Storage automation is critical for managing the vast amounts of data generated in modern computing environments. Advanced Python scripting offers robust solutions for automating storage tasks, enhancing efficiency, scalability, and reliability. This research explores the utilization of Python's versatile libraries and frameworks to develop automated storage systems. We present a comprehensive methodology encompassing system architecture design, data collection and preprocessing, feature engineering, algorithm selection, and model deployment. The study emphasizes the (...) integration of Python scripts with existing storage infrastructures, enabling real-time transaction verification, sentiment-based escalation triggers, and automated response generation. Through implementation workflows and code examples, we demonstrate the practical applications of Python in automating complex storage operations. Evaluation metrics and continuous monitoring strategies are discussed to ensure system performance and compliance with security standards. The findings indicate that Python-based automation significantly reduces manual intervention, minimizes errors, and optimizes storage management processes. This research contributes to the field by providing a detailed framework for leveraging Python in storage automation, highlighting its advantages, limitations, and potential challenges. Future work will focus on enhancing scalability and integrating machine learning models for predictive storage management. (shrink)

In the rapidly evolving landscape of data storage solutions, Dell EMC’s VMAX and PowerMax series have emerged as pivotal technologies for enterprise-level storage management. This research article delves into the architectural intricacies, performance benchmarks, and operational efficiencies of VMAX and PowerMax, providing a comprehensive guide for storage experts. By analyzing core components, integration points, and data processing methodologies, the study elucidates the advancements that PowerMax introduces over its predecessor, VMAX. Through empirical data collection, preprocessing, (...) and feature engineering, the research employs robust algorithmic models to evaluate system performance, scalability, and reliability. The implementation workflow encompasses initial setup, sentiment analysis, automated response mechanisms, and real-time transaction verification, ensuring a holistic examination of both storage solutions. Results indicate significant improvements in speed, data integrity, and user satisfaction with PowerMax. The discussion highlights the advantages, such as enhanced security and compliance, while addressing limitations and challenges inherent in transitioning from VMAX to PowerMax. Conclusively, this guide serves as an essential resource for IT professionals seeking to optimize their storage infrastructure through informed decision-making and strategic deployment of VMAX and PowerMax systems. (shrink)

As organizations increasingly migrate their data infrastructures to the cloud, efficient cloud operations become paramount for storage professionals. Amazon Web Services (AWS) offers a comprehensive suite of tools and services tailored to meet diverse storage needs. This research article explores the operational strategies and best practices for storage professionals leveraging AWS Cloud. It delves into system architecture, data management, security protocols, and performance optimization within the AWS ecosystem. The study employs a mixed-methods approach, combining (...) quantitative data analysis with qualitative insights from industry practitioners. Key components such as data collection, preprocessing, feature engineering, and model deployment are examined to establish a robust framework for AWS cloud operations. The findings indicate significant improvements in scalability, reliability, and cost-efficiency when adhering to recommended AWS practices. Additionally, the research highlights challenges related to data security, compliance, and system integration, offering actionable solutions to mitigate these issues. By providing a comprehensive overview of AWS cloud operations, this paper serves as a valuable resource for storage professionals aiming to optimize their cloud-based storage solutions. The implications of this study extend to enhancing organizational data strategies, ensuring regulatory compliance, and fostering innovation in cloud storage technologies. (shrink)

The proliferation of cloud computing has revolutionized data storage paradigms, offering scalable, flexible, and cost-effective solutions for diverse organizational needs. This research article delves into the comparative analysis of two leading cloud service providers: Microsoft Azure and Amazon Web Services (AWS). By examining their storage offerings, system architectures, integration capabilities, and security protocols, the study aims to provide storage professionals with insights into optimizing cloud storage strategies. The methodology encompasses a comprehensive evaluation of core (...) components, data collection and preprocessing techniques, feature engineering, algorithm selection, and model training. Additionally, the implementation workflow highlights initial setup, automated response generation, real-time transaction verification, and continuous monitoring. Results indicate that both Azure and AWS offer robust storage solutions with distinct advantages in scalability, reliability, and security. However, challenges such as cost management, integration complexity, and compliance adherence persist. The discussion elaborates on these findings, presenting a comparative table to elucidate the strengths and limitations of each platform. Conclusively, the research underscores the importance of strategic selection and implementation of cloud storage services to enhance operational efficiency and data integrity. Future studies are recommended to explore emerging technologies and their integration with cloud storage systems to address evolving business requirements. (shrink)

The rapid advancement of generative artificial intelligence (Gen AI) has revolutionized various domains, including financial analytics. This paper provides a comprehensive review of the applications, challenges, and future directions of Gen Al in financial analytics. We explore its role in risk management, credit scoring, feature engineering, and macroeconomic simulations, while addressing limitations such as data quality, interpretability, and ethical concerns. By synthesizing insights from recent literature, we highlight the transformative potential of Gen AI and propose frameworks for its effective (...) integration into financial workflows. This paper presents a systematic examination of generative artificial intelligence (Gen AI) applications in financial risk management, focusing on architectural frameworks and implementation methodologies. We analyze the integration of large language models (LLMs) with traditional quantitative finance pipelines, addressing key challenges in feature engineering, risk modeling, and regulatory compliance. The study demonstrates how transformer-based architectures enhance financial analytics through automated data processing, risk factor extraction, and scenario generation. Technical implementations leverage hybrid cloud platforms and specialized Python libraries for model deployment, achieving measurable improvements in accuracy and efficiency. Our findings reveal critical considerations for production systems, including computational optimization, model interpretability, and governance protocols. The proposed architecture combines LLM capabilities with domain-specific modules for credit scoring, value-at-risk calculation, and macroeconomic simulation. Empirical results highlight trade-offs between model complexity and operational constraints, providing actionable insights for financial institutions adopting Gen Al solutions. The paper concludes with recommendations for future research directions in financial Al systems. (shrink)

Web attacks, such as SQL injection, cross-site scripting (XSS), and distributed denial-ofservice (DDoS), pose significant threats to the security and integrity of online systems. Traditional detection methods, relying on rulebased systems or shallow machine learning, often struggle to keep pace with the evolving sophistication of these attacks. This paper proposes an end-to-end deep learning framework for detecting web attacks, leveraging the power of neural networks to automatically learn complex patterns and features from raw web traffic data. Unlike conventional approaches (...) that require extensive manual feature engineering, our method processes unprocessed HTTP request logs and network traffic directly, enabling a fully automated detection pipeline. The proposed model employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to capture both spatial and temporal dependencies in web traffic data. CNN layers extract local patterns, such as malicious payloads or anomalous headers, while RNN layers, enhanced with attention mechanisms, model sequential behaviors indicative of attacks. The framework is trained endto-end on a diverse dataset comprising benign traffic and real-world attack samples, ensuring robustness across various attack vectors. Preprocessing is minimized to tokenization and normalization, preserving the raw data's integrity and reducing human intervention. Evaluation results demonstrate that the model achieves superior performance compared to traditional methods, with an accuracy of over 95% and a low false-positive rate. It effectively identifies both known and zero-day attacks by generalizing learned representations. Additionally, the system scales efficiently, processing large volumes of traffic in real-time, making it suitable for deployment in production environments. This end-to-end deep learning approach not only enhances detection accuracy but also adapts to emerging threats, offering a proactive solution to the growing challenge of web security. Future work will focus on integrating explainability to improve trust and interpretability in security operations. (shrink)

