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Artificial Intelligence Application in Human Resources Information Systems for Enhancing Output in Agricultural Companies
Artificial intelligence (simulated intelligence) apparatuses like master systems, normal language handling, discourse acknowledgment, and machine vision have changed how much work in agribusiness, yet in addition its nature. This is on the grounds that the total populace and interest for food are developing, and the climate and water supply are evolving. Specialists and researchers are presently moving towards involving new IoT advances in shrewd cultivating to assist ranchers with utilizing manmade intelligence innovation to improve seeds, crop security, and composts. This will get ranchers more cash-flow and help the pay of the country in general. In agribusiness, computer-based intelligence is making its mark in three primary regions: checking soil and harvests, prescient examination, and cultivating robots. Along these lines, ranchers are utilizing sensors and soil tests increasingly more to accumulate information that can be utilized by ranch the board apparatuses for additional exploration and examination. This book adds to the field by giving an outline of how computer-based intelligence is utilized in agribusiness. It begins with a prologue to simulated intelligence, including a survey of all the computer-based intelligence techniques utilized in horticulture, similar to AI, the Web of Things (IoT), master systems, picture handling, and PC vision. 2024 IEEE. -
Artificial intelligence attitudes and resistance to use robo-advisors: exploring investor reluctance toward cognitive financial systems
Introduction: The study investigates resistance towards Financial Robo-Advisors (FRAs) among retail investors in India, grounded in innovation resistance theory. The study examines the impact of functional barriers and psychological barriers on resistance to FRAs, while considering users attitudes towards Artificial Intelligence (AI) as a moderator. It further evaluate the influence of such resistance on users intentions to use and recommend FRAs. Methods: Utilizing purposive sampling data was collected from 409 investors and further analyzed using structural equation modelling. Results: The findings revealed that all barriers under study, expect value barrier, substantially derive resistance towards robo-advisors, with inertia being the strongest determinant. Further, this resistance impedes both the intention to use FRAs and to recommend them. Moderation analysis results finds that users attitude towards AI significantly weakens the influence of inertia, overconfidence bias and data privacy risk on resistance, with no such impact on other relationships. Discussion: Overall, the study enriches IRT in Fintech context and provides theoretical and practical insights to enhance FRAs adoption in emerging markets. Copyright 2025 Verma, Schulze, Goswami and Upreti. -
Artificial Intelligence Based Automatic Question Paper Generation Using Natural Language Processing
Question paper generation is a crucial task in education, where the objective is to design an assessment that effectively evaluates students knowledge and understanding of various subjects. Traditional methods of question paper generation can be exceedingly difficult, time-consuming, and inappropriate and may not be fully optimized. They ensure a comprehensive assessment of students knowledge. The system also offers the flexibility to customize question papers based on specific preferences and requirements. This research introduces a comparative approach to question paper generation using Latent Semantic Analysis (LSA), Word Embedding, and Sequence-to-Sequence (Seq2Seq) models, leveraging the power of Artificial Intelligence (AI) and Natural Language Processing (NLP). This model compares their Semantic Representation Quality, Context Understanding, and Computational Complexity. Comparing these techniques shows that LSA offers simplicity but may lack precision, while word embedding balances complexity and semantic understanding. Seq2Seq models, despite their complexity, provide contextually rich mappings with the highest degree of fine-tuning potential. This comparative analysis underscores the importance of understanding the nuances and trade-offs of each approach, enabling educators to make informed decisions in adopting these technologies to enhance educational practices and student learning experiences. A few modules are included in this system, including the admin module, add user, subject selection, question entry, question management, paper management and difficulty level. By capitalizing on the capabilities of LSA, Seq2Seq models, and word embedding, educators can revolutionize the process of question paper generation, ultimately leading to more effective and impactful student learning outcomes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Artificial Intelligence Based Enhanced Virtual Mouse Hand Gesture Tracking Using Yolo Algorithm
