., & Cardoso, M. G. (2005). Predicting supermarket sales: The use of regression trees. Journal of Targeting, Measurement and Analysis for Marketing, 13, 239-249.

[12]. Suma, V., & Hills, S. M. (2020). Data mining based prediction of demand in Indian market for refurbished electronics. Journal of Soft Computing Paradigm (JSCP), 2(02), 101-110.

[13]. Tsoumakas, G. (2019). A survey of machine learning techniques for food sales prediction. Artificial Intelligence Review, 52(1), 441-447.

[14]. Wang, H. (2019). Sustainable development and management in consumer electronics using soft computation. Journal of Soft Computing Paradigm (JSCP), 1(01), 56.

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Research Paper

Role of Artificial Intelligence in Investment Management

Mahiba G. B.* , Jesintha P.**

*-** Department of Commerce, Women's Christian College, Nagercoil, Tamil Nadu, India.

Mahiba, G. B., and Jesintha, P. (2024). Role of Artificial Intelligence in Investment Management. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 35-39.

Abstract

AI is transforming the field of investment management by enhancing decision-making, improving operational efficiency, and optimizing portfolio management. As the technology evolves, it is expected that AI will play an even more integral role in driving the future of investment management, offering both opportunities and challenges for traditional asset managers. This study aims to know the most preferred artificial intelligence investing apps in investing management and to study the awareness among investors in artificial intelligence investing management. For this study, 120 investors were selected from Kanniyakumari district using the convenience sampling method. Primary and secondary data were collected for this study, and SPSS tools were used to analyze the data. This study found that there is no significant difference between gender and the awareness among investors of AI in investing management. This study concludes that AI plays a transformative role in investment management by enhancing decision-making, optimizing portfolios, and enabling personalized strategies, ultimately driving more efficient and effective investment outcomes in an increasingly complex financial landscape.

References

[1]. Cao, Z. (2024). Towards promoting the efficiency of enterprise investment management with Artificial Intelligence. Highlights in Business, Economics and Management, 31, 57-64.

[2]. Lalitha, K. P. (2024). A study on the role of Artificial Intelligence in investment management. International Journal of Creative Research Thoughts(IJCRT), 12(1), 189-200.

[3]. Liu, R. (2023). Investment management of enterprise annuity. Journal of Innovation and Development, 5(3), 40-45.

[4]. R. Karthika, A. A. (2023). Artificial Intelligence in investment management, asset management and warehouse management. i-manager's Journal on Data Science & Big Data Analytics, 1 (1), 40-46.

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Research Paper

A Generative AI Model for Forest Fire Prediction and Detection

Nallusamy M.* , Karthick S., Vetrivel B.*

*-*** Roever Engineering College, Perambalur, Tamil Nadu, India.

Nallusamy, M., Karthick, S., and Vetrivel, B. (2024). A Generative AI Model for Forest Fire Prediction and Detection. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 40-47.

Abstract

Forest fires pose significant threats to forest ecosystems, impacting humans, animals, and plants reliant on these environments. Traditional detection methods rely on handcrafted features like color, motion, and texture, yet achieving accuracy remains challenging. This study introduces a novel approach using a lightweight fire detection method employing Deep Convolution Neural Networks (DCNN), considering temporal aspects for enhanced accuracy. By leveraging DCNN, this study aims to improve forest fire detection capabilities, mitigating the devastating effects of wildfires on both natural habitats and communities. This method represents a promising advancement in the field, offering potential solutions to the ongoing challenge of timely and accurate forest fire detection.

References

[1]. Bot, K., & Borges, J. G. (2022). A systematic review of applications of machine learning techniques for wildfire management decision support. Inventions, 7(1), 15.

[2]. Jadon, A., Varshney, A., & Ansari, M. S. (2020). Low-complexity high-performance deep learning model for real-time low-cost embedded fire detection systems. Procedia Computer Science, 171, 418-426.

[3]. Jiao, Z., Zhang, Y., Xin, J., Mu, L., Yi, Y., Liu, H., & Liu, D. (2019, July). A deep learning based forest fire detection approach using UAV and YOLOv3. In 2019 1st International Conference on Industrial Artificial Intelligence (IAI) (pp. 1-5). IEEE.