The advent of generative artificial intelligence (AI) in the financial technology (FinTech) sector has created unprecedented opportunities for automating and enhancing financial advisory systems. This research focuses on the application of generative AI to develop automated financial advisory platforms, integrating natural language processing (NLP) and reinforcement learning (RL) for the formulation of personalized investment strategies. Traditional financial advisory models, often characterized by manual processes, human bias, and limited scalability, are increasingly unable to meet the demands of a fast-paced and diverse (...) investment landscape. In response, AI-driven systems present a transformative approach, leveraging the power of generative models to process vast amounts of data and provide real-time, tailored financial recommendations to both retail and institutional investors. This study delves into the technical mechanisms underpinning the integration of generative models with NLP and RL frameworks. Generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), play a critical role in simulating complex financial scenarios and generating investment strategies that reflect dynamic market conditions. By synthesizing vast amounts of historical market data, these models create high-dimensional representations of financial environments, which are then used to train reinforcement learning agents. The RL agents learn optimal investment strategies through continuous interaction with these simulated environments, dynamically adjusting to new market information and user preferences. This ability to simulate and optimize investment decisions allows for more sophisticated, personalized strategies, as compared to conventional rule-based systems. Natural language processing enhances the system by enabling it to process unstructured data from various sources, including financial news, reports, and social media, which can significantly impact market trends. NLP models, particularly transformer-based architectures like BERT and GPT, are employed to extract, interpret, and summarize relevant textual information in real-time, feeding it into the generative and RL models. This integration allows the financial advisory system to understand and respond to both quantitative and qualitative factors affecting financial markets. Moreover, the NLP component supports direct interaction between the AI-driven system and users, facilitating personalized communication and user-specific strategy recommendations. This two-way communication is pivotal in enhancing customer engagement, as users can input preferences, risk tolerance, and financial goals, which the system continuously refines and incorporates into its investment strategy formulation. Reinforcement learning plays a pivotal role in the adaptive learning process, allowing the system to improve its decision-making over time by receiving feedback from the environment, such as market performance and user satisfaction. Specifically, model-free RL approaches like Q-learning and policy gradient methods are applied to optimize investment strategies. These approaches enable the system to evaluate multiple potential actions in real-time and select those with the highest expected return, given the current market state and individual user profile. Over time, the RL agent learns to maximize cumulative returns by balancing exploration of new strategies with the exploitation of known profitable actions. By leveraging RL in tandem with generative models, the system can autonomously adjust its strategy in response to changing market conditions and user requirements, thereby delivering a highly customized investment plan that evolves with the investor’s financial landscape. The potential of these AI-driven advisory systems lies not only in their technical sophistication but also in their ability to democratize financial planning. Traditionally, high-quality financial advisory services have been accessible primarily to affluent individuals or large institutions due to the high cost of personalized financial advice. By automating the advisory process through AI, these systems can provide personalized financial planning at scale, making high-quality investment strategies accessible to a broader range of users, including those with limited financial literacy or smaller investment portfolios. This democratization of financial services is particularly significant in the context of retail investors, who can now access sophisticated financial insights and recommendations that were previously reserved for institutional clients. Furthermore, this paper explores the broader implications of AI-driven financial advisory systems on investor behavior and decision-making. By providing real-time, data-driven insights and personalized investment strategies, these systems have the potential to mitigate common cognitive biases in financial decision-making, such as overconfidence, loss aversion, and herd behavior. Through continuous learning and adaptation, AI-driven systems can guide users towards more rational, objective investment decisions, potentially improving overall financial outcomes for both retail and institutional investors. However, the paper also addresses the challenges associated with the deployment of AI in financial advisory systems, including issues of data privacy, algorithmic transparency, and the need for robust regulatory frameworks to ensure the ethical and responsible use of AI in financial decision-making. (shrink)

This research paper presents an in-depth analysis of advanced artificial intelligence (AI) algorithms designed to automate data preprocessing in the healthcare sector. The automation of data preprocessing is crucial due to the overwhelming volume, diversity, and complexity of healthcare data, which includes medical records, diagnostic imaging, sensor data from medical devices, genomic data, and other heterogeneous sources. These datasets often exhibit various inconsistencies such as missing values, noise, outliers, and redundant or irrelevant information that necessitate (...) extensive preprocessing before being analyzed by machine learning or statistical models. Traditional data preprocessing methods, which are largely manual and time-consuming, can result in errors that affect the quality of the data and, subsequently, the performance of predictive and diagnostic models. Thus, there is a growing need for intelligent, automated systems that can enhance data quality, streamline the preprocessing pipeline, and reduce the time and effort required by healthcare professionals and data scientists. The study begins by outlining the specific challenges associated with healthcare data, including its high dimensionality, incompleteness, and variability across different data sources and formats. These issues not only complicate the preprocessing stage but also hinder the ability to develop robust models capable of making accurate predictions or diagnoses. The paper then explores how AI algorithms—particularly those based on machine learning (ML), deep learning (DL), and reinforcement learning (RL)—can automate key data preprocessing tasks such as data cleaning, feature selection, normalization, and transformation. These algorithms are designed to identify patterns in data, detect anomalies, and automatically apply corrections or transformations based on predefined rules or learned behaviors, thereby minimizing human intervention. The paper also delves into specific AI techniques that have been successfully applied to healthcare data preprocessing. For instance, supervised learning models, such as decision trees and support vector machines (SVMs), have been utilized to perform imputation of missing data by predicting the most likely values based on the available information. Similarly, unsupervised learning methods, such as clustering algorithms, have been employed to group similar data points and remove outliers that could distort the performance of analytical models. Moreover, deep learning techniques, particularly autoencoders and generative adversarial networks (GANs), have demonstrated remarkable effectiveness in transforming high-dimensional medical data into lower-dimensional representations, enabling more efficient and accurate model training. In addition to the discussion of these algorithms, the paper emphasizes the role of natural language processing (NLP) in automating the preprocessing of unstructured healthcare data, such as clinical notes and diagnostic reports. NLP techniques, including named entity recognition (NER) and word embeddings, are instrumental in extracting relevant information from unstructured text, standardizing terminologies, and converting textual data into structured formats suitable for downstream analysis. Furthermore, AI-based feature selection algorithms are explored, which aim to identify the most relevant features in the dataset, thereby reducing its dimensionality and improving the computational efficiency of predictive models. The study goes on to highlight the significant reduction in processing time achieved by AIdriven automation of preprocessing tasks. In conventional settings, data preprocessing accounts for a substantial portion of the time spent on building healthcare models, often requiring expert intervention to manually inspect and clean the data. By employing AI algorithms, not only can this process be expedited, but the accuracy of the resulting data is also enhanced, which translates into better model performance. The paper provides a detailed comparative analysis of manual preprocessing methods versus automated AI-driven approaches, demonstrating the substantial time savings and improvements in data quality brought about by automation. (shrink)

Abstract: Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential challenges, such as overfitting, dataset (...) size, and class imbalance, and presents strategies employed to address these issues. Additionally, the paper emphasizes the importance of alternative evaluation metrics, including precision, recall, and F1-score, to assess the model's performance comprehensively. Detailed information regarding the model architecture, data preprocessing, and hyperparameter tuning is provided to enhance the reproducibility of the research. The study also discusses ethical considerations and the implications of false positives and false negatives in smoke detection applications. While achieving 100% accuracy is a notable accomplishment, this paper calls for cautious interpretation and further validation in real-world settings. It concludes by highlighting areas for future research and improvement in smoke detection technology, emphasizing the need for practical deployment and the reduction of false alarms. (shrink)

