Authors: Dr. K. Himabindu, Faruk Ahmed, Pankaj Kumar Bara, Krishna Kumar Yadav, Soham Parmar

Abstract: Healthcare chatbots are gradually moving beyond simple question-and-answer systems by bringing together large language models, information retrieval, multimodal analysis, and human oversight. This paper explores the open-source Multi-Agent Medical Assistant as a safety-focused tool for providing medical information. The system includes separate components for input screening, conversation, retrieval-augmented generation (RAG), web-based evidence retrieval, medical image processing, response generation, and human validation. Rather than attempting to provide autonomous diagnoses, the system emphasizes evidence-based responses, transparent communication of uncertainty, and human review.

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Authors: Atharv Sharma

Abstract: In this paper, we propose Adaptive Trust-Based Continuous Authentication Framework (ATCAF), a lightweight architecture to extend Zero Trust principles with continuous risk aware authentication. The proposed framework does not make a single authentication decision at login but continuously calculates a dynamic trust score by combining multiple contextual security indicators such as device trust, behavioral consistency, location confidence, network reputation and historical user reputation. Access decisions are made in real-time based on the continuously evolving trust score, enabling adaptive responses such as seamless access, step-up multi-factor authentication, privilege reduction or session termination. The paper describes the system architecture, mathematical trust model, continuous evaluation workflow, implementation methodology, public evaluation datasets, security analysis and experimental design to evaluate authentication accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), trust score stability and response latency . The framework we propose aims at improving identity security, while maintaining usability and reducing unnecessary authentication interruptions.

DOI: https://zenodo.org/records/22976298

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Authors: Vishal Sankhyan, Dr.Rajesh Chauhan, Dr. Akshay Bhardwaj

Abstract: Green computing refers to the environmentally responsible design, manufacture, operation, and disposal of computing systems and information technology (IT) infrastructure. The rapid growth of data centers, cloud computing, artificial intelligence, Internet of Things (IoT) devices, and other digital technologies has increased energy consumption and electronic waste. Green computing seeks to reduce the environmental impact of IT while maintaining performance, reliability, security, and economic efficiency. This research paper examines major approaches, techniques, and implementation strategies for green computing. The study discusses energy-efficient hardware, virtualization, server consolidation, cloud computing, power management, green software development, efficient data centers, sustainable networking, renewable energy, and electronic-waste management. It also presents an implementation framework for measuring and reducing the energy consumption of computing infrastructure. The paper identifies important challenges, including initial investment, compatibility, performance requirements, lack of awareness, measurement difficulties, and e-waste management. Finally, it discusses future directions such as artificial intelligence-based energy optimization, edge computing, sustainable data centers, and carbon-aware computing. The study concludes that combining hardware, software, infrastructure, and organizational strategies can significantly improve the sustainability of modern information technology.

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Authors: Mohana Ravi Teja Gutta

Abstract: Data migration of large volumes of data from legacy information systems to cloud-based architecture is becoming more and more common in enterprise digital transformation projects, traditional data migration methods usually suffer from issues related to the use of disparate data format, incompatibility of data schema, migration downtime, inefficient resource utilization, and insufficient data validation. These issues lead to lengthy migration process, higher migration risks, and lower migration performance. This research proposes a Cloud Native Enterprise Data Migration Framework (CN-EDMF) that combines data discovery, data dependencies, data harmonization, workload management, resource management, and data validation in one migration framework. The proposed framework employs metadata-based intelligence for identifying relationships within enterprise datasets, determining optimal migration sequence, and allocating cloud resources according to the characteristics of workloads. Moreover, automated mechanisms for ensuring data quality and data integrity verify that the process is executed successfully and reliably. The framework was evaluated experimentally using enterprise datasets varying between 5 TB to 25 TB and was compared with other existing frameworks including ServiceMiner (SM) and CloudNet (CN). As a result, CN-EDMF has managed to achieve the following results Migration Success Rate of 99.2%, Data Quality Preservation of 99.4%, Migration Reliability of 99.6%, and Resource Utilization Efficiency of 94.4%. The research proves the effectiveness of the proposed framework for improving the migration process. It provides an efficient solution for cloud-native enterprise migration.

