Category Archives: Uncategorized

Authors: Muskan Gupta

Abstract: This article looks at the idea of the Right to Disconnect, which means employees should not be expected to work or respond to messages outside their official working hours. It focuses on workers in India’s IT and BPM sectors. Using a mix of data from Indian and international labour surveys and a survey of 300 professionals in five major cities, the study examines how Indian labour laws currently do not protect workers from after-hours digital work, and how this affects their health and well-being. Key findings show that 68% of IT workers regularly get work messages after hours, and 42% show signs of burnout. The article also looks at how countries like France, Portugal, Belgium, and Spain handle this issue to suggest solutions for India. The study concludes that while a single national law might be hard to implement, a tiered approach — tailored to different sectors and included within India’s Occupational Safety, Health and Working Conditions Code — could protect employees without hurting business operations. Overall, the article highlights the importance of protecting workers’ digital rights as India’s work environment evolves.

Published by: vikaspatanker

Authors: A. M. Garba, M. Abubakar, K. J. Osinubi, O. Eberemu, T. S. Ijimdiya

Abstract: The potential of biotreated Aeolian soil using Bacillus brevis (B. brevis)-induced calcite precipitation for mitigating wind erosion was investigated through the application of the Bacterial Foraging Optimization Algorithm (BFOA). Laboratory experiments were conducted to generate the data required for the optimization analysis. Sediment flux (y) was considered the dependent variable, while B. brevis suspension density, pH, compactive effort, water content relative to optimum, and cementation conditions were considered independent variables influencing the wind-erodibility response of the treated soil. GeneXproTools 5.0 was employed to develop the fitness (objective) function describing the relationship between sediment flux and the selected independent variables. The resulting fitness function was subsequently incorporated into BFO codes developed in MATLAB 2016 to determine the combination of variables associated with minimum sediment flux. The optimization results demonstrated a strong relationship between the predicted and experimentally measured sediment-flux values, with a coefficient of determination (R²) of 0.912. This indicates that the developed optimization model provided a satisfactory representation of the experimentally observed wind-erodibility behaviour of the biotreated Aeolian soil. The optimization process further showed that sediment flux was strongly influenced by the selected independent variables. The minimum predicted sediment flux of approximately 3.91 × 10⁻³ kg/m²s was obtained after 50 iterations. Based on the optimization results, treatment of Aeolian soil with B. brevis suspension and 0.75 M cementation reagent demonstrated considerable potential for reducing wind-induced sediment transport. The findings indicate that BFOA can serve as an effective optimization tool for identifying suitable microbial treatment conditions for wind-erosion mitigation, particularly in arid and semi-arid environments.

DOI: https://doi.org/10.5281/zenodo.22042808

Published by: vikaspatanker

Authors: Anjali Savner, Assistant Professor Kapil Shah, Professor Kamlesh Patidar

Abstract: This paper presents the design and implementation of an Internet of Things (IoT)-based smart office automation system developed to improve energy efficiency, security, comfort, and real-time office monitoring. The proposed system integrates an ESP32 controller with IR, LDR, DHT11, MQ-2 and R307 fingerprint sensors, together with relay modules, an L298N motor driver, a DC motor, a 16×2 LCD and a P10 LED display. Occupancy information is used to automate lights and fans, while environmental information supports temperature, humidity, light and air-quality monitoring. Biometric authentication is used to update office availability, which is displayed outside the office. ESP-NOW/Wi-Fi communication supports wireless exchange of status information between controller and display modules. The supplied project report documents successful hardware and integration testing, including motion detection, environmental sensing, biometric authentication, appliance control, curtain operation, sensor integration and failure handling. The system provides a practical prototype for smart-office and future smart-campus applications.

DOI: https://doi.org/10.5281/zenodo.22042149

Published by: vikaspatanker

Authors: Manju Rathore

Abstract: Effective waste management practices are critical for mitigating environmental impact and promoting sustainability. This paper explores the role of biodegradable wastes in addressing these challenges and presents comparative data on the reduction of air pollutants associated with various waste materials. The analysis reveals that biodegradable wastes can significantly lower emissions compared to traditional materials. Specifically, glass waste results in 18% to 30% less air pollution, while plastic waste can reduce emissions by up to 66%. In contrast, iron cans contribute to 70% to 86% less air pollution, and aluminum cans are associated with a dramatic 95% reduction in air pollutants. These findings underscore the environmental benefits of transitioning towards more sustainable waste management practices and highlight the potential for biodegradable and recyclable materials to play a crucial role in minimizing air pollution and enhancing ecological sustainability.