The rapid evolution of cloud computing has revolutionized data storage solutions, offering scalable, flexible, and cost-effective alternatives to traditional on-premises systems. This research provides an in-depth analysis of leading cloud storage providers, including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others. By evaluating their system architectures, core components, integration capabilities, and security measures, this study aims to guide organizations in selecting the most suitable cloud storage solutions tailored to their specific needs. (...) The methodology encompasses a comprehensive review of existing literature, comparative analysis of key features, and performance assessments based on data collection and preprocessing. Additionally, the study explores the implementation workflows, including initial setup, automated response generation, real-time transaction verification, and continuous monitoring. Security and compliance are critically examined to ensure data integrity and adherence to regulatory standards. The results highlight the strengths and weaknesses of each provider, offering insights into their scalability, reliability, and cost-efficiency. The discussion addresses the advantages, limitations, and challenges associated with cloud storage adoption, providing a balanced perspective for decision-makers. This research contributes to the field by presenting a structured framework for evaluating cloud storage options, ultimately facilitating informed decisions that enhance organizational data management strategies in the digital era. (shrink)

Machine learning (ML) pipelines are essential for automating the workflow involved in model development, from data preprocessing to model evaluation and deployment. A well-structured ML pipeline ensures reproducibility, scalability, and efficiency. This paper offers a comprehensive guide to constructing and optimizing machine learning pipelines in Python, highlighting essential tools, best practices, and common challenges. We discuss the role of various Python libraries like Scikit-learn, TensorFlow, and Apache Airflow in facilitating the automation and deployment (...) of ML workflows. By illustrating pipeline design through practical examples and case studies, this guide provides actionable insights for ML practitioners seeking to improve workflow efficiency and model performance. (shrink)

The early and accurate detection of plant diseases plays a vital role in minimizing crop damage and enhancing agricultural productivity. Traditional methods for identifying plant diseases—such as manual observation and expert consultations—are often time-consuming, costly, and reliant on the availability of skilled personnel. To overcome these limitations, this study presents an automated system for plant disease detection using advanced machine learning techniques. The proposed framework utilizes convolutional neural networks (CNNs), specifically pretrained models like ResNet and SqueezeNet, to analyze images (...) of plant leaves and classify them as healthy or diseased. The system begins with the collection and preprocessing of a diverse dataset comprising both healthy and infected leaf samples. Image augmentation techniques such as rotation, scaling, and flipping are applied to increase dataset variability and enhance model robustness. Subsequently, feature extraction is conducted through CNN-based models that capture color, shape, and texture-based characteristics from the leaf images. Transfer learning is employed to finetune the models on the specific dataset, leveraging previously learned features for improved accuracy. The performance of the trained models is evaluated using key metrics including accuracy, precision, recall, and F1- score. Experimental results indicate a high degree of classification accuracy and generalization capability on unseen data. The final model is deployed through a user-friendly interface that can be integrated into mobile or web-based applications, allowing farmers to upload leaf images and receive instant disease diagnosis and possible treatment recommendations. The proposed system holds significant promise for real-world applications, particularly in regions with limited access to agricultural experts. Moreover, its scalability and adaptability pave the way for future extensions to other crops and disease types. This research underscores the transformative potential of machine learning in precision agriculture, offering a cost-effective, scalable, and efficient solution for early plant disease detection. (shrink)

The advent of cloud computing has revolutionized how machine learning (ML) models are developed, trained, and deployed. By providing scalable, on-demand infrastructure, cloud platforms empower researchers, startups, and enterprises to leverage advanced ML capabilities without the burden of maintaining expensive hardware. This paper explores best practices and diverse use cases for implementing machine learning in the cloud, focusing on resource optimization, workflow automation, and model lifecycle management. Cloud-based machine learning offers several strategic benefits including cost-efficiency, ease of access to (...) high- performance computing (HPC), and the integration of managed services for data preprocessing, model training, deployment, and monitoring. Popular services such as Amazon SageMaker, Google Cloud Vertex AI, and Microsoft Azure ML provide comprehensive environments that simplify end-to-end ML development. However, challenges such as data privacy, vendor lock-in, and cost unpredictability remain persistent concerns. This paper reviews recent literature and analyzes real-world applications of cloud ML in industries like healthcare, finance, and retail. It outlines a research methodology centered around evaluating ML workflows across leading cloud platforms, followed by a discussion of key findings on performance, cost, and scalability. A structured workflow is proposed to guide practitioners in selecting appropriate tools and architectures. Furthermore, the paper identifies the primary advantages and drawbacks of cloud-based ML, concluding with recommendations for overcoming current limitations and future research directions. Use cases such as fraud detection, medical diagnostics, customer segmentation, and predictive maintenance demonstrate the transformative potential of cloud-based ML when implemented with best practices. By highlighting successful implementations and addressing operational trade-offs, this work serves as a practical guide for decision-makers and ML practitioners aiming to maximize the value of machine learning in the cloud. Through a comprehensive examination of tools, techniques, and real-world scenarios, it aims to contribute to more efficient, ethical, and scalable ML solutions. (shrink)

As statistics is the backbone of the digital financial system dependence on centralized cloud storage structures makes users prone to troubles concerning statistics breaches operational price and lack of control this paper examines the deployment of a decentralized cloud storage DCS framework with the use of interplanetary file system IPFS and Ethereum blockchain clever contracts to triumph over those drawbacks the gadget proposed here improves protection and information availability by incorporating aes-256 encryption sharding of records (...) and decentralized metadata control by the introduction of a working prototype based on react.js, Ethereum wallet, ether.js and solidity this mission illustrates the viability of a decentralized statistics garage whilst resolving troubles with latency user adoption and value effectiveness experimental consequences affirm enhancements in safety and availability establishing a strong platform for additional research on decentralized storage architectures. (shrink)

In the era of big data and distributed ecosystems, understanding the origin, flow, and transformation of data across complex infrastructures is critical for ensuring transparency, accountability, and informed decision-making. As data-driven enterprises increasingly rely on hybrid cloud architectures, data lakes, and real-time pipelines, the complexity of tracking data movement and transformations grows exponentially. Traditional data lineage solutions, often based on static metadata extraction or rule-based approaches, are insufficient in dynamically evolving environments and fail to (...) provide granular, context-aware insights. This research introduces an AI-augmented, cognitive graph-based framework for autonomous data lineage, designed to enhance data traceability in large-scale and heterogeneous data ecosystems. The framework leverages cutting-edge machine learning (ML), natural language processing (NLP), and graph-based reasoning techniques to enable intelligent discovery, semantic interpretation, and continuous monitoring of data assets throughout their lifecycle. The core of the solution lies in the construction of a dynamic cognitive graph that represents relationships between datasets, processes, systems, and users, enriched with contextual annotations and temporal dimensions. Our architecture incorporates self-learning mechanisms, enabling adaptive lineage discovery and automated anomaly detection. By applying reinforcement learning and stream analytics, the framework not only maps data flows in real time but also evolves with system changes, schema variations, and business logic updates. It provides both forward and backward traceability, supports impact analysis, and enhances compliance auditing capabilities. Furthermore, our system is capable of processing both structured and unstructured metadata, employing advanced NLP models to extract implicit lineage information from data dictionaries, SQL queries, and documentation. The result is a holistic, intelligent, and scalable data lineage solution that reduces manual intervention, mitigates operational risks, and supports regulatory compliance frameworks such as GDPR, HIPAA, and SOX. Experimental evaluations conducted in hybrid cloud environments using tools such as Apache Kafka, Neo4j, Spark, and Python-based ML libraries demonstrate a significant improvement in lineage coverage, anomaly detection accuracy, and system scalability. Compared to conventional lineage tools, our AI-augmented framework delivers a 30% increase in traceability precision and a 40% reduction in manual effort required for lineage tracking and governance. This research lays the foundation for a new paradigm in data governance, where AI not only enhances observability but enables autonomous cognition within data infrastructure. The framework is poised to play a critical role in enabling data democratization, operational agility, and enterprise-wide data literacy. (shrink)

