Authors: Sreehari K B, Deepakumar M

Abstract: Early disease prediction is a crucial aspect of modern healthcare systems, as it enables timely medical intervention, improves patient survival rates, and reduces long-term healthcare costs. Many chronic and life-threatening diseases such as diabetes, cardiovascular disorders, cancer, and neurological conditions develop gradually and often remain asymptomatic during their early stages. Traditional diagnostic approaches, which rely on clinical rules, physician experience, and fixed statistical thresholds, are often inadequate for detecting these early-stage disease patterns. and neurological disorders progress slowly over time and are often diagnosed only at advanced stages. Late diagnosis significantly reduces treatment effectiveness and increases mortality rates. With the growing global disease burden and aging population, early detection has become a priority in modern healthcare systems. Advancements in healthcare digitization have led to the availability of large-scale medical data, including Electronic Health Records (EHRs), laboratory reports, and medical imaging. These datasets provide valuable insights into patient health patterns and disease progression, enabling the development of predictive models for early diagnosis. With the rapid digitization of healthcare, vast amounts of medical data are generated through Electronic Health Records (EHRs), laboratory test reports, diagnostic imaging, and wearable health devices. This has created opportunities for Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyze complex and high- dimensional medical data efficiently. Existing AI- based disease prediction systems have demonstrated improved accuracy compared to conventional methods; however, many of these systems suffer from limitations such as reliance on single-modal data, centralized data storage, poor generalization across healthcare institutions, severe class imbalance, and lack of interpretability. This project proposes an AI-based early disease prediction framework that addresses these limitations through the integration of multimodal clinical data, privacy-aware learning mechanisms, imbalance-sensitive training strategies, and explainable AI techniques. The proposed system learns complex patterns from longitudinal patient data and generates calibrated risk scores to support early diagnosis and preventive care. By improving transparency, robustness, and clinical trust, the proposed framework aims to provide an effective and scalable solution for early disease prediction in real-world healthcare environments.

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Published by: vikaspatanker

Authors: Akshay Bhangade, Dr. Pushpa Pathak

Abstract: The rapid expansion of higher education institutions has intensified the need for efficient, intelligent, and scalable student management solutions. Traditional college management systems often suffer from fragmented data handling, limited automation, and insufficient analytical capabilities, leading to administrative inefficiencies and suboptimal academic decision-making. This paper presents an enhanced Student College Management System that integrates intelligent academic automation, centralized student information management, and data-driven performance analytics within a unified architectural framework. The proposed system leverages automation to streamline core academic processes such as admissions, course registration, attendance tracking, assessment management, and result processing, thereby reducing manual intervention and operational errors. A centralized database architecture ensures secure, consistent, and real-time access to comprehensive student records across departments. Furthermore, advanced analytics modules utilize historical and real-time data to evaluate student performance, identify learning patterns, predict academic risks, and support evidence-based decision-making for faculty and administrators. The system architecture emphasizes modularity, scalability, and interoperability, enabling seamless integration with existing institutional platforms and future technological enhancements. By combining intelligent automation with robust analytics, the proposed solution enhances administrative efficiency, improves academic monitoring, and supports personalized student development. This integrated approach contributes to improved institutional governance, better learning outcomes, and a data-driven academic ecosystem aligned with modern higher education requirements.

Published by: vikaspatanker

Authors: Vikram S Tigadi, Yallaling R Dalawayi, Rajesh S Meti,, Rajguru M Hiremath, Professor Pooja C Shindhe

Abstract: Heart disease remains one of the leading causes of death throughout the world, and early detection is the key to improved patient outcomes. This paper introduces a Decision Support Heart Health Prediction System (DSHHPS) developed using machine learning techniques to help diagnose critical clinical and demographical data including age, BP level, cholesterol level, glucose level and other vital medical signs. The processed data is further sanitized using pre-cleaning, preprocessing and selection of features to make it reliable and accurate. Several different machine learning models are tested and compared The system evaluates many clinical information such as age, sex, blood pressure, cholesterol level, the results of the resting ECG reading, the type of chest pain and the amount of sugar in their bloodstream along with other important health readings. Rigor: The dataset is subjected to various cleaning, preprocessing and feature selection processes to remove inconsistencies and error prior to training the model. A number of machine learning models are experimented and compared to select the best one, which produces the most accurate predictions.

