Clinical Integration of Artificial Intelligence in Urology with a Focus on Temporal Deep Neural Networks for Emphysematous Pyelonephritis| Chapter 3 | Newer Frontiers in Urology, Volume III

 

Artificial intelligence (AI) is transforming urology by facilitating quick, data-driven analysis in diagnosis and treatment. This chapter explores core AI concepts and their applications in urological conditions like kidney stones, bladder cancer, prostate cancer, and benign prostatic hyperplasia (BPH). Examples of models (such as convolutional neural networks and support vector machines) used in urology are used to teach general AI approaches (machine learning and deep learning). Automated MRI prostate cancer diagnosis (AUC ~0.96), ureteroscopic stone identification (CNN ~90% accuracy), and bladder tumour segmentation are significant achievements.

 

Additionally, the chapter highlights issues with dataset heterogeneity, sample size, and selection bias while briefly discussing the types of datasets used in AI-driven urology research, including imaging, clinical, and longitudinal data. A case study of an emerging multi-task deep neural network (t-MTDNN) for the prediction of emphysematous pyelonephritis (EPN) is included. Feature descriptions (SHAP) are provided for the t-MTDNN architecture and workflow, and its clinical impact and performance metrics are analysed.

 

In conclusion, the strengths and challenges of AI models are compared (Table 1) and prospective opportunities in AI-assisted urology are described with an emphasis on the therapeutic advantages (improved accuracy, efficiency) and limitations (data requirements, interpretability). Instead of replacing clinical judgement, urologists and healthcare organisations view AI as a clinical decision-support tool that can enhance workflow efficiency, support hospital-level adoption through interdisciplinary collaboration, and augment physician expertise.

 

 

Author(s) Details

Roshan Reddy
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Rajan Ravichandran
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Vivek Meyyappan
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Velmurugan Palaniyandi
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Hariharasudhan Sekar
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Sriram Krishnamoorthy
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

 

Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-47485-93-0/CH3

Intelligent Battery Management in Electric Vehicles: A Comprehensive Review of AI Techniques | Chapter 02 | Engineering Research: Perspectives on Recent Advances Vol. 12

 

To reduce carbon emissions and tackle global environmental problems, the automotive sector has focused heavily on electric vehicles (EVs). However, the eventual deterioration of battery health and performance may adversely affect the efficiency of EVs. Due to their ability to accurately assess battery health, analyse faults, and control temperature for enhanced safety, reliability, and effective optimisation of EV performance, artificial intelligence (AI) techniques have garnered significant interest. This review investigates and evaluates the effects of AI techniques to improve the battery management system (BMS) of electric vehicles (EVs). A variety of methodologies are employed to perform a statistical analysis of relevant BMS papers. The statistical analysis assesses essential characteristics such as current research trends, keyword analysis, nation analysis, authorship, collaboration, publishers, and research classification. Moreover, a thorough examination of the goals, contributions, advantages, and disadvantages of advanced AI methods is provided. In addition, several key guidelines and recommendations are presented, along with a number of significant concerns and challenges, for potential future enhancement. Future researchers could utilise the statistical analysis as a guide to develop innovative BMS technologies for EVs that function and are managed sustainably.

 

Author(s) Details :-

 

Ashok Kumar Bandla
Department of CSE(AI&ML), Ramachandra College of Engineering, Eluru, A.P, India.

 

Y. Lavanya
Department of ECE, Ramachandra College of Engineering, Eluru, A.P, India.

 

D. Sai Prasanthi
Department of EEE, Ramachandra College of Engineering, Eluru, A.P, India.

 

G. Kaladhar
Department of EEE, St. Ann’s college of Engineering & Technology, Chirala, A.P, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/erpra/v12/6573

Capabilities of Engineers to Build Machines with Human-like Intelligence Using Artificial Intelligence (AI) and Machine Learning (ML) | Chapter 03 | Recent Research Advances in Arts and Social Studies Vol. 9

 