The shift to Microservices architecture and Application Programming Interface (API) - first development has transformed the landscape of software engineering, empowering development teams to create highly scalable, modular systems with agile, independent service deployment. However, the complexities of distributed architectures present unique challenges that traditional testing methodologies are often ill-equipped to address. These include managing inter-service dependencies, handling asynchronous communications, and ensuring data consistency across distributed nodes, all of which necessitate advanced testing strategies. This paper explores AI-enhanced testing (...) strategies specifically designed for Microservices and APIs, harnessing the power of machine learning, intelligent test generation, and anomaly detection. By leveraging machine learning models trained on production data, AI-driven approaches dynamically generate high-fidelity test cases and prioritize high-risk interactions, thereby optimizing test coverage and reducing test cycle duration. Additionally, intelligent test generation replicates real-world usage scenarios, creating adaptive tests that evolve with application changes. AI-powered anomaly detection adds a crucial layer of oversight, identifying deviations from expected behavior across interconnected services and flagging potential faults before they impact production. Furthermore, self-healing test mechanisms driven by AI continuously adjust and update test configurations as APIs evolve, maintaining relevance in high-speed CI/CD environments. This paper demonstrates how AI-driven testing elevates testing precision, enhances fault detection, and enables robust quality assurance in complex, API-driven systems. (shrink)

As organizations increasingly adopt cloud computing to enhance scalability, efficiency, and costeffectiveness, securing cloud-based applications and data storage has become a paramount concern. This shift has redefined the role of network engineers, who are now at the forefront of implementing and managing secure cloud infrastructures. This research paper examines the critical responsibilities of network engineers in safeguarding cloud environments, focusing on the challenges, strategies, and tools they employ to mitigate risks and ensure data integrity. The paper identifies (...) key challenges associated with cloud security, including data breaches, misconfigured systems, insecure interfaces, and insider threats. These vulnerabilities are further exacerbated by the dynamic and decentralized nature of cloud ecosystems, which often involve multi-tenant architectures and complex integrations. The study underscores the need for network engineers to adapt to these complexities by acquiring specialized skills in cloud security protocols and emerging technologies. A significant focus is placed on the implementation of robust network security frameworks, such as zero-trust architecture, which emphasizes the principle of "never trust, always verify." Network engineers play a crucial role in deploying access control measures, encryption mechanisms, and intrusion detection systems to safeguard sensitive data. Additionally, the research highlights the importance of continuous monitoring and threat intelligence to identify and address vulnerabilities proactively. The integration of automation and artificial intelligence (AI) in cloud security is another critical area explored in this study. Automation tools enable network engineers to streamline routine tasks, such as patch management and configuration audits, thereby reducing human error and response time. AI-driven solutions, on the other hand, enhance threat detection capabilities by analyzing large datasets for anomalous patterns and potential attacks. The paper also discusses the collaborative responsibilities of network engineers, who must work closely with developers, cloud providers, and organizational stakeholders to align security measures with business objectives. This includes implementing secure DevOps practices, ensuring compliance with industry regulations, and conducting regular security audits. Case studies of successful cloud security implementations are presented to illustrate the effectiveness of these collaborative approaches. In conclusion, this research emphasizes the evolving role of network engineers as pivotal defenders of cloud-based systems. It calls for continuous professional development to stay abreast of emerging threats and technologies, as well as fostering a culture of shared responsibility for security within organizations. By adopting a proactive and comprehensive approach, network engineers can significantly enhance the resilience of cloud infrastructures, ensuring the confidentiality, integrity, and availability of critical data and applications. This study aims to provide actionable insights and a strategic framework for network engineers and organizations striving to navigate the complexities of securing cloud environments. Through a combination of technological expertise, innovative tools, and collaborative practices, network engineers are positioned to address the unique challenges posed by cloud computing and play a central role in shaping the future of secure digital ecosystems. (shrink)

This research presents an automated system for predicting crop yield using the Random Forest Regression algorithm. The model leverages agricultural parameters such as soil composition, rainfall, temperature, and fertilizer usage to provide real-time and accurate yield predictions. A user-friendly web application developed with Python Flask allows for easy interaction, enabling farmers and agricultural professionals to input data and receive yield estimates. The results demonstrate the model’s reliability, achieving over 99% accuracy on test data. The (...) system's modularity supports future expansions, including real-time data integration and mobile deployment for widespread agricultural use. This project presents an AI-driven crop yield prediction system utilizing the Random Forest Regression algorithm. The system is designed to forecast agricultural yield based on various factors such as soil quality, rainfall, temperature, and historical crop data. Implemented using Python and deployed via a user-friendly Flask web interface, the application enables real-time yield prediction, empowering farmers and policymakers to make informed decisions. The model, trained on a diverse dataset, demonstrates high accuracy and robustness, handling non-linear data patterns effectively. By combining machine learning and intuitive design, this solution enhances agricultural planning, resource management, and overall productivity. (shrink)

: Natural Language Processing (NLP), a subset of Artificial Intelligence (AI), has emerged as a powerful tool in analyzing human language to detect underlying mental health issues such as depression and suicidal tendencies. By leveraging techniques in machine learning and deep learning, NLP systems can process and interpret textual data from diverse sources like social media, forums, and online journals to identify patterns and linguistic markers associated with mental health conditions. These markers include the use of emotionally charged words, (...) changes in writing style, expressions of hopelessness, isolation, or distress, which are critical indicators of a person's psychological state. The process typically involves data preprocessing, feature extraction using models such as TF-IDF or word embeddings (e.g., Word2Vec, GloVe), and classification using supervised learning techniques or deep learning models like BERT or LSTM. Understanding human emotions is a critical aspect of human-computer interaction, mental health diagnostics, and customer service applications. This project focuses on leveraging Natural Language Processing (NLP) techniques to analyze and interpret emotions expressed in textual data. By utilizing state-of-the-art machine learning models, including deep learning architectures such as recurrent neural networks (RNNs) and transformers, the project aims to develop models capable of detecting and classifying emotional states such as joy, sadness, anger, fear, and surprise from written content. The research investigates various aspects of emotion detection, including sentiment analysis, contextual understanding, and the role of linguistic features in emotion recognition. Additionally, it explores challenges like sarcasm, ambiguity, and cultural differences in emotional expression. The goal is to create an emotionaware system that can improve applications in fields such as automated customer support, mental health monitoring, and social media analysis, enabling more empathetic and responsive interactions between humans and machines. Through this work, we seek to enhance the understanding of emotions in textual communication, providing valuable insights into emotional dynamics and improving human-computer interactions. (shrink)

This paper addresses the critical role of automated data quality monitoring in Machine Learning Operations (MLOps) pipelines. As organizations increasingly rely on machine learning models for decision-making, ensuring the quality and reliability of input data becomes paramount. The paper explores various types of data quality issues, including missing values, outliers, data drift, and integrity violations, and their potential impact on model performance. It then examines automated detection methods, such as statistical analysis, machine learning-based anomaly detection, (...) rule-based systems, and data profiling. The integration of data quality monitoring into different stages of the MLOps pipeline is discussed, emphasizing continuous monitoring at data ingestion, pre-training validation, post- deployment drift detection, and feedback loops for model retraining. The paper also addresses key challenges in implementing automated data quality monitoring, including balancing precision and recall in anomaly detection, handling high-dimensional and unstructured data, managing false positives and alert fatigue, and adapting to evolving data distributions. By providing a comprehensive framework for automating data quality monitoring in MLOps pipelines, this paper aims to equip practitioners with the knowledge and strategies necessary to enhance the reliability and performance of machine learning systems in production environments. (shrink)

