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: This paper proposes a deployment-oriented artificial intelligence framework for real-time counterfeit currency authentication and denomination classification using hyperspectral imaging. Building on spectral-spatial representations, the proposed system combines informative spectral-band selection, a lightweight three-dimensional...

Key Word: Hyperspectral imaging; counterfeit currency; CNN-transformer; vision transformer; multi-task learning; explainable AI; edge AI; real-time classification; spectral-spatial attention

[1]. Y. Chen, N. M. Nasrabadi, and T. D. Tran, “Hyperspectral image classification using deep belief networks,” IEEE Transactions on Geoscience and Remote Sensing, 2014.
[2]. A.Dosovitskiy et al., “An image is worth 16×16 words: Transformers for image recognition at scale,” ICLR, 2021.
[3]. J. Hu, L. Shen, and G. Sun, “Squeeze-and-Excitation Networks,” CVPR, 2018.
[4]. R. R. Selvaraju et al., “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” ICCV, 2017.
[5]. A.Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” NeurIPS, 2012..

Abstract: Background: Software Defect Prediction (SDP) is a proactive quality assurance strategy that uses static code complexity metrics to catch software bugs before a system is deployed. software metrics are highly imbalanced. Because clean code segments vastly outnumber defective ones, standard machine learning models tend to ignore the minority class, leading to a dangerous bias that leaves actual defects undetected. This study develops a reliable, end-to...

Key Word: Software Defect; Machine Learning; Hyperparameter Tuning; Ensemble Learning

[1]. A. Khalid, G. Badshah, N. Ayub, M. Shiraz, and M. Ghouse, “Software defect prediction analysis using machine learning techniques,” Sustainability, vol. 15, no. 6, p. 5517, 2023, Available: https://doi.org/10.3390/su15065517
[2]. I. Arora, V. Tetarwal, and A. Saha, “Open issues in software defect prediction,” Procedia Computer Science, vol. 46, pp. 906–912, 2015, Available: https://doi.org/10.1016/j.procs.2015.02.161
[3]. A. Daza, “Software defect prediction based on a multiclassifier with hyperparameters: Future work,” Results in Engineering, vol. 25, p. 104123, 2025, Available: https://doi.org/10.1016/j.rineng.2025.104123
[4]. K. Bashir, T. Li, and C. W. Yohannese, “An empirical study for enhanced software defect prediction using a learning-based framework,” International Journal of Computational Intelligence Systems, vol. 12, no. 1, pp. 282–298, 2018, Available: https://doi.org/10.2991/ijcis.2018.125905638
[5]. H. Wei, C. Hu, S. Chen, Y. Xue, and Q. Zhang, “Establishing a software defect prediction model via effective dimension reduction,” Information Sciences, vol. 477, pp. 399–409, 2019, Available: https://doi.org/10.1016/j.ins.2018.10.056.

Abstract: Production incidents in large-scale distributed commerce systems are expensive and inevitable: one hour of downtime is estimated to cost Amazon US$100 million on major shopping days (Ahmed et al., 2023), Amazon lost US$66,240 per minute of server downtime in early 2016, and the average US data center outage cost US$740,357 per event at roughly US$7,900 per minute (Nguyễn et al., 2018). Yet incident management remains dominated by manual toil: on-call engineers spend multiple rounds of back-and-forth communication to identify root causes and mitigation steps (Ahmed et al., 2023). This paper argues that large language models are the missing reasoning layer that converts incident management from a human-bottlenecked diagnostic process into an automated, governed.....

Key Word: Root cause analysis, incident management, large language models, AIOps, microservices, cloud computing, anomaly detection, automated incident response, observability, self-healing systems.

[1]. 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
[2]. Allam, H. (2024). Zero-Touch Reliability: The Next Generation of Self-Healing Systems. International Journal of Artificial Intelligence Data Science and Machine Learning, 5, 59–71. https://doi.org/10.63282/3050-9262.ijaidsml-v5i4p107
[3]. 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
[4]. Awad, A., & Alahmari, D. (2025). Agentic Control Towers. In Advances in computational intelligence and robotics book series (pp. 161–182). IGI Global. https://doi.org/10.4018/979-8-3373-7006-4.ch006
[5]. Babatope, O. M., Oyewole, T., Ogbole, J. I., & Okoruwa, P. O. (2023). Developing an AI-Based Incident Response Automation Framework to Minimize Downtime in IT Service Operations. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2406–2423. https://doi.org/10.62225/2583049x.2023.3.6.5415..

Abstract: Data quality serves as the bedrock of reliable decision-making in the modern digital economy, yet traditional manual cleaning methods are increasingly inadequate for the sheer volume and velocity of contemporary "Big Data." This research explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in automating......

[1]. Batini, C., Scannapieco, M. (2016). Data and Information Quality: Dimensions, Principles and Techniques. Springer.
[2]. Redman, T.C. (2018). Data Driven: Profiting from Your Most Important Business Asset. Harvard Business Review Press.
[3]. Kim, J., Choi, S., & Kim, H. (2020). “A Survey on Data Quality Management: AI Techniques for Data Cleansing and Integration.” Information Systems Frontiers, 22(3), 687–707.
[4]. Wang, R.Y., Strong, D.M. (1996). “Beyond Accuracy: What Data Quality Means to Data Consumers.” Journal of Management Information Systems, 12(4), 5–33.
[5]. Hu, W., Li, S., & Zhang, X. (2021). “Machine Learning Approaches for Improving Data Quality in Big Data Systems.” Journal of Big Data, 8(1), 102.

Abstract: Hate speech detection systems must do more than assign a harmful-content label. Moderators and affected communities need to understand why a prediction was produced, while attackers continually modify spelling, punctuation, and wording to evade detection. This paper proposes an explainable and robust multitask transformer......

Keywords Explainable AI; robust NLP; multitask transformer; rationale extraction; hate speech; adversarial text; multilingual learning

[1]. Mathew et al. (2021). HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection. AAAI.
[2]. Rehman et al. (2026). X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and a Novel LLM-Consulted Explanation Framework. AAAI.
[3]. Devlin et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
[4]. Silva et al. (2026). A Multitask Transformer for Offensive Language Detection and Target Identification in HateBR. PROPOR 2026.
[5]. Kapil and Ekbal (2025). A Transformer Based Multi Task Learning Approach to Multimodal Hate Speech Detection. Natural Language Processing Journal