The study addresses the ongoing debate surrounding the capabilities of engineers to build machines with human-like intelligence using Artificial Intelligence (AI) and Machine Learning (ML). By highlighting the existing obstacles and proposing an alternative approach based on complexity theory and non-linear adaptive systems, the manuscript offers valuable insights and potential solutions to the challenges faced by engineers in the field of AI and ML research. Additionally, it aims to clarify the confusion and misuse of terminology surrounding AI and ML, contributing to greater clarity and understanding within the scientific community. AI and ML are attracting a lot of scientific and engineering attention nowadays, nothing up to now has been achieved to reach the level of building machines that possess human-like intelligence. However, the engineering community continuously claims that several engineering problems are solved using AI or ML. Here, it is argued that engineers are not able to build intelligent machines, implying that the systems claimed to have AI/ML belong to different engineering domains. The base of the syllogism is the existence of four main obstacles on which extensive elucidation is performed. These are (i) lack of precise definition of AI (and ML), (ii) impossible generation of requirements and verification and validation procedures for designing and fabricating machines with intelligence, (iii) no scientific consensus, (iv) philosophical fundamental issues with AI/ML which impose conceptual and assimilation problems in order not to be able making progress if not deal with them. In addition, an attempt to clear out the developed confusion, misuse and abuse of the phrases “Artificial Intelligence” and “Machine Learning” by scientists and engineers is carried out. The confusion is a result of the previous obstacles scientists and engineers are facing and avoid to face, hence creating and growing a kind of “Lusus Naturae” of this scientific field with socio-political impacts as well.  Furthermore, mathematical, and philosophical approaches are also mentioned that strengthen the argument against AI implementability as part of the whole syllogism. Finally, an alternative approach (not being unique) is suggested and discussed for performing research on AI and ML by the engineers. It is based on complexity theory and non-linear adaptive systems and provides the benefit of eliminating the before mentioned pragmatic and philosophical obstacles that engineers are facing and ignoring, without creating confusion on this scientific endeavor. This approach is based on the emergent properties of complex systems. So instead of trying to make the apple (as a symbol of AI), we build the apple tree which through complexity the apple will be grown (symbolically AI will be emanated).

 

Author(s) Details :-

 

Nikolaos Panagiotopoulos
On Board Computers & Data Handling Systems, European Space Research & Technology Centre (ESTEC), Noordwijk, Netherlands.

 

Please see the book here :- https://doi.org/10.9734/bpi/rraass/v9/322

Explainable Artificial Intelligence and Social Theory Integration for Advancing Educational Equity in Nepal | Chapter 2| Mathematics and Computer Science: Research Updates Vol. 8

 

This chapter examines entrenched socioeconomic disparities in Nepal’s education system through the integration of explainable artificial intelligence (XAI) and foundational social theories of equity. While Nepal has made progress in enrollment, persistent gaps in access, retention, and learning outcomes remain among groups marginalized by caste, gender, and geography. Existing policy analyses often rely on linear statistics or descriptive methods and lack operational links to sociological theory. To address this lacuna, we develop a mixed-methods framework that blends predictive machine learning with interpretability (SHAP) and qualitative inquiry to ground algorithmic findings in lived experience. Using national-level datasets — notably the Education Management Information System (EMIS) and the Nepal Living Standards Survey (NLSS)—we operationalize a Capability Index and train ensemble models (Random Forest and XGBoost) to predict capability deprivation and dropout risk. SHapley Additive exPlanations (SHAP) are applied to attribute model outputs to observable socioeconomic and school-level features. We formalize the predictive problem and its interpretability as follows: given feature set X = {x1, . . . , xn} and an outcome Y (capability index or dropout probability), we estimate \(\hat{Y}\) = f(X; θ) and decompose \(\hat{Y}\) additively into baseline and feature contributions \(\hat{Y}\) = ϕ0 +\(\Sigma\)i ϕi. This decomposition informs policy levers by quantifying marginal contributions of poverty, distance to school, caste status, and school resources. Beyond technical contributions, the chapter situates model outputs within Sen’s Capability Approach and Bourdieu’s Cultural Capital Theory to interpret how structural constraints and cultural resources shape educational opportunity. Deliverables include a resource allocation framework, SHAP-driven simulation dashboards for policymaking, and early-warning indicators for dropout prevention. Qualitative interviews with educators and community stakeholders are used to validate and contextualize the quantitative results. Together, these elements advance both theory and practice: they demonstrate how XAI can produce socially meaningful, policy-ready evidence for more equitable education in Nepal and similar low- and middle-income contexts.

 

Author(s) Details

Anmol Adhikari
Department of Computer Science, Noida International University, India.

 

Vivek Kumar Sinha
Department of Computer Science and Engineering, Noida International University, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/6555

Post-Quantum AI: Building Secure Machine Learning Systems in the Quantum Era | Book Publisher International

 

This book explores the intersection of artificial intelligence (AI) and quantum computing, focusing on the urgent need to secure machine learning systems in the face of emerging quantum threats. As quantum computers advance, they expose vulnerabilities in classical cryptographic methods, potentially undermining data integrity, privacy, and trust in AI-driven applications. To address these challenges, this study introduces the concept of post-quantum AI—a framework for integrating quantum-resistant cryptographic algorithms, Anomaly detection mechanisms, and Resilient machine learning architectures. This book makes three core contributions: it motivates a quantum-era threat model for machine learning (ii) it maps a migration path to standardised post-quantum cryptography Crypto-agile architectures (iii) it presents a defence-in-depth blueprint across the data → training → inference lifecycle that integrates privacy-preserving learning Governance. This work explicitly highlights key contributions, including proposed frameworks, algorithms, and case studies. Future research directions are also outlined to guide continued exploration in this emergent field. The final candidate algorithms from the NIST PQC standardisation process (NIST, 2022–2023) further strengthen this discussion.