DOI: https://zenodo.org/records/22957355

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Authors: Avinash Yeluri

Abstract: The fast embrace of multi-cloud computing has made it possible for organizations to gain scalability, flexibility, availability, and cost efficiency. Nevertheless, the monitoring of workloads in multi-cloud environments has become increasingly difficult because of fragmented visibility, privacy issues, increased communication overhead, and increasing telemetry data. Centralized monitoring methods involve sending information from distributed locations to a centralized location, resulting in higher costs and increased risks. The FL-IMME framework, introduced in this study, utilizes federated learning and is aimed at intelligent monitoring that preserves data locality. The framework combines telemetry gathering, generation of local intelligence, federated aggregation, anomaly detection, predictive monitoring, and adaptive decision-making mechanisms to ensure privacy-preserving and scalable monitoring capabilities. The novel approach involves training of local monitoring models in each cloud environment individually and exchanging encrypted model parameters via federated learning. Experiments were performed on a large multi-cloud observability data set and the performance was compared to CCFRL and AHFLP methods. The results revealed better performance of FL-IMME with anomaly detection accuracy of 99.0%, failure prediction accuracy of 99.1%, privacy preservation score of 99.4%, and system reliability of 99.5%. In addition, the proposed framework lowered the communication overhead and monitoring latency while increasing the efficiency of resources utilization.

DOI: https://zenodo.org/records/22957300

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Authors: Satya Prakash Prakki, Venkatarami reddy, Madhulika Gajjala

Abstract: Cloud HCM platforms are becoming a game-changer in payroll operations, offering an opportunity for automation, real-time data integration, streamlined processes, and scalable workforce management. While implementation-focused models of payroll transformation tend to focus on the process, they lack adequate attention to aspects of stakeholder accountability, governance, decision rights, compliance and change management. This poses problems such as ownership, slow decision making, integration issues, compliance risks and resistance to organizational change. This study suggests the Stakeholder-Centric Payroll Governance Model (SCPGM) to deal with the above limitations in the context of payroll transformation programs in Cloud HCM environments. The model proposed involves a framework of stakeholder identification, governance roles, decision-right matrix, risk and compliance controls, communication mechanisms and performance monitoring. The model aims to bring HR, payroll, IT, compliance, implementation partners and business leadership on board during the transformation process. Proposed quantitative evaluation criteria include implementation efficiency, decision time, accuracy of payroll, adherence to compliance, and satisfaction and transformation risk of stakeholders. Target decision-making time should be reduced by 20-30%, payroll processing efficiency should improve by 15-25%, and the stakeholder satisfaction with the transformation should be maintained at more than 90%. The proposed SCPGM offers a governance model to enhance accountability, collaboration, transparency and the results of Cloud HCM payroll transformation initiatives.

DOI: https://zenodo.org/records/22956245

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Authors: Hosea, J. O., Ogbonna, B. O.

Abstract: One of the problems associated with wind energy is fluctuations in wind speed, which cause unstable and inefficient operation of variable-speed wind turbines, especially above the rated operating range where blade pitch control is necessary to adjust aerodynamic power and maintain generator speed within the rated range. This study investigates the effect of a Fuzzy Logic Controller (FLC) on pitch-angle regulation compared with the dynamic performance of a proportional-integral (PI) controller. The same wind turbine model, generator reference speed, wind-speed profile, and operating conditions were used for both controllers. The proposed Mamdani-type FLC is based on generator-speed error and its rate of change, using scaling, saturation, fuzzification, rule-based inference, and defuzzification to derive an appropriate pitch-angle command. The FLC adjusts the pitch command according to changing turbine operating conditions, enabling control of the aerodynamic power coefficient, aerodynamic torque, generator speed, and output power under wind-speed disturbances. Controller performance was evaluated using rise time, settling time, percentage overshoot, steady-state error, RMSE, IAE, ISE, ITAE, and ITSE. The FLC increased rise time from 1.7677 s to 3.2920 s, while reducing settling time from 17.2793 s to 5.5018 s and overshoot from 7.662% to 0.880%. Furthermore, the FLC reduced steady-state error, IAE, ITAE, and ITSE by 19.20%, 20.76%, 71.56%, and 61.97%, respectively. The PI controller produced lower RMSE and ISE. Overall, the results showed that FLC-based pitch-angle control provides better damping, disturbance rejection, steady-state accuracy, and long-term tracking performance under variable wind operation, although its initial response is slower than conventional PI control.