Virtual mouse technology has revolutionized human computer interaction, allowing users to interact with digital environments without physical peripherals. The concept traces back to the late 1970s, and over the years, it has evolved with significant advancements in computer vision, motion tracking, and gesture recognition technologies. In recent times, machine learning techniques, particularly YOLOv8, have been integrated into virtual mouse technology, enabling accurate and swift detection of virtual objects and surfaces. This advancement enhances seamless interaction, intuitive hand gestures, and personalized virtual reality experiences tailored to individual user preferences. The proposed model, EHT (Enhanced Hand Tracking), leverages the power of YOLOv8 to address the limitations of existing models, such as Mediapipe. EHT achieves higher accuracy in hand tracking, real-Time hand gesture recognition, and improved multi-user interactions. It adapts to users' unique gestures over time, delivering a more natural and immersive computing experience with accuracy rates exceeding those of Mediapipe. For instance, across multiple sample datasets, EHT consistently outperformed Mediapipe in hand tracking accuracy. In Sample Dataset 1, EHT demonstrated an accuracy of 98.3% compared to Mediapipe's 95.65%. Similarly, in Sample Dataset 2, EHT achieved an accuracy of 99.35%, surpassing Mediapipe's 94.63%. Even in Sample Dataset 3, EHT maintained its superiority with an accuracy of 98.54 %, whereas Mediapipe achieved 98.26%. The successful implementation of EHT requires a custom dataset and optimization techniques to ensure efficiency on virtual reality hardware. EHT model is anticipated redefining how users interact with digital environments, unlocking new possibilities for intuitive and immersive computing experiences. 2023 IEEE. -
Artificial Intelligence Based Recruitment Prediction and Sentiment Analysis for Enhanced HR Efficiency
In the present era of data-driven organizational environment, the practice of Human Resource Management (HRM) has become increasingly reliant on intelligent Decision-Support Systems (DSS). This study develops a multifaceted two-pipeline model of Predictive Modelling (PM) and Sentiment Analysis (SA) to enhance workforce analytics capabilities. A publicly available HRM analytic dataset is used to train supervised classification models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), as well as an ensemble model that integrates these classifiers. These approaches use structured data to predict employee attrition based on features such as age, job role, experience, and job satisfaction. The unstructured textual data sources, including resumes and employee reviews, are handled using state-of-the-art Natural Language Processing (NLP) such as tokenization, Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations as Transformers (BERT)-based embeddings. The new Mathematically Modified Robustly Optimized BERT Pretraining (MM-RoBERTa) is proposed for extracting the PM and SA. All the models are evaluated using k-fold Cross-Validation (CV) and standard evaluation measures, namely Accuracy, F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), and Mean Absolute Error (MAE). The ensemble model achieves a predictive accuracy of 91.3%, and MM-RoBERTa outperforms existing SA with an accuracy of 93.1 %. The combination of predictive and affective insights is of practical use in fine-tuning talent retention, empowering HRM professionals to make informed decisions based on objective performance indicators and subjective emotional states. 2025 The Authors. -
Artificial Intelligence based Semantic Text Similarity for RAP Lyrics
Data mining is the primary method of gathering large volumes of knowledge. To make use of such data to implementation requires the use of effective machine learning strategies. Semantic Textual Similarity is one of the primary machine learning strategies. At its core, semantic textual similarity is the identification of words with similar context. Initial work in STS involved text reuse, word search among others. The proposed research work uses a specific method of determining textual similarity using Google's Word2Vec framework and the Continuous-bag-of-words algorithm for identifying word similarity in rap records. A large data set consisting of over 50,000 rap records is used. The key aspect of proposed methodology is to determine the words with similar context and cluster them into different word clusters also called bags. To achieve the desired result, the dataset is first processed to obtain the features. Once the features are selected, a model is generated by passing the data onto the Word2Vec framework. The research work on semantic textual similarity was repeated across three different runs, with the data set size changing in every run. At the end of each the accuracy of similarity obtained by the model was determined. The current research work has achieved average accuracy as 85%. 2020 IEEE. -
Artificial Intelligence based Smart Pneumatic Tools for Industrial Applications
This study presents the development and evaluation of a next-generation pneumatic instrument integrated with intelligent technologies aimed at enhancing operational efficiency, adaptability, and sustainability in advanced manufacturing environments. The system features smart sensors, adaptive learning algorithms, and real-time adjustable control mechanisms, enabling it to function effectively under extreme operating conditions. It handles fluid velocities up to 15.2 m/s and pressures as high as 720 kPa, demonstrating robust structural integrity and reliability. Notably, the instrument maintains precise control over material deformation with an accuracy of 0.05 mm, even under mechanical stress levels reaching 180 MPa and at a Reynolds number of 350,000. The embedded smart sensors facilitate instantaneous responsiveness to fluctuations in material behavior, dynamically optimizing both force application and energy efficiency. This results in a significant 30% reduction in power consumption, with operational power decreasing from 280 W in high-pressure scenarios to just 150 W under standard conditions. Furthermore, the tool exhibits superior thermal management, maintaining operational temperatures below 65 C. Its self-calibrating functionality, driven by intelligent algorithms, ensures consistent output, minimized error margins, and enhanced safety over extended use. Compared to traditional electrically driven systems, this intelligent pneumatic tool offers a more sustainable and cost-effective solution by reducing energy demand and extending service life. The integration of advanced sensing and control systems transforms conventional pneumatic tools into adaptive, high-performance devices suitable for modern, eco-conscious manufacturing setups. This research highlights the transformative potential of intelligent pneumatic systems in driving productivity, reducing operational costs, and supporting the transition to greener, more sustainable industrial practices. The Author(s) 2025. -
