Predicting Student Performance Using Deep Learning and Indian Knowledge Systems | Chapter 8 | New Horizons of Science, Technology and Culture Vol. 8

 

Background: Existing data-driven approaches have demonstrated promising predictive capabilities; however, many remain narrowly focused on technical optimisation. By treating behavioural data as context-free signals, these systems often overlook the cultural, social, and ethical dimensions that influence learning.

 

Aim: The aim of this chapter is to develop a culturally grounded and ethically responsible framework for predicting student academic performance by integrating mobile phone behavioural analytics with principles drawn from the Indian Knowledge System (IKS).

 

Objectives: The study seeks to (i) model student learning behaviour using temporally rich mobile usage data, (ii) enhance prediction accuracy and interpretability through deep learning architectures, (iii) operationalise IKS-inspired constructs to provide cultural and ethical context, and (iv) support fair, human-centred educational interventions.

 

Methods: An integrative multi-input deep learning framework is proposed that combines Temporal Convolutional Networks (TCN) for sequential behaviour modelling, attention mechanisms for feature prioritisation, and static psychometric and demographic feature fusion. Mobile phone data capturing usage patterns, activity rhythms, and engagement indicators are processed alongside IKS-informed contextual features. Model performance is evaluated against classical machine learning baselines using predictive and fairness-aware metrics, with interpretability analyses supporting transparent decision-making.

 

Results: Experimental validation on representative datasets demonstrates that the proposed framework consistently outperforms traditional machine learning models in terms of prediction accuracy and stability. Attention-based explanations reveal that IKS-inspired features contribute meaningfully to performance gains while reducing subgroup disparities. The results indicate improved fairness, enhanced interpretability for educators, and greater alignment with student well-being and learning rhythms.

 

Conclusion: The results indicate that embedding contextual and value-oriented dimensions improves not only predictive accuracy but also interpretability and fairness, making the system more aligned with real educational environments. The inclusion of explainability mechanisms further strengthens trust and transparency, which are critical for adoption in academic settings. Overall, this work contributes a meaningful step toward educational AI systems that support holistic learning, respect learner identity, and encourage responsible decision-making in data-driven education.

 

Novelty: The key novelty of this work lies in embedding indigenous knowledge principles directly into the design and interpretation of deep learning models for educational analytics. By bridging behavioural data, advanced neural architectures, and culturally rooted context, the framework advances a human-centric paradigm for academic performance prediction that emphasises ethical responsibility, cultural resonance, and holistic educational outcomes.

 

 

Author(s) Details

S. Vimala
Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli -2, Affiliated to Bharathidasan University, Tamil Nadu, India.

 

G. Arockia Sahaya Sheela
Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli -2, Affiliated to Bharathidasan University, Tamil Nadu, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/nhstc/v8/7176

Real-Time Object Detection via Cloud-Enabled Deep Learning: A Systematic Review | Chapter 6 | Mathematics and Computer Science: Research Updates Vol. 6

 

Automated vehicles, advanced surveillance systems, AR, and robots are just a few of the many new uses for real-time object recognition.  While deep learning models are becoming increasingly complex and accurate, they might be challenging to execute on edge devices with limited resources due to the computational demands. By offloading computationally intensive processes to scalable cloud infrastructure, cloud-enabled deep learning enables real-time processing without sacrificing detection accuracy, offering an effective alternative.  This study takes a close look at the current setup of cloud-based object recognition methods that work in real time. When considering latency, bandwidth, privacy, and processing costs, the pros and cons of several architectural paradigms are evaluated, including hybrid methodology, distributed inference, and edge-cloud cooperation. Additionally, the developments of lightweight convolutional neural networks (CNNs), single-shot detectors, and model compression techniques are examined, all of which are aimed at real-time performance in cloud environments.  Improving fault tolerance, optimizing data transmission, safeguarding data security and privacy, and developing more adaptive and efficient cloud resource management strategies for dynamic real-time object detection contexts are all areas that could be further explored in this review.

 

 

Author(s) Details

Abdul Razzak Khan Qureshi
Department of Computer Science, Medicaps University, Indore, Madhya Pradesh, India.

