Authors: Mazlan Othman

Abstract: Machine learning (ML) has emerged as a powerful approach for enhancing network security by enabling intelligent detection, prevention, and response to cyber threats. With the increasing complexity and scale of modern networks, traditional rule-based security systems are often insufficient to identify sophisticated attacks such as zero-day exploits, phishing, and advanced persistent threats (APTs). This paper explores the application of machine learning techniques in network security, focusing on how supervised, unsupervised, and reinforcement learning models can analyze network traffic patterns to detect anomalies and malicious activities. It also examines the role of ML in intrusion detection systems (IDS), intrusion prevention systems (IPS), malware detection, and behavioral analysis. Cloud-based and real-time security monitoring systems are discussed as key enablers for scalable ML deployment in distributed network environments. Additionally, the study highlights challenges such as adversarial attacks, data imbalance, privacy concerns, and model interpretability. Emerging solutions including federated learning, explainable AI, and edge-based security analytics are also reviewed. The findings emphasize that machine learning significantly strengthens network security frameworks by enabling proactive, adaptive, and intelligent threat detection mechanisms.

DOI: https://doi.org/10.5281/zenodo.20281072

Published by: vikaspatanker

Authors: Mazlan Othman

Abstract: Machine learning (ML) has emerged as a powerful approach for enhancing network security by enabling intelligent detection, prevention, and response to cyber threats. With the increasing complexity and scale of modern networks, traditional rule-based security systems are often insufficient to identify sophisticated attacks such as zero-day exploits, phishing, and advanced persistent threats (APTs). This paper explores the application of machine learning techniques in network security, focusing on how supervised, unsupervised, and reinforcement learning models can analyze network traffic patterns to detect anomalies and malicious activities. It also examines the role of ML in intrusion detection systems (IDS), intrusion prevention systems (IPS), malware detection, and behavioral analysis. Cloud-based and real-time security monitoring systems are discussed as key enablers for scalable ML deployment in distributed network environments. Additionally, the study highlights challenges such as adversarial attacks, data imbalance, privacy concerns, and model interpretability. Emerging solutions including federated learning, explainable AI, and edge-based security analytics are also reviewed. The findings emphasize that machine learning significantly strengthens network security frameworks by enabling proactive, adaptive, and intelligent threat detection mechanisms.

DOI: https://doi.org/10.5281/zenodo.20281072

Published by: vikaspatanker

Authors: Myana Ramesh, Kanchapogu Prasanth, Mr. T. Srinivas

Abstract: Every PKL match across multiple seasons outcomes, dates, venues, scores, teams in one place. That's what this dataset is. What you can actually do with it is more interesting than the description suggests. Win/loss trends show which teams hold up across a full season and which ones are inconsistent. Scoring patterns reveal whether a team plays the same way regardless of opponent or adjusts. Venue data is underrated — some teams genuinely perform differently away from home, and the numbers show it. Zoom out across seasons and the league's own growth becomes visible too. More cities, more matches, more structure. PKL didn't stay the same sport it was in its first season, and this data captures that shift better than any summary could.

Published by: vikaspatanker

Authors: Prof .P.V. Nimbalkar, S. D. Magar, P. S. Nimbalkar, N. D. Chormal

Abstract: This paper presents the design and implementation of a Smart Vending Machine System using Internet of Things (IoT) technology for automated dispensing of ready-made food items. The main objective of the proposed system is to provide a contactless, efficient, and user-friendly vending solution that reduces human intervention and waiting time. The system is built using an Arduino UNO microcontroller integrated with a Wi-Fi module to enable real-time monitoring and control. A QR code–based cashless payment mechanism is incorporated to enhance convenience and security. Once the payment is successfully verified, the controller activates the dispensing mechanism through a motor driver to deliver the selected food item automatically. The developed prototype was tested under different operating conditions and demonstrated reliable performance with accurate item delivery and quick response time. The proposed IoT-based vending machine system is cost-effective, scalable, and suitable for deployment in public places such as colleges, offices, and railway stations. Future enhancements can include mobile application integration and advanced inventory management for improved automation.