 

Key themes include the foundations of quantum mechanics relevant to computation, the fundamental differences between classical and quantum computing, and the transformative potential of quantum algorithms for optimisation, pattern recognition, and predictive analytics. The book highlights case studies spanning drug discovery, finance, mobile networks, and supply chain optimisation, illustrating how quantum-enhanced AI can revolutionise industry while simultaneously raising complex security and ethical concerns.

 

A central focus is the development and deployment of post-quantum cryptography (PQC)), such as lattice-based and hash-based algorithms, to safeguard AI models against adversarial and quantum-accelerated attacks. The discussion extends to adversarial machine learning, explainable AI (XAI), and hybrid classical–quantum systems as strategies for strengthening resilience.

 

The ethical, legal, and regulatory dimensions of post-quantum AI are also examined, emphasising fairness, transparency, accountability, and international cooperation. By combining technical innovations with responsible governance, the book advocates for building trustworthy AI systems that remain robust in the quantum era. Future work includes post-quantum cryptography (PQC) performance benchmarking in ML pipelines Patterns for crypto-agile key management, Assurance methods for privacy-preserving Federated learning as standards, and Implementations that are mature.

 

Author(s) Details

Amit Taneja
Vellore Institute of Technology, Tamil Nadu, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-88417-99-0

Assessing OTT Platform Efficacy Using Data Science and Data Mining Techniques | Chapter 8 | New Horizons of Science, Technology and Culture Vol. 4

 

In the digital era, where internet connectivity shapes lifestyles and choices, traditional modes of entertainment—such as cable television and movie theatres—are gradually witnessing a decline in relevance. In their place, Over-the-Top (OTT) platforms have emerged as the dominant medium of content consumption for a vast segment of the global population. These platforms offer on-demand access to a vast array of content across genres, languages, and formats, redefining how individuals engage with entertainment. Despite their growing ubiquity, a surprising segment of the population—approximately 20%—remains unfamiliar with the term "OTT platforms." Interestingly, nearly half of this segment engages with such services unknowingly through well-known brands like Netflix, Amazon Prime Video, and Disney+, indicating a gap in conceptual understanding despite regular usage.

 

This research paper aims to bridge that knowledge gap and contribute to the broader understanding of OTT platforms from both a consumer and analytical standpoint. The study adopts a data-driven approach, utilising data collected through convenience sampling via structured surveys to assess the depth of public awareness, usage frequency, platform preference, and behavioural trends related to OTT services. It explores consumer sentiment, viewing patterns, subscription tendencies, and satisfaction levels.

 

Furthermore, the paper employs data science and data mining techniques, including machine learning algorithms, to extract meaningful insights from the data. It analyses the correlation between consumer preferences and the performance of different OTT platforms, revealing how content variety, pricing strategies, user interface, and personalisation influence user loyalty and platform success. Sentiment analysis is used to evaluate the public's perception—both positive and negative—about these platforms, underlining the dual nature of technological advancements. By examining these facets, the study not only offers actionable insights for OTT service providers and marketers but also seeks to educate uninformed users about the evolving digital entertainment landscape. This research underlines the growing necessity of digital literacy in media consumption and highlights the transformative impact of data science in decoding user behaviour and platform dynamics in the entertainment industry.

 

 

Author(s) Details

 

Shivani Vats
Jagan Institute of Management Studies, New Delhi, Delhi 110085, India.

 

Disha Grover
Jagan Institute of Management Studies, New Delhi, Delhi 110085, India.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v4/6026

Machine Learning Feature Selection Techniques to Model the Elements of Cash Conversion Cycle and Other Covariates on Hospital Performance | Chapter 4 | Research Updates in Mathematics and Computer Science Vol. 8

 

This study seeks to contribute by empirically modeling the cash conversion cycle on hospital performance using two supervised machine learning feature selection techniques. In data science, model selection instability is a major concern, especially when dealing with a high number of features. Data mining, such as subset selection technique and regularization (shrinkage) techniques, pays attention to how to extract meaningful information by modeling the raw data. We employed methods such as best subset selection coupled with an exhaustive search using linear regression and shrinkage methods (Lasso, Ridge, and ElasticNet) to model a real dataset. The empirical results indicated that the Lasso outperformed the other shrinkage methods in feature selection even though the average root mean squared error (rmse) was close. Again, Account Receivable Days (ARD), Account Payable Days (APD), Inventory Turnover Days (INV), and Debt Ratio were discovered to be predictors of hospital performance, which are also components of the Cash Conversion Cycle. Finally, the results show that, on average, a day decrease in the hospital’s collection period will decrease performance by 1%, and a one-unit increase in a day in the account payable decrease performance by 0.003 times. Future studies could explore more advanced algorithms, like recursive feature elimination selection methods, to enhance the analysis of CCC on hospital performance. Lastly, it is recommended that hospitals focus on restructuring their cash conversion cycle management, particularly concerning days of account payables.