DOI: https://doi.org/10.5281/zenodo.22026478

Published by: vikaspatanker

Authors: Ms. Gauri J. Sutar, Associate Professor Dr. Sarita V. Balshetwar, Assistant Professor Mrs. Rajani M. Mandhare

Abstract: This paper presents a low-cost pediatric wearable wristband for continuous fever-risk monitoring using temperature, heart-rate, and motion sensing. The device integrates an ESP32 controller, a DS18B20 temperature sensor, a pulse sensor, an MPU6050 accelerometer, a GSM module, a local buzzer/LED alert, and a Blynk cloud dashboard. Because the wrist skin temperature differs from core body temperature, the temperature channel is calibrated against a clinical digital thermometer and reported as a calibrated body-equivalent temperature. A lightweight, transparent Fever Risk Score (FRS) combines physiological and motion parameters, and a supervised machine-learning classifier (Random Forest) trained on a labeled dataset of 2,400 pediatric monitoring instances classifies the child’s condition into Normal, Warning, and High-Risk states. On a held-out test set the classifier achieved 91.4% accuracy, 94.5% sensitivity, and 98.1% specificity for High-Risk detection (macro F1 = 0.90). Bench validation gave a mean absolute temperature error of 0.18 °C against a clinical thermometer and 2.45 bpm against a pulse oximeter, with 95.5% fall-detection accuracy. Dual-channel alerting delivered GSM SMS in 6.5 ± 1.2 s and Blynk cloud updates in 1.9 ± 0.6 s, and the prototype operated continuously for 18.6 h on a 3.7 V Li-ion battery. The results indicate a validated, deployable early-warning and caregiver-alerting tool suitable for homes, schools, and rural healthcare settings, positioned as decision support rather than a replacement for clinical diagnosis.

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Authors: Assistant Professor Vinay.M, Assistant Professor Raghuvarancheerla

Abstract: The blockchain technology has emerged as one such revolutionary solution to solving some major problems in the field of supply chain management that include information asymmetry, non-traceability, frauds and inefficiencies. In this paper, a novel methodology for managing the supply chain through the use of blockchain technology and improved consensus mechanism is proposed. The proposed methodology incorporates the use of permissioned blockchain along with an improved consensus algorithm known as the Delegated Proof of Stake (DPOS) which uses the concept of reputation-based voting and incentives. The performance improvements of the proposed system have been validated through the agent-based simulations of a four-tier supply chain model and are observed to be as high as 30.8% in service level improvement, 23.7% in cost saving and 30.8% in inventory reduction.

DOI: https://doi.org/10.5281/zenodo.22012991

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Authors: Assistant Professor Subhadip Sarkar, Assistant Professor Ms. Preeta Rajiv Sivaraman

Abstract: Employee turnover is a challenging situation for any organization because of high associated costs related to recruitment and training of new employees as well as losing organizational knowledge. This research aims to examine how machine learning can be used in predictive analytics in managing employee turnover through the use of the IBM HR Analytics data set. Five models including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting were tested systematically. It was revealed that ensemble models such as Random Forest have the highest predictive accuracy and AUC equal to 87.3% and 0.9348, respectively. Overtime, job satisfaction, monthly income, and tenure are found to be important factors affecting employee turnover. These results show that machine learning may change HR practices from reactive to proactive retention strategies based on data analysis.