Published by: vikaspatanker

Authors: Wafaa N. Jasim, Faten N. Jasim

Abstract: To characterize and analyze the capability of elements to attenuate X-rays, a number of important physical indicators were calculated, namely the mass attenuation coefficient µ/ρ, the half-value layer HVL, and the free path rate MFP, as they play a role in describing the nature of the material that can be used as a protection method in medical centers and laboratories related to dealing with X-rays, using two methods. The first is using the XCOM program, and the second method is the FFAST tool. They were applied to the elements Fe, Ag, Sn, Pt, Au, and Pb at an energy of 30 KeV, in order to evaluate the effectiveness of each element in medical and industrial applications related to radiation protection. We obtained good agreement between the two methods, and the Matlab program was relied upon in the calculations and drawings that show the relationship and agreement between them.

DOI: https://doi.org/10.5281/zenodo.17949497

Published by: vikaspatanker

Authors: Dr.C.K Gomathy, Ananth Lakshmi ss, Lakshmi A

Abstract: Urban areas are becoming increasingly congested as populations grow and public spaces experience unpredictable fluctuations in foot traffic. This constant movement creates challenges for city planners, traffic authorities, and public safety teams who require reliable, real-time information to manage crowds efficiently. This research investigates the use of Big Data Analytics for predicting urban crowd flow by analyzing digital footprint signals generated through everyday human interaction with technology. These signals include smartphone GPS activity, Wi-Fi hotspot connections, public sensor logs, transport card swipes, and metadata from CCTV systems. By integrating these diverse and continuous data streams, the study proposes a multi-layered predictive framework capable of detecting mobility patterns, forecasting future crowd density, and supporting city-level decision-making. Through machine learning and deep learning models, the framework processes large-scale movement data and produces highly accurate predictions. The findings demonstrate that Big Data-driven analysis significantly enhances crowd-flow forecasting accuracy, improves safety management, supports effective traffic control, and strengthens urban planning strategies for smart cities.

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Published by: vikaspatanker

Authors: Dr Hari Gangadhar Kale

Abstract: Many aspects of life have benefited from laser technology which is regarded as one of the most significant technologies of the 20th century. These days laser technology is valuable in all manufacturing fields and offers a number of unique advantages including the production of mechanical tools and machines. Laser technology has steadily taken over and dominated the mechanical market particularly in the areas of material handling and metal parts because of its advanced cleaning capabilities fine welding lines powerful etching strokes high power operation and precise distance measurement capability. The benefits this business receives from laser cutting technology.

Published by: vikaspatanker

Authors: Sharmila P, Harini S, Harini D, Harini RV, Jothivarshini S, Karthi S

Abstract: In recent years, personalized fashion recommendation systems have gained significant importance due to the growing demand for customized user experiences in online shopping platforms. This project presents a Smart Dress Recommendation System that provides personalized clothing suggestions based on individual user preferences and physical attributes. The system employs a rule-based recommendation approach, where users are guided through a structured questionnaire to collect essential information such as occasion, gender, budget, style preferences, body measurements, body shape, and skin tone. The body measurement module analyzes user inputs to classify body types such as pear, rectangle, apple, or hourglass, while the skin tone module allows users to select their skin color from a predefined palette. Based on these parameters, a set of predefined rules is applied to recommend suitable dresses that enhance the user’s appearance and meet their personal preferences. The system ensures that fashion suggestions are practical, affordable, and visually appealing by considering budget constraints and occasion-specific requirements.