 

Author(s) Details

 

Richmond Essieku

Department of Economics, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA and School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley 1201 W University Dr., Edinburg, TX 78541, USA.

Helena Baffoe

Department of Social Work, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA.

Makafui Komla Akatu

Department of Economics, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA.

Prince Bosompim

School of Financial Planning, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA.

James Ladzekpo

Department of Economics, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/rumcs/v8/440

Use of Machine Learning Models for Recommender System of Sentiment Analysis | Chapter 4 | Research Updates in Mathematics and Computer Science Vol. 9

 The study proposes an effective sentiment analysis recommender system framework using machine learning models. Recommender systems are used to build recommendations by processing information from actively gathered varied kinds of data. The data that is used for processing information depends upon the type of recommender system. In recent years, with the rapid growth of Internet technology, online shopping has become a rapid way for users to purchase and consume desired products. Tweet sentiment analysis is a product of the vast amount of user-generated content on social media platforms like Twitter. Sentiment analysis serves as the foundation for recommendation and decision support systems, and it is becoming a crucial tool on online platforms to extract user emotional state data and increase user happiness. 

 

Author(s) Details

A. Naresh

Department of CSE, Annamacharya Institute of Technology and Sciences Autonomous, Kadapa, India.

P. Venkata Krishna

Department of Computer Science, Sri Padmavati Mahila Visvavidyalayam, Tirupati, India.

 

Please see the link:- https://doi.org/10.9734/bpi/rumcs/v9/526

Evaluation of Machine Learning Model through Stock Price Prediction Research | Chapter 7 | Contemporary Research in Business, Management and Economics Vol. 9

 

This research holds paramount importance in advancing our utilization of artificial intelligence to predict economic factors, notably within the dynamic domain of the stock market. The primary objectives focus on determining the optimal performance among the seven machine learning models employed. Stock investment prices are never still; they are always changing. It is important to stay informed on the upward or downward trends of the market to make future investments. To accustom the machine learning (ML) predictor to the multitude of possibilities that could categorize stock patterns, 7 different ML models were trained on 1250 pieces of open stock market data dating to the last 5 years by assigning weight values to all the models based on their accuracy. The neural network ends up predicting the stock price with its given data at a mediocre level at best, with MSE averages of 29.93 and 26.85 respectively. Its highest weight, tesla, ends up with only 0.013% of the total weightage. Results showed that two of the ML models, specifically the Linear Regression and the Random Sample Consensus (RANSAC) Regressor models consistently outperformed the other 5 models, both ending up with the highest weight values of around 0.5 when predicting for Amazon, Apple, and Tesla. Therefore, the RANSAC and Linear Regression models are the best models to rely on when predicting open stock market prices using ML. Future endeavors must continue this trajectory by expanding model capacities, incorporating richer data sources, and embracing AI-driven advancements to propel stock market predictability into new realms.

 

 

Author(s) Details

Navye Vedant

Inspirit AI, Sammamish, WA, USA.

 

Please see the link:- https://doi.org/10.9734/bpi/crbme/v9/892

 

A Clinical Decision Support Model for Predicting Avoidable Re-hospitalization of Breast Cancer Patients in Kenyatta National Hospital |Chapter 7 | Mathematics and Computer Science: Contemporary Developments Vol. 2

 

This study develops a Clinical Decision Support Model (DSM) to aid in assessing and recommending the discharge of a breast cancer patient from the hospital ward since the discharge problem is often overwhelming for clinicians to process at the point of care or in urgent situations. The model incorporates breast cancer patient-specific data that is well-structured, having been obtained from pre-study administered questionnaires and current evidence-based guidelines. The obtained dataset of the pre-study questionnaires is processed using data mining techniques to generate an optimal clinical decision tree classifier model. This model assists physicians in enhancing their decision-making process when discharging a patient, based on basic cognitive processes in medical thinking. This resulted in new, better-formed, and superior discharge outcomes. The model improves the quality of patient discharge assessments through a predictive discharging model outcome designed from individual unique risk attributes at the point of discharge. This enables timely detection of possible deterioration in health quality upon said discharge, which is noted as a major contributor to ward congestion as a result of re-hospitalization from poor discharge assessment that currently wholly relies on overwhelmed clinicians at the point of care or in urgent situations. The outcome of the implemented model is that it bridges the gap caused by less informed clinical discharge, and reinforces discharge decisions that ensure better treatment outcomes, thus reducing unforeseeable deterioration in the quality of health for discharged patients and surges in the mortality rate blamed on mistrusted discharge decisions. This paper is organized to start with a discussion of the breast cancer scourge and clinical knowledge for discharging patients, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

Author(s) Details

Christopher Oyuech Otieno
Department of Computer Science, University of Nairobi, Nairobi, Kenya.