 

Ruby Bhatt
Department of Computer Science, Medicaps University, Indore, Madhya Pradesh, India.

 

Govinda Patil
Department of Computer Science, Medicaps University, Indore, Madhya Pradesh, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v6/5867

 

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

AI-Driven Wafer Inspection: Deep Learning, Transformers, and Generative Models for Defect Analysis in Semiconductor Manufacturing | Chapter 10 | Mathematics and Computer Science: Research Updates Vol. 8

 

Semiconductor manufacturing at advanced technology nodes demands inspection systems capable of identifying increasingly subtle, stochastic, and mixed-type defects. Traditional rule-based and handcrafted-feature approaches are no longer sufficient to address the complexity of modern wafer patterns, prompting the integration of artificial intelligence (AI) into high-volume manufacturing workflows. This chapter presents a unified framework for AI-driven wafer inspection that combines convolutional neural networks (CNNs), Vision Transformers (ViTs), and generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs). CNNs are effective for wafer map pattern classification, learning hierarchical spatial features that capture centre, edge-ring, and composite wafer failure modes. Transformers extend this capability by modelling long-range spatial dependencies, enabling improved performance in optical and SEM imaging scenarios where global context is essential. Generative models enhance sensitivity to rare or previously unseen defects by learning the underlying distribution of defect-free patterns and detecting deviations through reconstruction error or anomaly scoring.

 

 

 

A hybrid ViT–GAN architecture is introduced to demonstrate how discriminative and generative pathways can be fused to deliver high accuracy and low false-alarm rates across diverse defect classes. Extensive comparisons using public datasets and synthetic SEM-like datasets show that AI models substantially outperform classical techniques, particularly in low-sample regimes and in the presence of noise, illumination variations, or rotation. Deployment considerations, including inference speed, model compression, domain adaptation, and explainability, are discussed to highlight practical challenges in integrating AI into semiconductor fabs. The chapter concludes with emerging trends such as self-supervised learning, large vision models, multimodal data fusion, and temporal defect modelling, which are expected to shape the next generation of intelligent wafer inspection systems.

 

 

Author(s) Details

Balachandar Jeganathan
ASML, San Jose, CA, USA.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/6853

 

Enhanced Deep Learning Model for Accurate and Automated Detection of Hepatic Steatosis | Chapter 4 | Mathematics and Computer Science: Research Updates Vol. 8

 

Background: Hepatic Steatosis is one of the most prevalent liver disorders globally. Ultrasound imaging is widely used as the primary screening tool for Hepatic Steatosis. However, its diagnostic performance can vary significantly depending on the operator’s skill and the quality of the equipment. Recent advances in deep learning have brought new opportunities to medical imaging, providing automated, consistent, and quantitative assessments that reduce dependency on operator expertise.

 

Objectives: This study aims to develop a deep learning (DL)-based framework that enhances the detection and grading of Hepatic Steatosis from ultrasound images. The key goal is to achieve accuracy levels comparable to experienced radiologists while maintaining interpretability and efficiency for real-time use in clinical practice.

 

Methods: B-mode ultrasound images and cine clips were collected from patients, covering multiple liver views to capture diverse anatomical perspectives. Alongside imaging data, patient metadata such as age, body mass index (BMI), and comorbid conditions were also recorded to enrich the dataset. The proposed system employs a multi-view ultrasound preprocessing approach, followed by transfer learning to leverage existing feature representations. Attention-driven convolutional neural networks (CNNs) are then used to capture fine details across image regions. To ensure clinical usability, explainability modules are integrated, allowing transparent interpretation of model predictions.

 

Findings: Experimental evaluation demonstrated that the framework outperformed traditional single-view methods, offering improved sensitivity and specificity in detecting hepatic Steatosis. The performance was closely aligned with radiologist-level assessments. Furthermore, the system showed low latency, highlighting its suitability for near-real-time diagnostic applications.