DOI: http://doi.org/10.5281/zenodo.20279899

Published by: vikaspatanker

Authors: Anshu Kumar Mishra, Sohail Verma

Abstract: This paper investigates the relationship between leadership styles and team performance in startup organisations, using survey-based data collected from 120 respondents comprising founders, co-founders, team leads and early-stage employees across multiple sectors. The study identifies transformational leadership as the dominant style in the sample and finds strong positive associations between vision-driven leadership, team trust, communication frequency and performance outcomes. Transactional leadership shows moderate relevance in goal-setting and accountability, while laissez-faire approaches correlate with lower performance consistency. Exploratory chi-square testing reveals significant concentration in leadership style distribution, a meaningful link between startup stage and performance rating, and a strong association between trust levels and team output. The paper concludes that startup performance is not driven by a single leadership template but by the leader's ability to adapt style to team maturity, organisational stage and the demands of rapid growth. A hybrid leadership model combining transformational inspiration with transactional clarity emerges as the most effective pattern for high-performing startup teams.

Published by: vikaspatanker

Authors: Shah Md. Tanzimul Kabir, Zahid Hassan Ome

Abstract: This paper provides a comprehensive analysis of scalable database systems, specifically designed to support big data analytics, and examines their evolution, challenges, and emerging technologies in the exascale data processing era. By examining recent research studies from 2021 to 2026, the current paper seeks to investigate how distributed database architectures, including NewSQL, cloud-native, and data lakehouse, address the fundamental scalability challenge known as the "scalability trilemma" consisting of consistency, availability, and partition tolerance. The current research introduces the Adaptive Scalability Evaluation Framework (ASEF), which integrates horizontal scaling, elastic resources, query optimization, and storage efficiency. The analysis shows that recent scalable database architectures are based on disaggregated storage and compute architectures, enabling near-linear scaling to thousands of nodes with query latencies under 100ms for petabyte-scale data sets. Cloud-native database architectures are shown to be highly elastic, with variations in query latency at the 95th percentile below 15% during scaling events. Newly emerging architectures for lakehouses, which bring the flexibility of data lakes and the performance of data warehouses, provide query performance that is 3 to 5 times better than traditional data lakes and reduce the total cost of ownership by 30 to 50 percent. Evaluation in five dimensions for analytical workloads, such as scaling behavior, consistency model, query performance, storage efficiency, and operational complexity, shows that systems with workload awareness and adaptivity perform much better than static configurations. Continuous optimization provides an improvement in throughput performance that is between 2 to 4 times.

DOI: https://doi.org/10.5281/zenodo.20270564

Published by: vikaspatanker

Authors: Sk Samsul Hoda, Dr. Vipin kumar

Abstract: Remote sensing techniques used for measurement of atmospheric cloud properties operate under the notion that light extinction caused by scattering and absorption is exponential due to Beer-Lambert law. This is expected to be valid for a uni-form medium with no spatial correlations between particle position. The aim of this research was to show that under turbulent conditions, cloud droplets cannot be inter-preted as non-correlated, and in turn will exhibit a lower than exponential light decay from scattering. The research took place at the MTU π-Chamber laboratory. A tem-perature difference between the floor and ceiling of the chamber was applied to create convection- driven turbulence. When turbulent cloud conditions were achieved, it’s optical depth properties was analyzed. This was done by deriving the optical depth by computational means through the acquisition of its droplet size distribution, and processing it through Mie scattering theory, while simultaneously acquiring direct measurement of optical depth using a Laser-Hygrometer. Results showed that there is a trend where larger temperature differences inside the chamber caused the direct extinction of light to deviate more strongly from the computed extinction. This less then exponential extinction parameter allows us to understand the significant effect that a turbulent cloud cover has on radar and satellite signals.

DOI: http://doi.org/10.5281/zenodo.20269814

Published by: vikaspatanker

Authors: Abdullahi Idris, Aminu A. Abdullahi, Jamilu Awwalu, Abdullahi Uwaisu Muhammad

Abstract: The increasing availability of Electronic Health Records (EHRs), ICU monitoring systems and clinical sensor technologies has generated large volumes of temporal healthcare data that require advanced analytical approaches for effective interpretation and prediction. Traditional machine learning and statistical models often face challenges in handling complex temporal dependencies, irregular sampling, missing values and censored survival outcomes in clinical time-series data. This study employed a Hybrid Transformer Framework for Temporal Representation and Longitudinal Risk Prediction in Clinical Time Series synthesizing the relevant studies and clinical decision-making. The framework integrates the Transformer-LSTM architecture with Cox Proportional Hazards (Cox PH), Survival Random Forest (SRF) and XGBoost algorithms. The Transformer component captures long-range temporal dependencies using self-attention mechanisms, while the LSTM network models short-term sequential clinical patterns. Cox PH is applied for interpretable survival analysis, SRF for nonlinear ensemble survival prediction and XGBoost for high-performance risk classification and prediction. The review study utilizes healthcare datasets such as MIMIC-III, MIMIC-IV, elCU and PhysioNet as well as providing suitable comparative approaches against baseline models.