Oboko Robert Obwocha
Department of Computer Science, University of Nairobi, Nairobi, Kenya.

Andrew Mwaura Kahonge
Department of Computer Science, University of Nairobi, Nairobi, Kenya.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v2/1022

Predicting Diabetes Using Machine Learning: A Comprehensive Framework with Model Interpretability | Chapter 4| New Horizons of Science, Technology and Culture Vol. 3

 

This chapter explores the construction of a detailed machine learning (ML) framework for predicting diabetes using diverse real-world datasets. The alarming rise in diabetes prevalence globally and particularly in developing nations such as India necessitates innovative approaches for early detection and intervention. Traditional diagnostic techniques, though clinically established, often fall short in scalability and adaptability. This study focuses on bridging this gap by integrating ML methodologies that not only offer superior prediction accuracy but also provide transparency through interpretability tools.

 

Key contributions of this work include the comprehensive data preprocessing steps (missing value treatment, normalisation, encoding, and SMOTE-based class balancing), the comparative evaluation of three widely used classifiers (Logistic Regression, Random Forest, and XGBoost), and the use of SHAP values for enhancing model interpretability. Among the models tested, XGBoost achieved the highest performance with an accuracy of 97.93%, AUC of 0.9974, and excellent sensitivity and specificity values, confirming its suitability for real-world healthcare applications. The chapter concludes with discussions on model performance, interpretability, clinical relevance, limitations, and avenues for future research.

 

Author(s) Details

 

Mounika Panjala
Department of Statistics, Osmania University, Hyderabad-7, Telangana, India.

 

Bhatracharyulu N.Ch.
Department of Statistics, Osmania University, Hyderabad-7, Telangana, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v3/5950

Applications of Artificial Intelligence in Genetics | Book Publisher International

 

The rapid advancement of artificial intelligence (AI) has revolutionised various scientific domains, with genetics emerging as a particularly transformative field. Applications of Artificial Intelligence in Genetics offers a comprehensive exploration of how AI methodologies are reshaping genetic research and healthcare. Beginning with foundational concepts, the book provides an overview of AI's integration into biological sciences, emphasising its pivotal role in handling and interpreting vast and complex genetic datasets. Key chapters delve into the use of machine learning in genome-wide association studies (GWAS), gene expression profiling, and personalised genomic medicine, showcasing practical implementations and ground-breaking discoveries. Deep learning techniques and their applications in predictive modelling and variant interpretation are examined in detail, underscoring the growing sophistication of AI tools in genetics. The book also addresses critical challenges, such as data privacy, algorithmic bias, and ethical considerations, while outlining potential future directions for AI-driven genetic research. This interdisciplinary volume is intended for researchers, practitioners, and students seeking to understand and contribute to the dynamic intersection of artificial intelligence and genetics.

 

Author (s) Details

Dr. D. Udaya Kumar
Human Genetics Department, Andhra University, Visakhapatnam, Andhra Pradesh, India.

 

Dr. Rooth Vasantha Medapati
Human Genetics Department, Andhra University, Visakhapatnam, Andhra Pradesh, India.

 

Prof. Saritha Medapati

Vignan Institute of Pharmaceutical Technology, Duvvada, Visakhapatnam, Andhra Pradesh, India.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49970-45-8

Precision and Performance: Regression Methods in Data Processing | Chapter 3 | Engineering Research: Perspectives on Recent Advances Vol. 8

Regression analysis is a fundamental statistical technique used for modelling relationships between multiple variables, playing a significant role in predictive analytics and artificial intelligence. It helps in evaluating dependencies between a dependent variable and one or more independent variables, making it essential for forecasting outcomes. This study focuses on the application of regression models to analyse vehicle dynamics. By utilising data such as traffic velocity, road gradient, and actual speed, we aim to predict a vehicle’s velocity profile. Various regression techniques—including linear regression, multivariate linear regression, and nonlinear regression—are examined to determine their effectiveness in data processing. The core objective of this research is to develop reusable functions for each model, eliminating dependence on predefined programming functions, while enabling effective visualisation of data for optimal model selection. One advantage of a regression model over factor or cluster analysis is that the regression model can be used to obtain an estimate of the actual amount of change in a dependent variable that occurs as a result of a change in an independent variable. This study only focuses on simple linear regression; future work should focus on the use of multiple regression to capture more complex relationships.