The escalating levels of environmental pollution and the depletion of global fossil fuel reserves have accelerated the transition toward sustainable transportation alternatives. Electric Vehicles (EVs) are emerging as a promising solution to mitigate carbon emissions and reduce national oil import dependencies. However, the growing adoption of EVs presents new challenges, particularly in terms of establishing a scalable and efficient charging infrastructure. This paper presents the framework and architecture of a next-generation communication system designed for real-time EV charging slot prediction (...) and online booking, based on the Internet of Things (IoT). A cloud-based platform is developed for intelligent Charging Station Management, capable of networking and handling multiple charging nodes. We propose a stochastic queuing model to simulate EV arrival patterns and service mechanisms at charging stations. Furthermore, we formulate an objective function that optimizes key performance metrics such as minimal queuing delay, optimal charging time, reduced travel distance, and low-cost energy utilization. The system's dynamic scheduling algorithm ensures efficient slot allocation, thereby minimizing wait times and preventing on-road EV breakdowns due to battery depletion. Real-time data acquisition and forecasting capabilities embedded within the cloud and IoT framework significantly enhance resource utilization and customer convenience. The proposed solution represents a step forward in achieving sustainable, cost-effective, and intelligent E-Mobility infrastructure suitable for smart city applications. (shrink)

Foundation models, particularly large language models (LLMs), are transforming how data engineering tasks are automated across domains. This paper explores the use of LLMs to automate the creation, management, and optimization of Extract-Transform-Load (ETL) pipelines in a domain-agnostic manner. We provide conceptual frameworks and practical strategies for integrating foundation models into the ETL lifecycle, and we highlight use cases where such models (via platforms like Amazon Bedrock) generate pipeline code, enhance data transformation quality, and adapt pipeline execution. Through (...) a review of related work and an illustrative implementation, we demonstrate that LLMs can translate natural language specifications into pipeline workflows, assist in data harmonization and quality control, and optimize pipeline performance with minimal human intervention. We discuss evaluation metrics for these LLM-driven pipelines and examine the challenges of reliability, scalability, and governance. The results indicate that foundational models offer a promising avenue to drastically reduce development time and improve the robustness of data pipelines, suggesting a paradigm shift in AI-assisted data engineering. (shrink)

The Real-Time Cyber Threat Detection and Response System is an intelligent security framework designed to proactively identify, analyze, and respond to cyber threats in real time. With the growing sophistication of cyberattacks targeting critical infrastructure, traditional static defense mechanisms are no longer sufficient. This system addresses that gap by leveraging machine learning algorithms and dimensionality reduction techniques such as Principal Component Analysis (PCA) to enable accurate and efficient threat detection. The system captures input data, such as (...) network traffic or log features, and processes it through a trained machine learning model to classify instances as normal or malicious. A pre-trained model, stored using Python's pickle module, evaluates the processed input, while PCA ensures that irrelevant or redundant features are minimized to boost performance and reduce computation time. The application is deployed as a web-based interface using Flask, providing functionalities like secure data upload, dataset preview, performance visualization, and threat prediction. Users can interact with the system in real time, upload network traffic datasets, and receive instant feedback on potential threats. The platform is built to support automated response mechanisms, which can be extended to alert generation, logging, or blocking malicious activities. (shrink)

In today’s data-intensive world, real-time insights have become essential for businesses and systems that demand timely and intelligent decision-making. Traditional batch processing techniques are increasingly inadequate for handling high-velocity data streams generated from IoT devices, social media platforms, financial transactions, and industrial systems. This paper presents an architectural approach to real-time data stream processing using Apache Kafka integrated with AI models to enable dynamic analytics and automated responses. The system leverages Kafka's distributed messaging capabilities for reliable (...) data ingestion and delivery, while AI models trained on historical and real-time data are used to extract meaningful patterns and predictions. The proposed solution is designed to ensure low-latency inference, scalable processing, and seamless integration with existing data infrastructure. (shrink)

Abstract -/- This paper introduces a structured resonance-based alternative to traditional entropy-driven DNA data storage. By replacing probabilistic base encoding with CODES-guided coherence architecture, we demonstrate how the Resonance Intelligence Core (RIC) drastically improves information density, fidelity, and long-term retrieval. Our system maps prime-phase logic directly into base-pair harmonics using PAS (Phase Alignment Score), enabling coherent memory encoding in DNA with error rates and costs significantly lower than stochastic methods. We present side-by-side simulations, coherence-anchored Verilog/Python code, (...) and a path to microfluidic hardware integration. -/- This work redefines biological memory not as entropic data retention but as coherence-anchored resonance—a philosophical shift with implications for epistemology, cognition, and AGI design. (shrink)

As applications increasingly handle sensitive and personal information, ensuring the security of this data has become a critical concern across industries such as finance, healthcare, and e-commerce. Traditional security mechanisms that rely on single-point protection models are no longer sufficient to mitigate the growing complexity and frequency of cyber threats. This paper presents a comprehensive Layered App Security Architecture aimed at safeguarding sensitive data through a multi-tiered defense approach. The proposed framework incorporates security controls at every architectural layer, (...) from the user interface to the backend data storage systems, aligning with the principles of defense-in-depth and zero trust. (shrink)

Using Agentic AI in concert with AWS Full Stack Development, this study offers a technically solid and scalable method to securely process financial data. AWS Lambda for serverless computation, Amazon S3 for scalable storage, AWS Glue for data classification and transformation, Athena for SQL-like querying, and QuickSight for interactive visualisation comprise the fundamental elements of the system. While Agentic AI modules enable autonomous decision-making abilities to regulate data integrity, recognise abnormalities, and adaptably react to compliance (...) violations, Custom AWS configuration rules are used for real-time compliance monitoring. Tight guidelines like PCI-DSS and GDPR enable secure cloud migration, improved auditability, and policy execution. These guidelines open the path for real-time, low-latency analytics on enormous financial data. Combining data-driven insight production with intelligent control will help to build a transparent and strong environment fit for modern financial activities, as the suggested approach shows. (shrink)

Data analysis and machine learning are increasingly essential in various industries; however, the complexity of existing tools creates barriers for non-technical users. This paper presents DataBuddy, a no-code tool designed to automate data analysis and machine learning processes through an intuitive graphical interface. DataBuddy integrates Python libraries like Pandas, Matplotlib, Seaborn, and Scikit-Learn, providing features such as automated exploratory data analysis (EDA), dynamic visualizations, and machine learning model training — all without requiring programming knowledge. The (...) tool allows users to upload datasets, select columns, visualize data, and train predictive models through simple checkboxes and dropdown menus. Performance metrics and analysis reports are generated automatically and saved in organized folders, ensuring accessibility and efficiency. DataBuddy empowers users from diverse backgrounds to derive insights, make data-driven decisions, and build machine learning models with minimal effort. The results demonstrate the tool’s effectiveness in simplifying complex workflows, reducing analysis time, and bridging the gap between technical and non-technical users. Despite the growing importance of data analysis and machine learning across industries, many existing tools remain inaccessible to non-technical users due to their reliance on coding expertise. DataBuddy is a no-code tool that overcomes these challenges by providing a user-friendly graphical interface that allows users to perform data preprocessing, exploratory data analysis (EDA), visualization, and machine learning tasks without writing any code. The platform integrates Python libraries such as Pandas, Matplotlib, Seaborn, and Scikit-Learn to automate complex processes while offering simplicity through dropdowns, checkboxes, and guided workflows. Users can upload datasets, manipulate and analyze data, and train predictive models with minimal effort. DataBuddy aims to empower professionals from non-technical backgrounds to make informed, data-driven decisions with ease, significantly reducing the time and resources needed for comprehensive analysis. (shrink)