DOI: https://doi.org/10.5281/zenodo.20269570

Published by: vikaspatanker

Authors: Shivanshi Verma

Abstract: This study evaluates leachate quality from a municipal solid waste disposal site in Lucknow and examines its impact on nearby groundwater. The analytical framework, sampling design and index-based interpretation were prepared in line with the uploaded thesis and sample journal paper, while the numerical results were derived from the uploaded laboratory workbook. One leachate sample and seven groundwater samples were assessed for physicochemical and heavy metal parameters using APHA-based methods. The leachate showed acidic to near-neutral reaction (pH 6.1), very high electrical conductivity (83,892 µS/cm), total dissolved solids (38,180 mg/L), chemical oxygen demand (16,800 mg/L), biochemical oxygen demand (2,000 mg/L), hardness (1,620 mg/L), chloride (980 mg/L), sulphate (678.5 mg/L), nitrate (103.44 mg/L), fluoride (8.8 mg/L) and substantial heavy metal burden, indicating strong contaminant potential. The Leachate Pollution Index was 25, confirming significant pollution load. Groundwater quality varied spatially: Sample-7 recorded a WQI of 75.13 and fell in the good category, whereas Samples 2–4 were poor and Samples 1, 5 and 6 were very poor. Elevated TDS, alkalinity, hardness, iron, manganese, nickel, copper and zinc were the major causes of groundwater deterioration. The data indicate that leachate migration has affected groundwater quality in the vicinity of the disposal site, although the effect is not controlled by distance alone. The study recommends leachate containment, regular groundwater surveillance, and priority treatment for metal and salinity-related contamination.

DOI: https://doi.org/10.5281/zenodo.20269275

Published by: vikaspatanker

Authors: Zaira Siddiqui

Abstract: Groundwater is an essential source of drinking water in urban regions; however, rapid urbanization, industrial growth, and anthropogenic activities have significantly deteriorated groundwater quality in many Indian cities, including Lucknow. The present study aims to evaluate the spatial variability of groundwater quality in Lucknow district using Geographic Information System (GIS)-based techniques and Water Quality Index (WQI) approaches. Major physicochemical parameters including pH, electrical conductivity (EC), total hardness, calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), fluoride (F⁻), nitrate (NO₃⁻), and sulphate (SO₄²⁻) were analyzed for groundwater quality assessment. Spatial interpolation of groundwater parameters was performed using the Inverse Distance Weighting (IDW) method in GIS to generate thematic distribution maps and identify contamination hotspots. Two groundwater quality assessment approaches, namely Arithmetic Water Quality Index (AWQI) and Weighted Water Quality Index (WWQI), were applied to evaluate overall groundwater suitability for drinking purposes. The results revealed significant spatial variability in groundwater quality across Lucknow district. Elevated concentrations of hardness, EC, nitrate, chloride, and sulphate were observed in several urbanized and densely populated regions, indicating strong anthropogenic influence on groundwater systems. The AWQI and WWQI hotspot maps indicated that eastern and southeastern parts of Lucknow district exhibited comparatively poor groundwater quality, while northern and western regions showed relatively better water quality conditions. Comparative analysis demonstrated that WWQI provided a more realistic and reliable assessment because parameter-specific weighting improved sensitivity toward critical contaminants. GIS-based hotspot mapping successfully delineated vulnerable groundwater zones and highlighted areas requiring immediate monitoring and management intervention. The study demonstrates that integration of GIS and WQI techniques is highly effective for groundwater quality assessment, contamination hotspot identification, and sustainable groundwater resource management. The findings of this study can support policymakers and environmental planners in developing targeted groundwater protection and remediation strategies for rapidly urbanizing regions.

DOI: http://doi.org/10.5281/zenodo.20268893

Published by: vikaspatanker