iosr-JCE  Volume-28 ~ Issue-5 ~ Sep. - Oct. 2026

Abstract: Background: Cluster analysis of high dimensional data poses various challenges including problems associated with the curse of dimensionality, presence of irrelevant attributes, and high computational costs. Conventional GMM based techniques do not perform well with data having thousands of attributes and small number of observations, e.g., Coil20, GLIOMA, and Lung datasets.
Materials and.....

Key Word: Data clustering, Ensemble clustering, GMM, Adaptive subspace generation, Reliability-aware learning, Cross-partition consensus, Random sampling

[1]. A. Saxena, M. Prasad, A. Gupta, et al., “A review of clustering techniques and developments,” Neurocomputing, vol. 267, pp. 664–681, 2017.
[2]. M. Mittal, L. M. Goyal, D. J. Hemanth, et al., “Clustering approaches for high-dimensional databases: A review,” WIREs Data Mining and Knowledge Discovery, vol. 9, no. 3, article no. e1300, 2019.
[3]. G. T. Reddy, M. P. K. Reddy, K. Lakshmanna, et al., “Analysis of dimensionality reduction techniques on big data,” IEEE Access, vol. 8, pp. 54776–54788, 2020.[4].
D. Wang, X. Y. Guo, S. Li, et al., “Robust high dimensional expectation maximization algorithm via trimmed hard thresholding,” Machine Learning, vol. 109, no. 12, pp. 2283–2311, 2020.
[5]. J. L. Liu, D. Cai, and X. F. He, “Gaussian mixture model with local consistency,” in Proceedings of the 24th AAAI Conference on Artificial Intelligence, Atlanta, GA, USA, pp. 512–517, 2010.

Abstract: Data Science and Marketing Analytics has become an important area for organizations that want to make evidence-based business decisions. Modern organizations collect large volumes of customer, transaction, web, social-media and campaign data. Data science methods for cleaning, integrating, analyzing and modelling these data, while marketing analytics converts analytical findings into practical decision related to customer acquisition, retention....

Key Word: data science, marketing analytics, machine learning, customer segmentation, predictive analytics, customer lifetime value, business intelligence, marketing optimization, artificial intelligence.

[1]. Berman, R. (2018) Beyond the last touch Attribution in online advertising. Marketing Science.
[2]. Bruce, P, Bruce, A., & P. (2020). Practical statistics for data scientists: 50+ essential concepts using R and Python (2nd ed.). O'Reilly Media.
[3]. Davenport, T. H & Harris, J. G. (2017). Competing on analytics: The new science of winning (Updated ed.). Harvard Business Review Press.[4].
Han, J. Kamber, M. & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.
[5]. James, G, Witten, D., Hastie, T., & R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer

Abstract: Communication is a core skill for a successful leader. Effective communication includes Speaking, Writing, listening and matching words and actions. Communication is the part of our life to provide a specific character and which is to perform an accurate speaking and well perform our life style. Communication is a controller of our language to connect with another person to how to speak and body language. technology such as emails, presentations, and text messages, used judiciously to augment personal communications. active listening skills (the most important...

Key Word:○
Gesture
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Intercultural communication
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Interpersonal communication
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Man-woman relationships
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Nonverbal communication
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Body language

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Abstract: The objective of this paper is to design a breast cancer detection model using hybrid deep learning. The design model encapsulates pre-processing, feature extraction, and classification. Convolutional neural networks (CNN) and recurrent neural networks (RNN) are the names of the two deep learning architectures. Furthermore, the tumour-segmented binary image is regarded as input to CNN, and both GLCM and GLRM are regarded as input to RNN. It is demonstrated that the AND operation of two classifier outputs will produce diagnostic accuracy that is superior to that....

Key Word: BCD, Deep Learning, LSTM, CNN, Feature Extraction MIAS

[1]. Gherghout, Youcef, Yamina Tlili, and Labiba Souici. "Classification of breast mass in mammography using anisotropic diffusion filter by selecting and aggregating morphological and textural features." Evolving Systems 12 (2021): 273-302.
[2]. Senousy, Zakaria, Mohammed M. Abdelsamea, Mona Mostafa Mohamed, and Mohamed Medhat Gaber. "3E-Net: Entropy-based elastic ensemble of deep convolutional neural networks for grading of invasive breast carcinoma histopathological microscopic images." Entropy 23, no. 5 (2021): 620.
[3]. Teuwen, Jonas, Nikita Moriakov, Christian Fedon, Marco Caballo, Ingrid Reiser, Pedrag Bakic, Eloy García, Oliver Diaz, Koen Michielsen, and Ioannis Sechopoulos. "Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation." Medical image analysis 71 (2021): 102061.
[4]. Nyemeesha, V. "A systematic study and approach on detection of classification of skin cancer using back propagated artificial neural networks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 1737-1748..

Abstract: The computing substrate that runs critical supply chains — checkout pipelines, order orchestration, inventory services, payment gateways, logistics routing — fails in ways that are expensive, fast, and hard to explain: one hour of downtime is estimated to cost $100 million on major shopping days at Amazon, and supply-chain-level disruptions average $184 million in cost per event with traditional response consuming 3–5 days (Ahmed et al., 2023; Yan et al., 2025). This paper discloses a system and associated methods for closing the gap between failure onset and recovery: predictive failure intelligence over a continuously updated service dependency graph; blast...

Key Word: Predictive failure intelligence, self-healing systems, root cause analysis, large language models, service dependency graphs, blast radius, autonomous remediation, rollback, recovery verification, AIOps, distributed systems, supply chain infrastructure, governance

[1]. Aboutorab, H., Hussain, O. K., Saberi, M., Hussain, F. K., & Prior, D. D. (2024). Adaptive identification of supply chain disruptions through reinforcement learning. Expert Systems with Applications, 248, 123477. https://doi.org/10.1016/j.eswa.2024.123477
[2]. Ahmed, T., Ghosh, S., Bansal, C., Zimmermann, T., Zhang, X., & Rajmohan, S. (2023). Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models. 1737–1749. https://doi.org/10.1109/icse48619.2023.00149
[3]. Akhtar, P., Ghouri, A. M., Khan, H. U. R., Haq, M. A., Awan, U., Zahoor, N., Khan, Z., & Ashraf, A. (2022). Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions. Annals of Operations Research, 327(2), 633–657. https://doi.org/10.1007/s10479-022-05015-5
[4]. Akubilla, J., Somoye, O. I., Abiodun, F., & Serifat, O. A. (2025). The Role of Explainable AI in Enhancing Trust and Transparency in Supply Chain Risk Mitigation. International Journal of Multidisciplinary Research and Growth Evaluation, 6(3), 367–377. https://doi.org/10.54660/.ijmrge.2025.6.3.367-377
[5]. AlMahri, S., Xu, L., & Brintrup, A. (2026). Automating Supply Chain Disruption Monitoring via an Agentic AI Approach. ArXiv.Org. http://arxiv.org/abs/2601.09680.