The Internet of Things (IoT) has revolutionized the way we interact with the world by enabling devices to collect, process, and exchange data in real time. However, IoT networks often face challenges related to latency and scalability when large amounts of data are processed centrally in cloud-based infrastructures. Edge computing, which brings computation and data storage closer to the source of data generation, is emerging as a solution to address these challenges. By performing computation at (...) the edge of the network (e.g., on local IoT devices, gateways, or nearby servers), edge computing reduces latency, optimizes bandwidth usage, and enables faster decision-making. Edge computing in IoT networks enhances scalability by offloading data processing from centralized cloud servers, making it feasible to handle large volumes of data from numerous IoT devices simultaneously. It also addresses latency concerns by allowing data to be processed locally, thereby reducing the time required for communication between devices and the cloud. This is particularly beneficial in real-time applications like autonomous vehicles, smart cities, healthcare, and industrial automation. Despite its potential, implementing edge computing in IoT networks introduces several challenges, including resource limitations on edge devices, data security and privacy concerns, and the complexity of managing distributed systems. This paper explores the role of edge computing in improving the performance of IoT networks, analyzing its advantages and challenges, and presenting case studies from various domains. Moreover, it discusses various architectures, technologies, and strategies for effective edge computing deployment in IoT ecosystems. (shrink)

The Internet of Things (IoT) wireless LAN in healthcare has moved away from traditional methods that include hospital visits and continuous monitoring. The Internet of Things allows the use of certain means, including the detection, processing and transmission of physical and biomedical parameters. With powerful algorithms and intelligent systems, it will be available to provide unprecedented levels of critical data for real-time life that are collected and analyzed to guide people in research, management and emergency care. This chapter provides (...) a quick overview of IoT features and how they relate to wireless discovery and technology to deploy the medical applications you need. In the world, the revolution in any industry is to connect your products and devices to the Internet and make them independent and remotely connected, so that anyone can use and view them from anywhere and anytime. The Internet of Things provides us with a home automation system that uses smart devices to overcome this obstacle, allowing us to easily manage our appliances. A smart city is a vision to integrate a variety of information and communication solutions for residents with essential services, such as smart parking on all streets. The main motivation for using the Internet for parking objects is simply collecting data to get free parking. The IoT-based RTSSPS architecture is divided into three parts: a WSN-based smart street parking module, an IoT-based smart street parking module, and an IoT-based smart street parking module. IoT-based cloud with street parking algorithm, rating and future directions. (shrink)

The proliferation of Internet of Things (IoT) devices has introduced a range of opportunities for enhanced connectivity, automation, and efficiency. However, the vast array of interconnected devices has also raised concerns regarding cybersecurity, particularly due to the limited resources and diverse nature of IoT devices. Intrusion detection systems (IDS) have emerged as critical tools for identifying and mitigating security threats. This paper proposes a Multi-Layer Intrusion Detection Framework for IoT systems, leveraging Ensemble Machine Learning (EML) techniques to improve the accuracy (...) and effectiveness of detecting intrusions. The proposed framework integrates multiple layers of defense, including data preprocessing, feature extraction, classification, and post-processing, to provide a robust solution to IoT security challenges. This research explores the application of EML methods such as Random Forests, AdaBoost, and Gradient Boosting, combining them into a unified model to detect and mitigate various types of attacks in IoT environments. Experimental results demonstrate the superiority of the proposed framework in terms of detection accuracy, false positive rate, and computational efficiency. (shrink)

The Web Article Summarizer is an advanced NLP-based application that leverages state-of-the-art transformer models, including BART for abstractive summarization and BERT for contextual understanding, combined in a dual-encoder architecture to generate accurate and coherent summaries from lengthy articles. Built using the Flask framework, the system features a scalable RESTful API that enables seamless integration with web and mobile platforms, while its multi-stage preprocessing pipeline ensures optimal text normalization and feature extraction. Evaluated using ROUGE metrics, the solution demonstrates superior performance (...) in maintaining content integrity and domain-specific terminology handling compared to traditional methods. Designed for researchers, students, and professionals, this tool addresses information overload challenges by delivering concise yet comprehensive summaries, with future enhancements planned for multilingual support via mBART, cloud deployment for real-time processing, and JWT authentication for secure API access, making it a versatile and powerful solution for automated text summarization across diverse applications. (shrink)

A contemporary cybersecurity strategy known as the Zero Trust Security Model aims to eradicate implicit trust and ensure uninterrupted authentication for every user, device, and application. Zero Trust security models diverge from conventional perimeter-based security by anticipating threats within and beyond the network, necessitating rigorous authentication and limitations on user access privileges. Components like Multi-Factor Authentication (MFA), Zero Trust Network Access (ZTNA), micro-segmentation, and AI-driven threat detection strengthen security by reducing vulnerabilities and stopping unauthorized access. The integration of Artificial (...) Intelligence and Machine Learning enhances Zero Trust by facilitating real-time risk assessment and automated reaction to security incidents. Implementation of Zero Trust security comes with its own set of difficulties, such as the intricacies of deployment, problems with integration, and worries about the user experience. As cyber threats become increasingly complex, organizations must place a high priority on implementing Zero Trust to guarantee robust security in cloud-based and hybrid work environments. This study examines the core concepts of Zero Trust, its fundamental technologies, deployment methods, and practical examples to demonstrate its efficacy in safeguarding digital resources. By implementing a Zero Trust model, businesses can substantially lower their cybersecurity vulnerabilities and enhance adherence to regulatory requirements. The research indicates that Zero Trust is a fundamental security strategy for contemporary businesses, providing sustained defense against progressively sophisticated threats. (shrink)

The project Automated Document Classification using YOLOv3 and Tkinter for Real-Time Segmentation presents an innovative solution for automating the extraction of visual content from PDF documents using advanced computer vision techniques. Leveraging the power of the YOLOv3 object detection model, combined with pdf2image, OpenCV, and Tkinter, the system efficiently converts PDF pages into high-resolution images, detects and labels objects within them, and presents the results through a user-friendly graphical interface. This approach significantly reduces manual labor and error in (...) tasks like document annotation, visual data extraction, and image analysis. The system's ability to handle complex layouts and detect multiple object classes demonstrates its robustness and scalability. It proves especially beneficial in fields such as legal document processing, educational content digitization, and intelligent archival systems. With further enhancements like OCR integration, cloud deployment, and support for semantic understanding, the solution has strong potential to evolve into a powerful AI driven document analysis platform. (shrink)

With the rapid development of communication technology, the explosive growth of mobile and IoT devices, and growing requirements for real-time data processing, a new paradigm of computing, Edge Computing, has appeared. It moves computing power in the direction of data sources to mitigate latency, bandwidth usage, and dependence on cloud computing. In parallel, Artificial Intelligence (AI) has progressed notably with deep learning technology, highly optimized hardware, and distributed computing paradigms to yield smart applications of high computational loads. Nonetheless, (...) the huge amounts of data generated on the network edge impose heavy challenges in managing data, network optimization, and implementing AI models. This has pushed the convergence of Edge Computing and AI, which has led to a new research area called Edge Intelligence. Edge Intelligence is further divided into two broad categories • AI for Edge (Intelligence-enabled Edge Computing) – This is concerned with augmenting Edge Computing architectures with AI-based methods, including resource management, task scheduling, computation offloading, and network optimization. • Edge AI (Artificial Intelligence on Edge) – This involves executing AI models on edge devices directly, enabling local training and inference with minimal dependence on the cloud, thereby improving privacy, efficiency, and real-time processing. This paper provides an overview of Edge Intelligence, including fundamental concepts, future technologies, and research directions. We identify critical challenges such as efficient deployment of AI models, decentralized AI learning through federated learning, and edge-centric accelerations of domain-specific hardware, and discuss how Edge Intelligence has the potential to transform domains like autonomous systems, smart cities, factory automation, and wireless networks. This paper documents the present trend and future path to act as the basis for researchers, engineers, and industry players seeking to improve the topic of AI-driven Edge Computing. (shrink)