Artificial Intelligence based System in Protein Folding using Alphafold
Artificial Intelligence has a high potential to solve many real-world problems. In the recent years researchers are dealing with one of the biggest complications in biology, which is protein folding. With the assistance of technology, we can foresee how proteins fold from a chain of amino acids into 3D shapes that do life's errands. There are mainly three big problems associatedwith folding of proteins. The first problem is there any particular folding code. The second one there is a folding system. Then the final problem is we able to determine the 3D structure of proteins. Proteins are the microscopic machines and structural building blocks of our cells. They carry out important functions like breaking down foods, storing oxygen and forming scaffolds to help cells keep their shape. Each one is built up of one amino acid chain that folds in on itself into a mostly defined structure. Each part of our body and in any other organism is made either from or by proteins and this is true for every living creature, even for viruses. The structure of very small proteins can be foreseen using the computer method. This article is all about the protein folding problem with more spotlights on the role of AI-based systems in protein structure forecasts. The motivation behind this article is to convey an overall understanding of AI-based answers for protein folding problems. 2022 IEEE -
Artificial Intelligence Driven Air Quality Prediction for Sustainable Goa
Clean Air is essential for the health and survival of both humans and wildlife. Air pollution has been linked to various serious diseases, including cancer. Rapid industrial growth and increasing population have contributed to rising pollution from transportation, industries, and agriculture. As a result, air pollution has become a major issue, particularly in developing countries like India. To ensure good air quality, accurate and reliable monitoring and prediction are required. Machine Learning (ML) models have shown promise in predicting Air Quality Index (AQI) over traditional methods. This research aims to propose a AQI prediction model using Attention based Bi-directional Long Short-Term Memory (ABiLSTM) to predict AQI in various cities across Goa, India. Data processing methods are used to manage date before providing it into the ABiLSTM model. Daily AQI series from 2022 to 2024 for six cities in Goa- Panaji, Pond, Assanora, Codli, Tilamol, and Tuem are collected and utilized to verify the proposed model. Two models are tested, including BiLSTM and ABiLSTM. Experimental results showed that the ABiLSTM model outperformed BiLSTM model in all cities, reporting lower error values and higher R2 scores. A comprehensive analysis with a set of evaluation indices confirmed that the proposed ABiLSTM model effectively captures the characteristics of the original AQI series and achieves a higher accuracy in AQI prediction. 2025 IEEE. -
Artificial Intelligence Driven Drug Delivery Systems: Recent Advances and Emerging Trends
Drug Delivery Systems (DDS) play a critical role in ensuring the therapeutic efficacy and safety of pharmaceutical agents. Conventional drug delivery approaches often suffer from limitations such as poor bioavailability, non-specific targeting, and systemic toxicity. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the design and optimization of drug delivery platforms. AI-driven methods enable predictive modeling, intelligent nanocarrier design, and personalized therapeutic strategies by analyzing large biomedical datasets. These technologies facilitate optimized drug formulation, controlled release mechanisms, and targeted delivery, thereby improving treatment outcomes. AI algorithms such as Support Vector Machines (SVM), random forests, Convolutional Neural Networks (CNN), and reinforcement learning are increasingly applied in nanoparticle design, pharmacokinetic modeling, and clinical decision support systems. Additionally, emerging concepts such as self-driving laboratories, autonomous drug delivery systems, and AI-guided nanomedicine are reshaping pharmaceutical research. This review provides a comprehensive analysis of recent advances in AI-driven drug delivery systems, covering computational techniques, nanocarrier optimization, clinical applications, and emerging research trends. Comparative analysis tables summarize key algorithms, delivery platforms, and research developments reported in the literature. Finally, major challenges including data quality, regulatory issues, and interpretability of AI models are discussed along with future directions for the integration of AI in precision medicine and smart therapeutics. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved. -
Artificial Intelligence Driven Integrated Hydraulic and Pneumatic Pressure Control Systems for Advanced Regulation of Shut-off Valves