DOI: https://doi.org/10.5281/zenodo.22012826

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Authors: Assistant Professor M Sravan Kumar Babu, Assistant Professor Devanshi Hemal Shah

Abstract: Robotic Process Automation (RPA) in combination with Artificial Intelligence (AI) has become a revolutionizing paradigm for enterprise automation that allows for surpassing the boundaries of traditional automation based on rules. In this paper, an advanced framework for Intelligent Process Automation (IPA) is proposed, combining deterministic nature of RPA with the cognitive capabilities of AI, which makes it possible to automate not only structured but unstructured business processes as well. Hybrid approach is designed to use natural language processing, optical character recognition, and machine learning models to deal with difficult decisions. Empirical study shows impressive increases in efficiency, as the time of task completion is reduced by 33%, and errors occur 65% less often. Comparative analysis with conventional RPA shows IPA to be a better solution in many aspects of performance. The results add to the literature on intelligent automation and have practical implications for organizations.

DOI: https://doi.org/10.5281/zenodo.22012612

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Authors: Bilal Ibrahim Maijamaa, Salim Ahmad, Zaharaddeen Salele Iro, Aminu Aliyu Abdullahi

Abstract: Missing values are a common challenge in real-world datasets, often reducing the accuracy and reliability of machine learning models. Although numerous machine learning and ensemble-based imputation techniques have been proposed, many rely on single-level stacking architectures and do not explicitly model both linear and nonlinear relationships during prediction. This study proposes a Particle Swarm Optimization (PSO)-optimized two-level stacking ensemble for numerical data imputation. The framework employs PSO for base-learner selection and hyperparameter optimization; the selected base learners are Random Forest and XGBoost. A two-metalearner architecture is then used in which Linear Regression captures linear dependencies and Random Forest models nonlinear interactions to generate the final imputed values. The proposed framework was evaluated on the Breast Cancer Wisconsin and Wine Quality datasets under Missing Completely at Random (MCAR) mechanisms at 30%, 20%, and 10% missingness. Performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R²), and processing time. Experimental results demonstrate that the proposed model consistently outperformed the standalone Random Forest and XGBoost models across all missingness levels. On the Breast Cancer dataset, the proposed model achieved RMSE values of 0.0747, 0.0627, and 0.0529 with corresponding R² values of 67.66%, 70.51%, and 74.63% at 30%, 20%, and 10% MCAR, respectively. Similarly, on the Wine Quality dataset, it recorded RMSE values of 0.1873, 0.1751, and 0.1681 with corresponding R² values of 49.54%, 52.15%, and 54.64%. Furthermore, the proposed approach outperformed a recently reported hybrid imputation method, achieving substantially lower prediction errors while maintaining acceptable computational cost. These findings demonstrate that integrating PSO optimization with a two-level stacking ensemble provides an accurate, robust, and scalable framework for numerical missing value imputation across datasets with varying degrees of missingness.

DOI: https://doi.org/10.5281/zenodo.22009422

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Authors: Paul ‘Seun Sosina, Odebiyi Olusola James

Abstract: Artificial intelligence is transforming national security governance by strengthening intelligence gathering, threat detection, border surveillance, cybersecurity, crime prevention and conflict early warning. However, its deployment across Africa raises concerns about privacy, algorithmic bias, accountability, misinformation, institutional readiness and technological dependence. This study examined artificial intelligence and national security governance in Africa through a comparative analysis of Nigeria, South Africa, Kenya, Rwanda and Egypt. Anchored in Technology Domestication Theory, the study adopted a qualitative multiple-case design. Data were obtained through documentary analysis of peer-reviewed studies, national policies, legislative frameworks, security strategies and African Union policy documents. The data were analysed using thematic synthesis and comparative policy analysis. Findings revealed that AI improves data processing, threat identification, surveillance capability and proactive security decision-making, although the scale of adoption varies across the five countries. Egypt and Rwanda demonstrate centralized policy arrangements, while Kenya and South Africa possess digital ecosystems but face implementation and regulatory challenges. Nigeria’s adoption is constrained by infrastructure deficiencies, institutional responsibilities and technical capacity. Shared challenges include inadequate oversight, poor data quality, skills shortages, algorithmic bias, privacy risks and dependence on foreign technologies. The study concludes that effective AI-supported security requires context-sensitive governance grounded in human rights, democratic accountability, strategic autonomy and human oversight. It recommends risk-based regulation, oversight, impact assessments, indigenous capacity development, stronger data protection and harmonized standards.

DOI: https://doi.org/10.5281/zenodo.21993615

Published by: vikaspatanker