The rapid expansion of digital data has made metadata crucial in organizing, managing, and retrieving information effectively. Machine learning (ML) offers powerful tools to automate and enhance metadata processing, leading to improved accuracy, scalability, and efficiency. This paper explores how ML algorithms are applied to metadata extraction, classification, annotation, and enrichment. We review current research, examine the methodologies employed, and present a comparative analysis of techniques. Our findings suggest that supervised learning models, especially deep learning architectures, outperform traditional rule-based (...) systems in most scenarios. However, challenges remain in terms of interpretability, bias, and data quality. (shrink)

This paper presents the design and real-world implementation of an efficient, web-based Internship Management System (IMS) developed using the Django framework. The system addresses the limitations of traditional manual internship processes, which are often fragmented, time-consuming, and prone to communication gaps. The proposed IMS provides a centralized platform that integrates all key stakeholders—interns, human resources (HR), and hiring managers—thereby streamlining the entire internship lifecycle from application submission to project assignment, performance evaluation, and certificate generation. The system incorporates (...) role-based access control, secure data handling, and automated workflows for tasks such as application review, project allocation, and intern evaluation. Through features such as email notifications, document management, and status tracking, the IMS minimizes administrative overhead and improves transparency and responsiveness. The implementation has been tested for functionality, usability, and efficiency, and preliminary results demonstrate significant improvements in intern onboarding speed, administrative coordination, and overall stakeholder satisfaction. This paper discusses the system architecture, development methodology, deployment details, and its effectiveness in a real-world academic environment, while also exploring its scalability and potential for future enhancements. (shrink)

We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of (...) data sources, types, models, and modalities (including text, images, audio, and signals). The result of applying this strategy to a given body of data is an evolving Dataspace that allows the application of a variety of integration and analytic processes to diverse data contents. We conceive semantic enhancement (SE) as a lightweight and flexible process that leverages the richness of the structured contents of the Dataspace without adding storage and processing burdens to what, in the intelligence domain, will be an already storage- and processing-heavy starting point. SE works not by changing the data to which it is applied, but rather by adding an extra semantic layer to this data. We sketch how the semantic enhancement approach can be applied consistently and in cumulative fashion to new data and data-models that enter the Dataspace. (shrink)

Ensuring cloud resiliency through engineering is essential for maintaining high availability, fault tolerance, and disaster recovery within contemporary cloud infrastructures. As more businesses move towards multi - cloud environments, maintaining system reliability and efficiency while also controlling costs takes centre stage. This study delves into optimal strategies for bolstering cloud reliability via automated failover systems, real - time data duplication, load distribution, and self - restoring networks. The analysis focuses on strategies for disaster recovery, cost - effective resource (...) management, and enhancing security resilience to minimize potential risks. The report draws attention to the difficulties involved in integrating multiple cloud systems, maintaining data consistency, and dealing with cyber threats. It also explores the development of new technologies like AI - powered automation, edge computing, and predictive analytics for identifying potential failures. The study offers valuable insights into how to optimally configure cloud infrastructure to achieve the highest levels of efficiency and dependability. Future developments in autonomous cloud systems, quantum encryption, and eco - friendly computing models to enhance cloud robustness. This paper provides a detailed guide for companies seeking to construct reliable cloud infrastructure that maintains operational stability and reduces the frequency of service interruptions. (shrink)

The purpose of this research is to design a system using machine learning to automate sign language gesture recognition and facilitate unproblematic interaction between the hearing/speech impaired community and society as a whole. This research works towards solving the communication disability faced by the deaf and mute community through a computer vision framework for recognizing hand gestures and mapping them to relevant text or speech. The work employs a rich dataset of static American Sign Language (ASL) hand signs and (...) uses Convolutional Neural Networks (CNNs) for efficient feature extraction and gesture classification. The suggested model is trained and evaluated on a huge collection of labeled gesture images and exhibits high accuracy with robust generalization to novel data. One of the primary contributions of the work is the implementation of the trained model into an interactive, realtime graphical user interface developed with Python's Tkinter library. The interface captures live input from a webcam, processes each frame to identify gestures, and shows the identified character on the screen. The results of the evaluation confirm the ability of CNNs to accurately identify ASL alphabets with little delay. The proposed system presents an effective and scalable approach towards real-time sign language interpretation, supporting inclusiveness and accessibility of the disabled community. The present research also creates a scope for extension in development, i.e., inclusion of dynamic gesture detection and portability to mobile or embedded platforms to provide enhanced use in real-life situations. (shrink)

As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to testing, deployment, (...) and monitoring. Approaches for implementing continuous integration and continuous deployment (CI/CD) pipelines tailored for ML workflows are discussed, enabling efficient and repeatable model updates and deployments. The paper emphasizes the criticality of implementing comprehensive monitoring and observability mechanisms to track model performance, detect drift, and ensure the reliability and trustworthiness of deployed models. The paper also addresses the challenges of managing model versioning and governance at scale, including techniques for maintaining a centralized model registry, enforcing access controls, and ensuring compliance with regulatory requirements. The paper aims to provide a comprehensive guide for organizations seeking to establish scalable and robust MLOps practices, enabling them to unlock the full potential of machine learning while mitigating risks and ensuring responsible AI deployment. (shrink)

Cloud storage has become an essential component of modern data management, but increasing storage costs present a significant challenge for organizations. Conventional tier-based storage systems necessitate manual distribution, resulting in potential inefficiencies and increased expenses. This study presents a forecasting model for intelligent data tiering, utilizing machine learning to automate storage selections based on access frequency. Utilizing historical usage patterns, the model automatically categorizes data into three storage tiers: hot, warm, (...) or cold, thereby balancing cost-effectiveness and data retrieval speed. The suggested framework incorporates predictive analysis to decrease operational costs and enhance the use of cloud resources. The experimental data show that the model is highly effective in predicting data access patterns, resulting in significant financial savings when compared to traditional storage management methods. Performance evaluations show that predictive tiering reduces latency and improves scalability. This research offers a practical, data-driven strategy for cloud service companies and businesses looking to improve their storage infrastructure. Organizations can achieve a sustainable balance between cost savings and ensuring efficient long-term data management practices. (shrink)

The exponential complexity of contemporary Life Insurance applications - shaped by stringent regulatory compliance requirements, dynamic customer - centric innovation, and expansive product diversification - has magnified the challenges of maintaining traditional test automation frameworks. These frameworks often falter under the weight of frequent application updates, evolving user interfaces (UI), and fluctuating backend integrations, leading to brittle test scripts and heightened maintenance costs. Self-healing test automation frameworks offer a cutting-edge, AI-driven solution to this paradigm. By leveraging advanced machine learning (ML) (...) algorithms and intelligent heuristics, these frameworks autonomously detect, diagnose, and remediate test failures caused by changes in application elements, workflows, or environments. Through real-time adaptation to UI modifications, API updates, and evolving test environments, self-healing frameworks ensure uninterrupted testing cycles with minimal manual intervention, significantly improving efficiency and scalability. This paper provides a comprehensive exploration of the architectural design and operational mechanisms of self healing test automation frameworks within the context of Life Insurance applications. Core focus areas include their ability to dynamically recalibrate element locators and adapt test scripts during runtime, thereby enhancing test coverage, optimizing maintenance workflows, and reducing the overall defect detection lifecycle. Through an in-depth analysis, the paper highlights the integration of AI-powered locator optimization engines, the deployment of predictive analytics for early anomaly detection, and the orchestration of continuous self-healing pipelines in CI/CD ecosystems. Detailed case studies from the Life Insurance domain illustrate tangible benefits such as accelerated regression cycles, substantial cost reductions, and improved system resilience. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying (...) the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into (...) the dynamics of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

The financial industry is increasingly embracing cloud-native technologies to ensure scalable, reliable, and secure deployment of AI/ML models for critical applications such as fraud detection, risk assessment, and real-time customer analytics. Traditional on-premise or monolithic deployment approaches limit agility and scalability, particularly in environments requiring high-frequency data processing and compliance adherence. This paper explores a comprehensive framework for cloud-native model deployment tailored specifically for financial applications, incorporating containerization, orchestration, CI/CD pipelines, and microservices architecture.