This study presents the design and implementation of an Integrated Hydraulic and Pneumatic Pressure Control System (IHPPCS), emphasizing the development of advanced regulating and shut-off valves (ARASVs) to improve precision, responsiveness, and operational safety across diverse industrial processes. By synergistically combining the strengths of hydraulic and pneumatic technologies, the proposed system addresses critical limitations of conventional fluid control methods, offering enhanced adaptability under dynamic load conditions. The Advanced Regulating and Shut-Off Valve (ARASV) serves as the system's core component, incorporating precision-engineered mechanismssuch as spring-loaded diaphragms and pistonsto regulate fluid pressure with high accuracy. Regulating valves ensure consistent pressure levels, while shutoff valves function as critical safety devices, instantly isolating flow during overpressure events. This dual-function architecture enhances both performance stability and system protection. Technical analysis of the ARASV design reveals significant advantages, including minimal response time lag, reduced hysteresis, and high repeatability under cyclic operation. The integrated approach enables real-time pressure modulation, improving energy efficiency and reducing mechanical wear. Practical evaluations conducted in simulated industrial environments confirm the superior control, fidelity and reliability of the IHPPCS. The findings underscore the transformative potential of hybrid fluid power systems in next-generation industrial automation. By merging the force density of hydraulic systems with the speed and flexibility of pneumatics, the IHPPCS represents a scalable, intelligent solution for complex pressure control requirements. This research contributes to the advancement of sustainable and intelligent control systems, paving the way for more efficient, safe, and adaptive industrial operations. The Author(s) 2025. -
Artificial Intelligence Empowered Smart Manufacturing for Modern Society: A Review
Artificial Intelligence (AI) has emerged as a transformative force in the realm of smart manufacturing, shaping the landscape of modern society. This paper delves into the application of AI in smart manufacturing and its profound impact on various aspects of society, from industrial processes to daily life. We discuss how AI-driven technologies optimize efficiency, sustainability, and quality in manufacturing, enabling Society 5.0s vision of a harmonious convergence between technology and humanity. From intelligent automation to predictive analytics and personalized experiences, we uncover the multifaceted role of AI in shaping the future of smart manufacturing and its broader implications for a modern, interconnected society. 2024 Scrivener Publishing LLC. -
Artificial Intelligence for Bio-Inspired Security
[No abstract available] -
Artificial intelligence for blockchain and cybersecurity powered IoT applications
The objective of this book is to showcase recent solutions and discuss the opportunities that AI, blockchain, and even their combinations can present to solve the issue of Internet of Things (IoT) security. It delves into cuttingedge technologies and methodologies, illustrating how these innovations can fortify IoT ecosystems against security threats. The discussion includes a comprehensive analysis of AI techniques such as machine learning and deep learning, which can detect and respond to security breaches in real time. The role of blockchain in ensuring data integrity, transparency, and tamper-proof transactions is also thoroughly examined. Furthermore, this book will present solutions that will help analyze complex patterns in user data and ultimately improve productivity. 2025, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Boulouard, Abhishek Kumar, Vandana Sharma and Keshav Kaushik. All rights reserved. -
Artificial intelligence for blockchain and cybersecurity powered IoT applications
The objective of this book is to showcase recent solutions and discuss the opportunities that AI, blockchain, and even their combinations can present to solve the issue of Internet of Things (IoT) security. It delves into cuttingedge technologies and methodologies, illustrating how these innovations can fortify IoT ecosystems against security threats. The discussion includes a comprehensive analysis of AI techniques such as machine learning and deep learning, which can detect and respond to security breaches in real time. The role of blockchain in ensuring data integrity, transparency, and tamper-proof transactions is also thoroughly examined. Furthermore, this book will present solutions that will help analyze complex patterns in user data and ultimately improve productivity. 2025, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Boulouard, Abhishek Kumar, Vandana Sharma and Keshav Kaushik. All rights reserved. -
Artificial Intelligence for Cyber Defense and Smart Policing
The future policing ought to cover identification of new assaults, disclosure of new ill-disposed patterns, and forecast of any future vindictive patterns from accessible authentic information. Such keen information will bring about building clever advanced proof handling frameworks that will help cops investigate violations. Artificial Intelligence for Cyber Defense and Smart Policing will describe the best way of practicing artificial intelligence for cyber defense and smart policing. Salient Features: Combines AI for both cyber defense and smart policing in one place Covers novel strategies in future to help cybercrime examinations and police Discusses different AI models to fabricate more exact techniques Elaborates on problematization and international issues Includes case studies and real-life examples This book is primarily aimed at graduates, researchers, and IT professionals. Business executives will also find this book helpful. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Artificial intelligence for diabetic retinopathy detection: A systematic review