[4]. Kim, S. Y., & Muminov, A. (2023). Forest fire smoke detection based on deep learning approaches and unmanned aerial vehicle images. Sensors, 23(12), 5702.

[5]. Preeti, T., Kanakaraddi, S., Beelagi, A., Malagi, S., & Sudi, A. (2021, June). Forest fire prediction using machine learning techniques. In 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.

[6]. Ranjani, D., Haripriyabala, M., Indhu, J., Janani, V., & Jothi, A. (2023, April). Forest fire detection using Convolutional Neural Networks (CNN). In 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 852-857). IEEE.

[7]. Shamta, I., & Demir, B. E. (2024). Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV. Plos one, 19(3), e0299058.

[8]. Wahyono., Harjoko, A., Dharmawan, A., Adhinata, F. D., Kosala, G., & Jo, K. H. (2022). Real-time forest fire detection framework based on artificial intelligence using color probability model and motion feature analysis. Fire, 5(1), 23.

[9]. Yandouzi, M., Grari, M., Berrahal, M., Idrissi, I., Moussaoui, O., Azizi, M., & Elmiad, A. K. (2023). Investigation of combining deep learning object recognition with drones for forest fire detection and monitoring. International Journal of Advanced Computer Science and Applications (IJACSA), 14(3), 377-384.

[10]. Zhang, L., Wang, M., Fu, Y., & Ding, Y. (2022). A forest fire recognition method using UAV images based on transfer learning. Forests, 13(7), 975.

i-manager's Journal on Data Science & Big Data Analytics (JDS)

Current Issue Vol. 2 Issue 2

Most Read

Most Cited

Volume 2 Issue 2 July - December 2024

Breast Cancer Diagnosis Model Based on Convolutional Neural Networks' Multiple Architectures

Ayobami Ekundayo* , Enesi Femi Aminu**, Winifred Omone Saliu***, Oluwaseun Adeniyi Ojerinde****, Uchenna Cosmas Ugwuoke*****

*-**** Department of Computer Science, Federal University of Technology, Minna, Nigeria.

Ekundayo, A., Aminu, E. F., Saliu, W. O., Ojerinde, O. A., and Ugwuoke, U. C. (2024). Breast Cancer Diagnosis Model Based on Convolutional Neural Networks' Multiple Architectures. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 1-19.

Abstract

References

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Impact of Artificial Intelligence on Cyber Shopping in Kanniyakumari District

Goldie Sheila Jesolit C. S.* , Chithra James**

*-** Department of Commerce, Women's Christian College, Nagercoil, Tamil Nadu, India.

Jesolit, C. S. G. S., and James, C. (2024). Impact of Artificial Intelligence on Cyber Shopping in Kanniyakumari District. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 20-27.

Abstract

References

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An Interactive Visualization and Data Analysis of Supermarket Store

Likita H. L.* , Hiresh Yadav**, Anjali Hedaoo***, Swagota Bera****, Siddhartha Choubey*****

*-***** Shri Shankaracharya Engineering College, Bhilai, Chhattisgarh, India.

Likita, H. L., Yadav, H., Hedaoo, A., Bera, S., and Choubey, S. (2024). An Interactive Visualization and Data Analysis of Supermarket Store. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 28-34.

Abstract

References

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Role of Artificial Intelligence in Investment Management

Mahiba G. B.* , Jesintha P.**

*-** Department of Commerce, Women's Christian College, Nagercoil, Tamil Nadu, India.

Mahiba, G. B., and Jesintha, P. (2024). Role of Artificial Intelligence in Investment Management. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 35-39.

Abstract

References

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A Generative AI Model for Forest Fire Prediction and Detection

Nallusamy M.* , Karthick S.**, Vetrivel B.***

*-*** Roever Engineering College, Perambalur, Tamil Nadu, India.

Nallusamy, M., Karthick, S., and Vetrivel, B. (2024). A Generative AI Model for Forest Fire Prediction and Detection. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 40-47.

Abstract

References

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Ayobami Ekundayo* , Enesi Femi Aminu, Winifred Omone Saliu, Oluwaseun Adeniyi Ojerinde, Uchenna Cosmas Ugwuoke

Likita H. L.* , Hiresh Yadav, Anjali Hedaoo, Swagota Bera, Siddhartha Choubey

Nallusamy M.* , Karthick S., Vetrivel B.*