By embracing AI-driven HR analytics, organizations can anticipate market shifts, prepare their workforce for future challenges, and stay ahead of the competition. This study outlines the essential components of AI-driven HR analytics, demonstrates its impact on workforce agility, and concludes with potential future enhancements to further optimize HR functions. Key words: Predictive Workforce Analytics, Talent Optimization, Machine Learning in.

Our findings reveal that implementing accelerated computing can achieve substantial improvements, often reducing computation times by more than 60% compared to traditional sequential methods. This paper details the experimental setup, including algorithm selection and parallelization techniques, and discusses the role of memory bandwidth and latency in achieving optimal performance. Based on the analysis, we propose a streamlined methodology to guide the deployment of accelerated computing frameworks in various industries. Concluding with a discussion on future directions, we highlight potential advancements in (...) hardware architectures and software optimizations that could further augment computational efficiency and scalability in accelerated computing. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, Support (...) Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

Migrating legacy database applications to modern cloud-based solutions, such as Snowflake, is becoming essential for organizations aiming to leverage scalable, efficient, and cost-effective data solutions. Legacy databases, typically bound to on-premises infrastructure, often lack the flexibility required by contemporary data analytics and storage needs. Snowflake, a cloud-native data platform, provides a versatile environment that enables efficient data storage, sharing, and processing. This research examines a structured methodology for migrating legacy database applications to Snowflake, addressing key stages from initial assessment to (...) data transformation, integration, and performance optimization. A systematic, step-by-step migration process is implemented to minimize risks, reduce downtime, and ensure data integrity. (shrink)

Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and (...) usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. (shrink)

The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time.

The sample size of the study was conducted in Tiruchirappalli city with 75 respondents through Non-Probability Random Sampling Method. The tools and techniques used were simple percentage, chi-square and ANOVA. The obtained result of the study that majority of the women prefer TVS Scooty and most of the respondents prefer two wheelers due to convenient while driving and majority of the respondents have great impact on colour and model prefer the vehicle. New inventions and designs were introduced to meet the (...) requirements of the current day affaires. (shrink)

This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements (...) a. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We (...) examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. (shrink)

The performance of the deep learning models is evaluated using metrics such as accuracy and error rate. Accuracy measures how well the models are able to reconstruct the facial features compared to the original images, while the error rate indicates the frequency of incorrect reconstructions. By quantifying these metrics, the project can assess the effectiveness of each algorithm in reconstructing distorted faces. The accuracy levels of VGG19 and 3D CNN are compared using the performance metrics.

This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage (...) needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by (...) training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like Mean (...) Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. (shrink)

The scope of the project encompasses the design, development, and implementation of AI-driven functionalities such as interest assessment, skill analysis, resume building, and personalized recommendations. The methodology involves data collection through user inputs, preprocessing of data for analysis, and the creation of a robust system architecture comprising frontend interfaces, backend servers, and database management. The implementation of the application involves a comprehensive technology stack, including Python for AI algorithms, TensorFlow for ML models, React.js for front end development, Flask for backend (...) services, and PostgreSQL for database management. (shrink)

The routing process then prioritizes nodes with higher energy levels, reducing premature node failure. A novel energy-aware transmission algorithm ensures that data packets are transmitted over the most energy-efficient paths, thus extending the overall network longevity. Simulation results demonstrate that the proposed protocol achieves significant improvements in energy consumption, data transmission reliability, and network lifetime compared to traditional methods. The conclusion discusses the potential future enhancements, including adaptive algorithms that can further reduce energy consumption in large-scale UWSNs.

By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, the (...) system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

We propose a systematic workflow for automating the storage and retrieval process, starting from the identification of the stock to its precise placement and retrieval within the storage facility. The design also addresses potential challenges such as robot mobility, collision avoidance, and space optimization. Performance metrics, including accuracy, time efficiency, and system scalability, are measured using simulation-based experiments in a controlled environment. The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the way (...) for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and integration with Internet of Things (IoT) for real-time data analysis and continuous system improvement. (shrink)

The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future (...) enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)