 

Published by: vikaspatanker

Authors: Srinivasa Chakravarthy Seethala

Abstract: This research paper investigates the role of machine learning enhanced query optimization techniques in improving the performance, scalability, and reliability of SQL and NoSQL databases used for high volume big data processing in financial and healthcare applications. Traditional rule based and cost based query optimizers often struggle to adapt to dynamic workloads, heterogeneous data distributions, and rapidly evolving access patterns that characterize modern financial transactions and healthcare data ecosystems. This study addresses the research problem of how adaptive machine learning driven optimization models can overcome these limitations by learning from historical query execution patterns, system telemetry, and workload characteristics. A mixed methodology is adopted, combining conceptual framework design, algorithmic modeling, and comparative performance analysis across representative SQL and NoSQL environments handling large scale transactional and analytical workloads. The findings demonstrate that machine learning enhanced optimizers significantly reduce query latency, improve resource utilization efficiency, and enhance workload predictability under peak data volumes compared to traditional optimization approaches. The paper highlights key innovations such as predictive cost modeling, adaptive index selection, and real time execution plan refinement driven by supervised and reinforcement learning techniques. From an academic perspective, this research contributes to the evolving discourse on intelligent data management systems by extending optimization theory into data driven adaptive architectures. From an industry standpoint, the results provide actionable insights for designing resilient, high performance data platforms capable of supporting mission critical financial and healthcare operations where accuracy, compliance, and responsiveness are paramount.

DOI: http://doi.org/10.5281/zenodo.17938651

Published by: vikaspatanker

Authors: Srinivasa Chakravarthy Seethala

Abstract: This study proposes an AI driven compliance intelligence platform designed to enable continuous monitoring and automated risk assessment within highly regulated CRM and ERP environments. Organizations operating in finance, healthcare, public sector, and other compliance intensive domains increasingly rely on complex enterprise platforms where regulatory obligations evolve faster than traditional audit and control mechanisms can adapt. The research addresses the limitations of static compliance models by introducing an architecture that integrates machine learning, natural language processing, and policy aware analytics to interpret regulatory requirements, monitor transactional and configuration level signals, and dynamically assess compliance risk in real time. A mixed methodological approach is adopted, combining conceptual system design with simulated enterprise data flows and scenario based evaluations across common regulatory regimes such as data protection, financial controls, and access governance. The findings demonstrate that AI driven compliance intelligence can significantly improve early risk detection, reduce manual audit effort, and enhance traceability across CRM and ERP processes by continuously correlating system behavior with regulatory intent. The platform introduces adaptive risk scoring, automated control validation, and explainable compliance insights that support both operational teams and governance stakeholders. From a strategic perspective, the study contributes to a forward looking compliance paradigm that shifts organizations from periodic, reactive audits toward proactive and continuous assurance models. Academically, the research extends existing literature on enterprise governance by formalizing compliance intelligence as a scalable, data driven capability embedded within enterprise software ecosystems.

DOI: http://doi.org/10.5281/zenodo.17938605

Published by: vikaspatanker

Authors: Srinivasa Chakravarthy Seethala

Abstract: Real time event streaming pipelines form the operational backbone of modern digital platforms, supporting continuous data ingestion, processing, and delivery across cloud native and distributed environments. Despite their importance, reliability management in such pipelines remains largely reactive, relying on threshold based monitoring and post failure diagnostics that are insufficient for preventing cascading disruptions. This study addresses the problem of anticipating reliability degradation in real time event streaming systems by proposing a predictive reliability engineering framework grounded in multi modal deep learning. The primary objective is to enable early identification of failure precursors by jointly analyzing heterogeneous telemetry sources, including system metrics, execution logs, distributed traces, and event level metadata. A mixed method research approach is adopted, combining quantitative modeling of historical incident data with qualitative architectural analysis of streaming platforms to inform model design and integration. The proposed framework employs temporal and representation learning techniques to fuse multi modal signals and generate probabilistic reliability risk scores ahead of observable failures. Experimental evaluation across representative streaming workloads demonstrates improved failure prediction accuracy, longer warning lead times, and reduced false alert rates compared to single source monitoring baselines. The findings highlight the innovation of multi modal fusion for reliability prediction and its implications for proactive operational decision making. From an academic perspective, the study advances reliability engineering by introducing predictive, data driven models tailored to real time pipelines. From an industry standpoint, the framework supports more resilient event driven architectures through earlier intervention, reduced downtime, and improved service continuity, reinforcing the strategic value of intelligent reliability management in high availability systems.

DOI: http://doi.org/10.5281/zenodo.17938555

Published by: vikaspatanker