The incidence of diabetic retinopathy (DR) has increased at a rapid pace in recent years all over the world. Diabetic eye illness is identified as one of the most common reasons for vision loss among people. To properly manage DR, there has been immense research and exploration of state-of-the-art methods using artificial intelligence (AI) enabled models. Specifically, AI-empowered models combine multiple machine learning (ML) and deep learning (DL) based algorithms to improve the performance of the developed system architectures that are commercially utilized for the detection of DR disease. However, these models still exhibit several limitations, such as computational complexity, low accuracy in DR stage detection due to class imbalance, more time consumption, and high maintenance cost. To overcome these limits, a more advanced model is required to accurately predict the DR stage in the initial stages. For example, the identification of DR disease in the initial stage helps the ophthalmologist to make an accurate and safe diagnosis, and thereby, eyesight-related issues may be treated more effectively. This study conducted a systematic literature review (SLR) to provide a detailed discussion of the background of diabetic retinopathy, its major causes, challenges faced by ophthalmologists in DR detection, and possible solutions for identifying DR in the initial stage. Also, the SLR provides an in-depth analysis of the existing state-of-the-art techniques and system models used in DR diagnosis based on AI, ML, and recently developed DL-based approaches. Furthermore, this present survey would be helpful for the research community to receive information on the recent approaches used for DR identification along with their significant challenges and limitations. 2024 The Authors -
ARTIFICIAL INTELLIGENCE FOR DIGITAL TALENT ACQUISITION AND MANAGEMENT :Analytical Approaches, Practices, Models for Digitalization
In an era of rapid technological innovation and digital transformation, artificial intelligence (AI) is reshaping the way organizsations approach talent acquisition and management. Artificial Intelligence for Digital Talent Acquisition and Management: Analytical Approaches, Practices, Models for Digitalization analyses the intersection of AI and workforce strategies, offering readers a thorough exploration of analytical approaches, cutting-edge practices, and innovative models. Bridging the gap between AI theory and practical application, the authors provide actionable frameworks and strategies for leveraging AI in workforce management. From streamlining talent acquisition to enhancing management practices, the authors present a comprehensive roadmap for navigating the transformative impact of AI on digital talent strategies. Whether you're a business leader, HR professional, or academic, this book equips you with the tools and knowledge to thrive in the ever-evolving landscape of digitalization. Editorial matter and selection 2026 Ilango Velchamy, Leena N. Fukey, V. R. Uma, and Kaliyaperumal Srinivasan. -
Artificial Intelligence for Enhanced Anti-Money Laundering and Asset Recovery: A New Frontier in Financial Crime Prevention
The incorporation of artificial intelligence (AI) into asset recovery and anti-money laundering (AML) procedures signifies a revolutionary change in the handling of financial crime. This article investigates the use of AI techniques to improve AML compliance, detect suspicious activity, and improve transaction monitoring. Financial institutions can now analyze massive volumes of transaction data in real-time, find anomalies, and lower false positives thanks to AI-based solutions, which include machine learning algorithms and predictive modeling. The research also outlines the difficulties and advantages of implementing AI, such as enhancing the effectiveness and caliber of suspicious activity reports (SARs) while resolving security and privacy issues with data. The study makes the case that AI's capacity to offer collaborative analytics and dynamic risk assessments is essential for the development of AML frameworks and the overall effectiveness of financial crime prevention. 2024 IEEE. -
Artificial Intelligence in Banking Security-Technical Innovations and Challenges
The accelerating adoption of artificial intelligence (AI) technologies in the banking sector has introduced transformative possibilities for enhancing security frameworks against increasingly sophisticated cyber threats. This research investigates the technical innovations driven by AI, such as machine learning algorithms, biometric authentication systems, and natural language processing, and their impact on improving fraud detection, cybersecurity monitoring, and compliance automation. The paper identifies how AI systems, through real-time analysis of large-scale transaction data, can locate abnormal behavioral patterns and respond proactively to potential threats, significantly reducing human error and response time. A detailed analysis of the current literature reveals a significant research gap in integrating explainable AI, secure data governance frameworks, and scalable models suited for diverse banking environments. The outcome of this research highlights the need for a balanced approach that fosters technological innovation while addressing regulatory compliance, ethical concerns, and operational constraints, paving the way for a secure and intelligent banking infrastructure. 2025 IEEE.