 

Author (s) Details

Sandhya Tatekalva
Department of Computer Science, S.V. University, Tirupati, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/erpra/v8/5675

 

Applications of Artificial Intelligence in Genetics | Book Publisher International

The rapid advancement of artificial intelligence (AI) has revolutionised various scientific domains, with genetics emerging as a particularly transformative field. Applications of Artificial Intelligence in Genetics offers a comprehensive exploration of how AI methodologies are reshaping genetic research and healthcare. Beginning with foundational concepts, the book provides an overview of AI's integration into biological sciences, emphasising its pivotal role in handling and interpreting vast and complex genetic datasets. Key chapters delve into the use of machine learning in genome-wide association studies (GWAS), gene expression profiling, and personalised genomic medicine, showcasing practical implementations and ground-breaking discoveries. Deep learning techniques and their applications in predictive modelling and variant interpretation are examined in detail, underscoring the growing sophistication of AI tools in genetics. The book also addresses critical challenges, such as data privacy, algorithmic bias, and ethical considerations, while outlining potential future directions for AI-driven genetic research. This interdisciplinary volume is intended for researchers, practitioners, and students seeking to understand and contribute to the dynamic intersection of artificial intelligence and genetics.

 

Author (s) Details

Dr. D. Udaya Kumar
Human Genetics Department, Andhra University, Visakhapatnam, Andhra Pradesh, India.

 

Dr. Rooth Vasantha Medapati
Human Genetics Department, Andhra University, Visakhapatnam, Andhra Pradesh, India.

 

Prof. Saritha Medapati
Vignan Institute of Pharmaceutical Technology, Duvvada, Visakhapatnam, Andhra Pradesh, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49970-45-8

Enhancements and User-Centered Innovations in AI in Libraries | Chapter 1 | Scientific Research, New Technologies and Applications Vol. 2

Library services are transforming because of artificial intelligence (AI), which is bringing about major improvements and user-focused innovations that improve accessibility, effectiveness, and user engagement. This article examines how artificial intelligence (AI) transforms libraries, emphasizing important technologies including computer vision, natural language processing, and machine learning. The organization and finding of materials are streamlined by AI-driven automated cataloging and metadata management, and improved search capabilities yield more relevant and accurate results. Artificial Intelligence (AI) supports digital preservation initiatives by guaranteeing the longevity and availability of vital materials. Chatbots and virtual assistants provide 24/7 assistance, enhancing customer happiness and lightening the workload of employees. The user experience is enhanced by automated resource management and personalized recommendations, making library visits more productive and enjoyable. Furthermore, AI powers immersive technologies like virtual and augmented reality and improves accessibility for those with impairments. It also makes interactive learning easier through educational platforms. Libraries may ethically utilize AI's promise by tackling obstacles and moral issues like prejudice and data privacy. This article highlights AI's critical role in contemporary libraries by showing how these advancements provide more welcoming, inclusive spaces that change to meet the communities' changing requirements.

 

Author (s) Details

Suryakanth Halburga
New Horizon College of Engineering, Marthahalli, Bangalore, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v2/1866

 

Using Ensemble Machine Learning: A Multifaceted Approach to Predicting Rehabilitation Outcomes | Chapter 2 |Disease and Health Research: New Insights Vol. 8

The field of rehabilitation for individuals with neuromuscular diseases has witnessed significant advancements, driven by the convergence of robotics, assistive technologies, and machine learning. These innovations are reshaping how rehabilitation is approached, offering new possibilities for personalized, effective, and efficient treatments that were previously unattainable. This chapter delves into the transformative role of these technologies, emphasizing the development and application of ensemble machine learning models specifically designed to predict rehabilitation outcomes. The study provides a comprehensive overview of the current state of rehabilitation technologies, focusing on the integration of robotics and orthotics into therapeutic practices. Robotics and assistive technologies, such as powered exoskeletons and smart orthoses, are becoming increasingly vital in enhancing the mobility and autonomy of patients with neuromuscular impairments. These devices are not only extending the capabilities of traditional rehabilitation methods but are also enabling more precise and controlled therapeutic interventions that can be tailored to the unique needs of each patient. In the following sections, this study explores the intricate design and functionality of orthoses and assistive robots, illustrating how these devices are engineered to support and augment human movement. The chapter discusses various types of orthotic devices, from simple mechanical braces to advanced robotic systems that interact seamlessly with the human body, adapting to the user's movements and providing real-time feedback. These innovations are shown to significantly improve the effectiveness of rehabilitation exercises, accelerate recovery times, and enhance overall patient outcomes. Central to this discussion is the introduction of a novel, multifaceted approach to predicting rehabilitation outcomes using ensemble machine learning models. These models, which combine the strengths of multiple algorithms, are designed to capture complex patterns in patient data, thereby providing more accurate and reliable predictions of rehabilitation success. This study present a detailed analysis of the development process of these models, including the selection of relevant features, the training and validation of the models, and the implementation of advanced visualization techniques to interpret the results. Through rigorous cross-validation and real-world testing, the robustness of these ensemble models is demonstrated in predicting outcomes across diverse patient populations. The results indicate that these models outperform traditional predictive methods, offering superior accuracy and the ability to generalize across different patient scenarios. The chapter also includes a series of visualizations that illustrate the importance of various features in the prediction process, the relationship between actual and predicted outcomes, and the distribution of model residuals. Furthermore, this study discusses the implications of these findings for clinical practice. The ability to predict rehabilitation outcomes with high precision allows clinicians to tailor interventions more effectively, optimizing treatment plans for individual patients and potentially reducing recovery times. The chapter concludes by considering the broader impact of these technologies on the healthcare system, highlighting the potential for machine learning models to drive innovation in personalized medicine and rehabilitation. Looking forward, several key areas for future research were identified including the integration of real-time data analytics into rehabilitation devices, the development of more sophisticated models that can adapt to changing patient conditions, and the exploration of new forms of human-machine interaction that can further enhance the efficacy of rehabilitation. The ongoing evolution of these technologies promises to open new frontiers in the treatment of neuromuscular diseases, ultimately improving the quality of life for millions of patients worldwide.