 

Conclusion: Unlike conventional models that rely on a single static image, this study introduces a multi-view fusion strategy enhanced with attention mechanisms and explainability tools. This combination not only strengthens predictive accuracy but also ensures transparency and trustworthiness, critical factors for adoption in clinical settings. Despite the promising performance, challenges such as data variability, subtle early-stage disease patterns, and model interpretability remain. Addressing these limitations through larger, diverse datasets and explainable AI approaches will be essential for translating these models into clinical practice.

 

 

Author(s) Details

A. Sahaya Mercy
Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli-2, Affiliated to Bharathidasan University, Tamil Nadu, India.

 

G. Arockia Sahaya Sheela
Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli-2, Affiliated to Bharathidasan University, Tamil Nadu, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/6581

Novel Automatic Segmentation Approach for Early Brain Tumour Detection: Comparative Evaluation with AI Approaches | Chapter 5 | Medical Science: Updates and Prospects Vol. 1

 

Background: Automatic object detection in medical images is a crucial step in the diagnostic process. The problem of detecting brain tumours at an early stage is well advanced with deep learning algorithms (DLA) such as convolutional neural networks (CNN). The issue lies in the fact that these algorithms necessitate a training phase involving a large database of several hundred images, which can be time-consuming and require complex computational infrastructure.

 

Objective: This study aimed to comprehensively evaluate a proposed method, which relies on an active contour algorithm, for identifying and distinguishing brain tumours in magnetic resonance images.

 

Methods: The proposed algorithm was tested using brain images from the BRATS Challenges 2021, specifically focusing on glioma tumours. The proposed segmentation method is made up of an active contour algorithm, an anisotropic diffusion filter for pre-processing, active contour segmentation (Chan-Vese), and morphological operations for segmentation refinement.

 

Results: Its performance was evaluated using various metrics, such as accuracy, precision, sensitivity, specificity, Jaccard index, Dice index, and Hausdorff distance. The proposed method exhibited higher performance measures than most classical image segmentation methods and was comparable to the deep learning methods. These results indicate its ability to detect brain tumours accurately and rapidly.

 

Conclusion: The results section provided both numerical and visual insights into the similarity between segmented and ground truth tumour areas. The findings of this study highlighted the potential of computer-based methods in improving brain tumour identification using magnetic resonance imaging. Future work must validate the efficacy of these segmentation approaches across different brain tumour categories and improve computing efficiency to integrate the technology into potential clinical processes.

 

 

Author(s) Details

Mohammed Almijalli
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Faten A. Almusayib
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Ghala F. Albugami
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Ziyad Aloqalaa
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Omar Altwijri
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Ali S. Saad
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v1/6551

Agrosense: IOT-Based Smart Vegetable Gardening System|Chapter 6 | Intelligent IoT Systems: From Research to Real-World Solutions

 

Greenhouse farming needs precise monitoring to guarantee healthy plant growth, while conventional methods are cumbersome, inexact, and not amenable to steady-state operation. This study outlines an Internet of Things (IoT)-based vegetable garden monitoring system that automatically controls irrigation, climate, and plant disease in a greenhouse. The system incorporates an LDR light sensor, ESP32-CAM, soil moisture sensor, and DHT11 temperature-humidity sensor controlled by an ESP8266. A motor pump is activated when the soil moisture is less than the threshold value; temperature and humidity are controlled by a fan, while light is provided through an LED when light intensity is low. At specified time epochs, the ESP32-CAM shoots plant pictures that are analysed through a Python-based deep learning algorithm to determine five plant health classes: yellow leaves, curled leaves, leaf spot, white fly infestation, and healthy. A web page presents sensor readout in real time as well as disease classification outcome. Experimental validation indicates classification accuracy of 94.6 %, ±2°C accuracy in temperature regulation, ±5% in humidity control, and 92% accuracy in irrigation. Against previous works that employed IoT to automate greenhouse farming, this solution is not only superior in control accuracy but also incorporates automatic disease diagnosis. Despite limitations due to internet reliability as well as dataset size, the solution provides insights into how IoT, as well as AI, can minimize manual intervention, facilitate early detection, as well as, promote sustainable farming.