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Authors: Mr. Murari Kumar, Dr. Deepti shinghal

Abstract: In modern electronic systems, analog-to-digital converters (ADCs) play a critical role in bridging the analog and digital domains. At the heart of ADC architecture lies the comparator, a fundamental circuit responsible for precise and fast voltage comparison. With the rapid growth of portable and battery-powered devices, there is an increasing need for high-speed and low-power CMOS comparators to meet performance and energy efficiency demands. This review paper presents a comprehensive analysis of various design techniques and topologies used to achieve high speed and low power consumption in CMOS comparators. Key performance parameters such as propagation delay, power consumption, input offset voltage, and resolution are discussed in detail. Additionally, the paper highlights the trade-offs involved in comparator design and explores recent advancements, including dynamic comparators, adaptive biasing, and low-voltage operation techniques. The role of high-performance comparators in different ADC architectures—such as flash, SAR, and pipeline ADCs—is also reviewed, providing insights into future research directions in this critical field of analog circuit design.

DOI: http://doi.org/10.5281/zenodo.22935146

Published by: vikaspatanker

Authors: Professor Vikram Singh, Research Scholar Naresh Kumar

Abstract: The rapid generation of deepfake videos poses significant challenges to real-time detection systems, which often encounter a wide range of quality variations and novel manipulation techniques. In order to deal with the challenges faced we propose a hybrid InceptionV3-LSTM network that encapsulates spatial feature extraction and action sequence over time. This architecture employs InceptionV3 pretrained on ImageNet for modeling frame-level artifacts, then stacks a bidirectional LSTM to jointly learn the temporal problem from multiple sequences. The model displays robustness under high compression and when tested on low compression, it shows a decline of just 2.5% in accuracy which implies that the model is sensitive. According to temporal analysis, LSTM proves capable of detecting manipulations as illustrated due to reconstruction loss variance being higher by 3.2× of low-fidelity deepfakes. The deepfake inference data under tool-based evaluation shows this detection. The InceptionV3-LSTM’s full-fledged version achieves 150 ms latency and 96% accuracy while real-time performance granted 20 ms/frame latency using MobileNet-LSTM with no accuracy loss (96%). The solution presented in this study can be deployed, balancing detection accuracy and computational efficiency. However, limitations still exist for novel manipulation techniques and multi-modal integration could be an avenue for future work. The objective of this experimental study is to achieve the enhanced detection accuracy using separate training and testing datasets of deepfake videos which can help get a better security performance and robustness. By integrating these advances, the application allows users to check the authenticity of people and deep fake videos. A framework for real-time detection of visual deepfake videos analyzes pixel-level artifacts including blending boundaries, double edges, eyes, ears, nose, head pose inconsistencies and other possible parameters sub-pixel noise patterns unique to manipulation videos.

DOI: http://doi.org/10.5281/zenodo.22934344

Published by: vikaspatanker

Authors: Assistant Professor Hakam Chand

Abstract: The paper analyses infrastructure and market connectivity as joint determinants of climate-resilient horticulture in Kangra district, Himachal Pradesh. It uses a secondary-data case-study approach and the latest consistent state horticulture series available in the supplied publications, covering 2011-12 to 2024-25. Although horticulture output expanded during the period, climate variability, fragmented production, seasonal irrigation, post-harvest losses and costly transport threaten the stability of smallholder returns. Kangra’s traditional kuhl systems, road network, urban centres and crop diversity provide a strong base, but infrastructure must function as an integrated service chain. The study develops an Infrastructure Readiness Matrix covering water, planting material, extension, protection, aggregation, grading, cold chain, processing, roads, digital information and finance. It also proposes indicators for reliability, utilisation and inclusion. The analysis suggests that water harvesting and micro-irrigation should be combined with crop planning; pack houses should be located only where verified throughput exists; and producer aggregation should precede capital-intensive cold-chain investment. Climate resilience requires diversified crops and seasons, quality planting material, local weather and pest advisories, and multiple market channels. A phased investment and monitoring framework is recommended for Kangra.

DOI: http://doi.org/10.5281/zenodo.22933192

Published by: vikaspatanker