 

Author (s) Details

Rocco de Filippis
Institute of Psychopathology, Rome, Italy.

 

Abdullah Al Foysal
Computer Engineering (AI), University of Genova, Genova, Italy.

 

Please see the book here:- https://doi.org/10.9734/bpi/dhrni/v8/2486

 

Machine Learning Applications in Oil Well Petrophysics and Original Oil in Place Estimation: A Systematic Literature Review | Chapter 1 | Current Approaches in Engineering Research and Technology Vol. 8

One type of artificial intelligence (AI) that can be used in any field of study is machine learning. Numerous algorithms that are used to perform different tasks, including classification, estimation, prediction, comparison, approximation, optimization, and selection, are included in it. The reservoir engineer can predict the total amount of crude oil that may be in the reservoir by assessing the original oil in place. When there is limited data available, machine learning is found to execute reserves estimation quickly and accurately. These and other characteristics of machine learning led to a thorough evaluation of the literature covering research done between 2010 and 2021, examining the advantages and disadvantages of the studies as they were presented. To determine the hydrocarbon potential of a reservoir, the oil industry collects several kinds of data from both subsurface and surface sources. Large-scale data collection, analysis, and output prediction are all known to be possible using sensors. After a set of inclusion and exclusion criteria were applied to the 3127 study-related papers that were gathered from 4 databases, 104 journal articles that satisfied the requirements were used for the review. According to the study's findings, 2019 had the most publications (20 out of 104) on the subject under review among the years under evaluation. Additionally, 39% of authors noted that the algorithm was not performing well, and 61% of authors reported insufficient data. It was also discovered that the sector employed machine learning more for forecasting and prediction than for other purposes, with artificial neural networks (ANNs) being the most used AI technology. The work provides researchers with further insights into machine learning for petrophysics analysis and original oil in place estimation. This clever new technology simplifies and streamlines the process of evaluating data.

 

Author (s) Details

Ekemini Anietie Johnson
Department of Computer Science, University of Uyo, Nigeria and TETFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Nigeria.

 

Okure Udo Obot
Department of Computer Science, University of Uyo, Nigeria and TETFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Nigeria.

 

Kingsley Attai
Department of Mathematics and Computer Science, Ritman University, Nigeria.

 

Julius Akpabio

Department of Petroleum Engineering, University of Uyo, Nigeria.

Udoinyang Godwin Inyang
Department of Computer Science, University of Uyo, Nigeria and TETFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Nigeria.

 

Anietie Emmanuel John
Department of Mathematics and Computer Science, Ritman University, Nigeria.

 

Mfon Okpu Esang
Department of Computer Science, Federal Polytechnic Ukana, Nigeria.

 

Eduediuyai Ekerette Dan
Department of Computer Engineering, Federal Polytechnic Ukana, Nigeria.

 

Imaobong Okpongette Akpan
Department of Mechanical Engineering, Federal Polytechnic Ukana, Nigeria.

 

Aniefiok Bassey
Department of Computer Science, Alvan Ikoku Federal University of Education, Nigeria

 

Kitoye Ebire Okonny
ICT Centre, Ignatius Ajuru University of Education, Port Harcourt, Nigeria.

Ifeanyi Bardi
Department of Computer Science, Ignatius Ajuru University of Education, Port Harcourt, Nigeria.