 

 

Author(s) Details

Hanamant R Jakaraddi
Department of MCA, Acharya Institute of Technology, Bangalore-560107, India.

 

Hridhik Hareendran
Department of MCA, Acharya Institute of Technology, Bangalore-560107, India.

 

Sridevi G
Department of Computer Applications, Acharya Institute of Graduate Studies, Bangalore-560107, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-88417-94-5/CH6

The Fast-evolving Landscape of Deep Learning: What’s New and What’s Next | Chapter 3 | Engineering Research: Perspectives on Recent Advances Vol. 10

 

Deep learning is rapidly evolving with transformative breakthroughs in foundation models (GPT-4, Gemini), generative AI (diffusion models, video synthesis), and self-supervised learning, reducing reliance on labelled data. Key advances in efficient AI (edge computing, model compression) and reinforcement learning (robotics, autonomous systems) are expanding practical applications. Emerging frontiers like neurosymbolic AI and AGI research highlight both progress and unresolved challenges. This chapter examines these cutting-edge developments, offering insights into deep learning’s current state and future trajectory.

 

 

Author(s) Details

K. Sridhar
Department of Computer Science and Engineering, Malla Reddy (MR) Deemed to be University, Maisammaguda (H), Gundlapochampally (V), Medchal - Malkajgiri District, Telangana, India.

 

Ravikumar Thallapalli
Department of Computer Science and Engineering, Vaageswari College of Engineering (Autonomous) Accredited by NAAC A+, Beside LMD Police Station, Karimnagar, Telangana, India.

 

M. Srinivas
Department of Computer Science and Engineering (AI&ML), Vaageswari College of Engineering (Autonomous) Accredited by NAAC A+, Beside LMD Police Station, Karimnagar, Telangana, India.

 

P. Venkateshwarlu
Department of Computer Applications, Vaageswari College of Engineering (Autonomous) Accredited by NAAC A+, Beside LMD Police Station, Karimnagar, Telangana, India.

 

Please see the book here :-https://doi.org/10.9734/bpi/erpra/v10/6079

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

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

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

Breaking the Mould: Rethinking Deep Learning with Unconventional Architectures | Chapter 2 | Mathematics and Computer Science: Research Updates Vol. 5

Deep learning has become the cornerstone of modern artificial intelligence, enabling breakthroughs in areas such as computer vision, natural language processing, and robotics. However, traditional deep-learning approaches face significant challenges, including data hunger, computational costs, and a lack of interpretability. This chapter explores unconventional pathways in deep learning that address these limitations and push the boundaries of AI. This paper delves into neuroevolution, spiking neural networks, capsule networks, and quantum machine learning, highlighting their unique advantages, challenges, and applications. Additionally, the study discussed the ethical considerations of these emerging technologies, emphasising the need for responsible development. By examining these unconventional approaches, this chapter aims to inspire researchers and practitioners to explore new frontiers in deep learning and unlock its full potential.

 

Author (s) Details

K. Sridhar
Department of Computer Science and Engineering, Vaageswari College of Engineering, (Autonomous) Accredited by NAAC A+, Beside L.M.D Police Station, Karimnagar, India.

Goolla Mamatha
Department of CSE(AI&ML), Vaageswari College of Engineering, (Autonomous) Accredited by NAAC A+, Beside L.M.D Police Station, Karimnagar, India.

 

Sabbani Anitha
Department of CSE, Vaageswari College of Engineering, (Autonomous) Accredited by NAAC A+, Beside L.M.D Police Station, Karimnagar, India.

