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All Journal Emerging Science Journal
Singh, Satyanand
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Environmental Science

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Comparative Assessment of Machine Learning Approaches for Early Lung Cancer Diagnosis Maheshwari , Garvit; Tiwari, Babita; Tinka, Domonkos; Singh, Satyanand
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-02

Abstract

Lung cancer, a leading cause of cancer-related mortality worldwide, often escapes early detection due to the absence of distinct symptoms in its initial stages. This work investigates how Machine Learning (ML) might improve early diagnosis by analyzing Electronic Health Records (EHR) data. Multiple ML models were developed and evaluated on a synthetic dataset created to replicate real-world patient characteristics, allowing controlled experimentation while safeguarding privacy. Model performance was tuned using both conventional optimization methods and nature-inspired approaches, with the aim of balancing predictive accuracy and computational efficiency. In our synthetic dataset experiments, ensemble learners optimized with metaheuristic techniques reached accuracy levels approaching 99 percent while maintaining computational efficiency and generally outperformed simpler baselines. The contribution of this work lies in exploring the integration of GFO and WOA for feature selection and hyperparameter tuning of XGBoost, together with a soft-voting ensemble. This approach provides an experimental pathway for enhancing predictive performance under computational constraints. However, as the dataset is synthetic, the conclusion remains experimental; validation against clinical records will be essential before translation into practice.
Artificial Intelligence and Business Process Management: A Responsible Framework for Sustainable Transformation Sarkambayeva , Shynara; Singh, Satyanand; Mukhanova , Gulmira; Amralinova, Bakytzhan; Turegeldinova , Aliya
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-022

Abstract

This study aims to develop a responsible and sustainable framework for implementing artificial intelligence (AI) in business process management (BPM), with a focus on aligning technological advancement with strategic economic transformation. It addresses the need for ethical, sector-sensitive AI adoption in emerging economies undergoing digital modernization and diversification. The research integrates enterprise information system considerations, privacy-preserving modular architectures, and national regulatory frameworks related to data localization and cybersecurity. A sectoral analysis is conducted to assess global AI adoption maturity and its implications for economic transformation, using Kazakhstan as a contextual reference point. The results reveal that consumer-facing sectors such as retail and financial services exhibit high near-term adoption potential, while healthcare requires gradual infrastructure and talent development. More significantly, mid-term opportunities in manufacturing, logistics, and transportation sectors present Kazakhstan with a comparative advantage. AI adoption in manufacturing is projected to grow by 83% within three to seven years, underscoring the importance of timely investments in automation, smart technologies, and workforce upskilling. This study contributes a context-aware framework for responsible AI-enabled BPM. It offers actionable insights for policymakers and business leaders in emerging economies, advocating for sectoral prioritization, strategic timing, and capacity-building to ensure sustainable digital transformation.
Minimal Redundancy Linear Array and Uniform Linear Arrays Beamforming Applications in 5G Smart Devices Singh, Satyanand
Emerging Science Journal Vol. 4 (2020): Special Issue "IoT, IoV, and Blockchain" (2020-2021)
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2021-SP1-05

Abstract

Minimum Redundancy Linear Arrays (MRLAs) and Uniform Linear Arrays (ULAs) investigation conducted with the possibility of using them in future 5G smart devices. MRLAs are designed to minimize the number of sensor pairs with the same spatially correlated delay. It eliminates selected antennas from the entire composite antenna array and preserves all possible antenna spacing.  MRLAs have attractive features for linear sparse arrays, even if the built-in surface is deformed, it works without problems. To our knowledge, MRLAs have not been applied to smart devices so far. In this work, a 7-element ULAs and 4-element MRLAs (same aperture) were used for the simulation. The Half Power Beamwidth (HPBW) is 0.666 and the Null-to-Null Beamwidth ( ) is 1.385 in ψ-space. In comparison, the standard 4-element arrays are 1.429 and 3.1416, while the standard 7-element linear arrays are 0.801 and 1.795 respectively. Experimental results show that 4-element MLRAs have a narrower mean beam, much higher sidelobes and shallow nulls. Therefore, in terms of main lobe features, 4- elements MRLAs have an improvement over the standard 7-element ULAs. Doi: 10.28991/esj-2021-SP1-05 Full Text: PDF
Environmental Energy Harvesting Techniques to Power Standalone IoT-Equipped Sensor and Its Application in 5G Communication Singh, Satyanand
Emerging Science Journal Vol. 4 (2020): Special Issue "IoT, IoV, and Blockchain" (2020-2021)
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2021-SP1-08

Abstract

In the recent few years, due to its significant deployment to meet global demand for smart cities, the Internet of Things (IoT) has gained a lot of attention. Environment energy harvesting devices, which use ambient energy to generate electricity, could be a viable option in near future for charging or powering stand-alone IoT sensors and electronic devices. The key advantages of such energy harvesting gadgets are that they are environmentally friendly, portable, wireless, cost-effective, and compact. It is significant to propos and fabricate an improved, high-quality, economical, and efficient energy harvesting systems to overcome power supply to tiny IoT devices at the remote locations. In this article, various types of mechanisms for harvesting renewable energies that can power sensor enabled IoT locally, as well as its associated wireless sensor networks (WSNs), are reviewed. These methods are discussed in terms of their advantages and applications, as well as their drawbacks and limitations. Furthermore, methodological performance analysis for the decade 2005 to 2020 is surveyed in order to identify the methods that delivered high output power for each device. Furthermore, the outstanding breakthrough performances of each of the aforementioned micro-power generators during this time period are emphasized. According to the research, thermoelectric modules can convert up to 2500í—10^(-3) W/cm^2, thermo-photovoltaic 10.9%, piezoelectric 10,000 mW/cm^3 and microbial fuel cell 6.86 W/m^2 of energy. Doi: 10.28991/esj-2021-SP1-08 Full Text: PDF
Co-Authors Amralinova, Bakytzhan Maheshwari , Garvit Mukhanova , Gulmira Sarkambayeva , Shynara Tinka, Domonkos Tiwari, Babita Turegeldinova , Aliya