 

Please see the book here:- https://doi.org/10.9734/bpi/caert/v8/1874

Artificial Intelligence in Predictive Analytics of Patient Outcomes and Disease Management | Chapter 6 | An Overview of Disease and Health Research Vol. 2

Artificial Intelligence (AI) has revolutionised predictive analytics in healthcare, offering innovative approaches for patient outcome prediction and disease management. This review explores the role of AI-driven predictive models in early disease detection, prognosis estimation, and personalised treatment strategies. This review also discusses recent advancements, limitations, and future prospects of AI-powered predictive analytics in healthcare, emphasising its transformative potential in improving patient care and disease prevention. Machine learning (ML) algorithms, deep learning (DL) networks, and natural language processing (NLP) have significantly enhanced predictive capabilities by analysing vast datasets, including electronic health records (EHRs), genetic information, and real-time patient monitoring data. AI applications in disease management facilitate early intervention, optimise resource allocation, and improve clinical decision-making. However, challenges such as data privacy, model interpretability, and ethical considerations remain key concerns. Ensuring transparency and explainability in AI models is crucial to gaining clinician and patient trust while mitigating risks associated with biased or erroneous predictions.

 

Author (s) Details

Zaki Siddiqui
Department of Medicine, MLB Medical College, Jhansi, Uttar Pradesh, India.

 

Nirali D Vyas
GMERS Medical College, Vadnagar, Gujarat, India.

 

Soumya Kumar Acharya
Department of General Medicine, PGIMER and Capital Hospital, Bhubaneswar, Orissa, India.

 

Sanket Patel
GMERS Medical College, Vadnagar, Gujarat, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/aodhr/v2/5641

Machine Learning for Stock Market Forecasting: A Decision Support Framework Using Infosys Historical Data |Chapter 2 | New Advances in Business, Management and Economics Vol. 7

The volatility of stock markets makes prediction a complex but essential task for investors seeking to optimise their decision-making. This study presents a data-driven decision support model for stock market prediction using historical stock price data of Infosys Ltd., a leading IT firm in India. By applying data mining techniques—specifically classification and rule-based prediction—the model aims to identify meaningful patterns from past trends and assist in forecasting future price movements.

The study uses monthly trading data from the National Stock Exchange for the period 2010 to 2015, including open, high, low, and close price values. These values were transformed into symbolic categories (rise/fall) for analytical clarity. Using the ESTARD Data Miner tool, decision trees and rule-based classifiers were generated to derive actionable prediction rules. The rules were further tested using a What-If analysis for real-time prediction scenarios.

The findings demonstrate the effectiveness of symbolic conversion and decision tree modelling in predicting the stock trend classes. This predictive framework holds potential for guiding investors in making more informed buy/sell decisions and enhancing the reliability of investment strategies based on historical data patterns.

 

Author (s) Details

Sanjeev Gour
Medicaps University, Indore, MP, India.

 

Sanjana Sharma
Acropolis Institute of Technology & Research, Indore, MP, India.

 

Prerita Kulkarni
Medicaps University, Indore, MP, India.

 

Shimna Mohan K.
Medicaps University, Indore, MP, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nabme/v7/5193

Integrating Modern and Intelligent Approaches with Efficiency for Soil Nutrient Deficiency Detection | Chapter 10 | Agricultural Sciences: Techniques and Innovations Vol. 1

The essentiality of soil health to the vitality of our planet cannot be overstated. This complex ecosystem teems with microscopic organisms and vital nutrients that foster the growth of plants, feeding into the cycle of life that sustains us all. The application of intelligent technologies, particularly artificial intelligence (AI) and machine learning (ML), in detecting soil nutrient deficiencies is revolutionising modern agriculture. While traditional soil nutrient analysis methods are effective, they are often costly, time-consuming, and lack real-time insights. Emerging intelligent approaches overcome these limitations by providing rapid, accurate assessments, enabling precise and timely fertilisation. Case studies, such as those of Indian startups CropIn and Fasal, illustrate the successful implementation of AI-driven solutions, leading to improved crop yields, optimised fertiliser use, and enhanced sustainability. This article explores current advancements, ongoing research, and future prospects, emphasising AI’s role not only in detection but also in predictive nutrient management. Additionally, it outlines a roadmap for farmers and stakeholders, guiding them in adopting these technologies by understanding their potential, selecting appropriate tools, and fostering a culture of innovation. Overall, integrating intelligent approaches into soil nutrient management paves the way for a more efficient, sustainable, and economically viable agricultural future. For successful adoption, farmers and stakeholders must embrace change, leverage available resources, and seek continuous learning and collaboration.

 

Author (s) Details

Pranav Raj
Banaras Hindu University, India.

 

 Please see the book here:- https://doi.org/10.9734/bpi/asti/v1/4722