: In the evolving landscape of data center networks, agility, automation, and scalability have become essential components for modern enterprise IT infrastructures. Cisco's Application Centric Infrastructure (ACI) represents a significant shift toward intent-based networking by aligning network operations with application requirements. This paper explores how Cisco ACI enhances data center performance, simplifies network management, and supports rapid application deployment through a software-defined networking (SDN) approach Cisco ACI is built around a policy-driven model that integrates software and (...) hardware components to provide centralized automation and real-time application visibility. Key components such as the Application Policy Infrastructure Controller (APIC), ACI fabric, and endpoint groups (EPGs) form the foundation of this architecture. These elements work together to deliver dynamic provisioning, efficient resource allocation, and seamless scalability, making ACI ideal for complex, multitenant environments. The study evaluates the deployment process, operational efficiencies, and performance improvements associated with ACI, including reduced provisioning times, improved security posture through microsegmentation, and enhanced troubleshooting capabilities. It also examines the role of ACI in supporting hybrid cloud environments and its integration with DevOps practices and orchestration tools. The results underscore Cisco ACI’s value in transforming traditional data center operations into a more agile, secure, and responsive framework. With its focus on application-centricity, automation, and programmability, ACI offers a strategic pathway for organizations seeking to modernize their data centers while aligning network behavior with business intent. (shrink)

Cryptocurrency price forecasting is a challenging task due to market volatility and unpredictable investor behavior. This project introduces a sentiment-driven machine learning approach to improve prediction accuracy. Sentiment data is collected from platforms like Twitter and financial news using Natural Language Processing (NLP) techniques. These sentiments are quantified and combined with historical price data to form a robust dataset. An LSTM (Long Short-Term Memory) model is used for its ability to learn temporal dependencies in timeseries data. (...) The model is trained to forecast future price movements by understanding both technical trends and public sentiment. Alerts are generated based on significant sentiment-influenced price changes to assist users in decisionmaking. The system utilizes Python, TensorFlow, and relevant APIs for data extraction and preprocessing. React is used for building the front-end interface, allowing interactive user experiences. The approach bridges the gap between numerical analysis and behavioral finance. Evaluation metrics such as RMSE and MAE are used to assess the model’s performance. Results show that incorporating sentiment data improves forecast accuracy. The application is scalable and adaptable for real-time use. Future enhancements include live data integration and predictive analytics for trade suggestions. This project highlights the power of combining sentiment analysis with deep learning in financial forecasting.. (shrink)

The rapid evolution of the financial sector, particularly in banking and fintech, necessitates continuous innovation in financial product development and testing. However, challenges such as data privacy, regulatory compliance, and the limited availability of diverse datasets often hinder the effective development and deployment of new products. This research investigates the transformative potential of AI-driven synthetic data generation as a solution for accelerating innovation in financial product development. Synthetic data, generated through advanced AI techniques such as Generative (...) Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, can simulate real-world financial scenarios with a high degree of fidelity while preserving privacy and compliance standards. The use of synthetic data enables financial institutions and fintech companies to conduct rigorous testing, modeling, and validation of new products and services without relying on sensitive customer data. By generating realistic yet artificial datasets, organizations can explore a broader range of scenarios, including rare or extreme market conditions, thus enhancing the robustness and reliability of their financial models. This paper provides a comprehensive analysis of the underlying methodologies for synthetic data generation, focusing on their application to financial product development. It delves into the specific architectures and frameworks used in generating synthetic data, including GANs, VAEs, and synthetic minority over-sampling techniques (SMOTE), and examines their respective advantages and limitations. The paper also addresses the critical issue of ensuring the quality and utility of synthetic data, emphasizing metrics such as statistical similarity, privacy preservation, and applicability to real-world use cases. The discussion extends to the ethical and regulatory implications of deploying AI-driven synthetic data in finance, highlighting the need for transparent and explainable AI models to ensure trust and compliance. Moreover, the research explores practical case studies where financial institutions and fintech firms have successfully implemented synthetic data to develop and test new products, demonstrating significant reductions in time-to-market and development costs. One of the key contributions of this research is the exploration of how AI-driven synthetic data generation can facilitate the development of innovative financial products such as algorithmic trading strategies, risk management tools, credit scoring models, and fraud detection systems. By simulating diverse market behaviors and customer interactions, synthetic data enables the fine-tuning of algorithms and models to achieve higher accuracy and performance. Additionally, the paper discusses the integration of synthetic data generation into existing financial data ecosystems, proposing a framework for leveraging hybrid datasets that combine synthetic and real data to optimize model training and validation. The potential for synthetic data to drive collaborative innovation in finance is also considered, as it allows multiple stakeholders, including banks, fintech startups, and regulators, to share and analyze data without compromising confidentiality or privacy. The research also addresses the limitations and challenges associated with synthetic data generation in the financial domain, including issues related to data representativeness, overfitting, and the potential misuse of synthetic datasets. It emphasizes the need for ongoing research to develop more sophisticated algorithms that can generate highly realistic and diverse financial data. Furthermore, it identifies areas for future exploration, such as the use of federated learning and differential privacy techniques to enhance the security and privacy of synthetic data generation processes. The findings of this paper underscore the importance of AI-driven synthetic data generation as a catalyst for innovation in banking and fintech, providing a secure, scalable, and cost-effective means to develop, test, and validate new financial products and services. As the financial industry continues to evolve, the role of synthetic data in shaping the future of financial product development will become increasingly critical, paving the way for more efficient and innovative financial solutions. (shrink)

Hate speech detection is a critical aspect of online content moderation, ensuring that digital platforms remain safe and inclusive. With the exponential rise of social media, harmful content such as hate speech and offensive language has increased, necessitating automated solutions for effective moderation. This project employs Natural Language Processing (NLP) and Machine Learning (ML) techniques to classify tweets into three categories: Hate Speech, Offensive Speech, and No Hate or Offensive Speech. By leveraging a Decision Tree Classifier, the system efficiently (...) detects and categorizes harmful content while reducing manual intervention. The methodology involves data preprocessing, feature extraction using CountVectorizer, and training a classification model to achieve high accuracy. The proposed system overcomes the limitations of traditional keyword-based filtering by improving context awareness and scalability. The implementation is designed to process large volumes of data, making it highly suitable for real-world applications. This approach enhances digital safety, minimizes human effort in moderation, and ensures compliance with ethical standards. Future improvements may include the integration of deep learning models like LSTMs or Transformers and real-time social media API monitoring to enhance accuracy further. This project contributes to the growing need for robust and automated hate speech detection solutions in the digital era. (shrink)