 

Krishnaveni Bandari
Department of CSE, Vaageswari College of Engineering, (Autonomous) Accredited by NAAC A+, Beside L.M.D Police Station, Karimnagar, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcsru/v5/5081

Artificial Intelligence in Radiology and Pathology: Transforming Medical Imaging Interpretation | chapter 5 | Medical Science: Recent Advances and Applications Vol. 4

Artificial intelligence (AI) has revolutionised the fields of radiology and pathology, significantly enhancing the accuracy, efficiency, and accessibility of medical imaging interpretation. AI-driven algorithms, particularly deep learning and machine learning models, have demonstrated remarkable capabilities in detecting, classifying, and segmenting pathological findings in medical images, including X-rays, CT scans, MRIs, and histopathological slides. These advancements not only aid in early disease detection and diagnosis but also facilitate workflow optimisation, reducing radiologists' and pathologists' workload. Furthermore, AI-driven predictive models contribute to precision medicine by enabling personalised treatment plans. However, challenges such as data privacy, ethical concerns, and the need for robust validation limit widespread clinical adoption. This review explores the current applications of AI in radiology and pathology, its impact on diagnostic accuracy, and the challenges that must be addressed for seamless integration into clinical practice.

 

Author (s) Details

Divyesh Goswami

Department of Pathology, Nootan Medical College and Research Center, Visnagar, Gujarat, India.

 

Amar C Sajjan
Department of Microbiology, Chalmeda Anand Rao Institute of Medical Sciences, Karimnagar, Telangana, India.

 

Ruchi Yadav
Department of Pathology, Maharishi Chyawan Government Medical College Koriyawas, Mahendergarh, Narnaul, Haryana, India.

 

Nipa Hathila
Department of Radiodiagnosis, Pacific Medical College and Hospital, Udaipur, Rajasthan, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/msraa/v4/5423

AI-Powered Simulations for Medical Training: A Game-Changer in Skill Development | Chapter 4 | Medical Science: Trends and Innovations Vol. 13

The integration of Artificial Intelligence (AI)-powered simulations in medical training has revolutionised skill development by providing an interactive, immersive, and adaptive learning environment. These simulations leverage machine learning, virtual reality (VR), augmented reality (AR), and deep learning algorithms to create realistic clinical scenarios, enabling medical professionals to practice decision-making, procedural techniques, and diagnostic skills in a risk-free setting. AI-driven simulations enhance competency-based education, personalised learning, and real-time feedback, ensuring improved clinical proficiency and patient safety. This book chapter explores the role of AI-powered simulations in medical education, their impact on cognitive and psychomotor skill acquisition, and the challenges associated with their adoption. The findings suggest that AI-based simulations are a game-changer in medical training, offering scalable, cost-effective, and high-fidelity learning experiences that bridge the gap between theoretical knowledge and real-world clinical practice.

 

Author (s) Details

 

Latif Bagwan
Department of General Surgery, Nootan Medical College and Research Centre, Visnagar, Gujarat, India.

 

Rajesh Gouri
Department of General Surgery, Gujarat Adani Institute of Medical Sciences, Bhuj, Gujarat, India.

 

Avani V Mendpara
H J Doshi Hospital, Rajkot, Gujarat, India.

 

Azmat Kamal Ansari
Department of Biochemistry, Uttar Pradesh University of Medical Sciences, Saifai, Etawah, Uttar Pradesh, India.

 

Brijesh R. Koyani
H J Doshi Hospital, Rajkot, Gujarat, India.

 

Please see the book here:-  https://doi.org/10.9734/bpi/msti/v13/5223

SIAS: Sensory Impairment Assistive Software | Chapter 16 | Leading the Charge: A Guide to Management, Entrepreneurship and Technology in the Dynamic Business Landscape Edition 1

SIAS – Sensory Impairment Assistive Software, presents an innovative application of YOLO (You Only Look Once), and CNN (Convolutional Neural Network). Through Python-based deep learning techniques, the system leverages YOLO’s speed and accuracy to identify objects within a live camera feed, providing vocal announcements to the user. Furthermore, it offers a few additional features, which include gesture-to-speech conversion to facilitate communication between the physically challenged, Text-to-speech conversion, and Image Processing. This proposes an innovative communication system framework for deaf, dumb and blind people within a single compact device.

 

Author (s) Details

 

Supriya C

Department of Information Science and Engineering, Acharya Institute of Technology, Bengaluru, Karnataka -560107, India.

 

Janavi Mahesh
Department of Information Science and Engineering, Acharya Institute of Technology, Bengaluru, Karnataka -560107, India.

 

Karan
Department of Information Science and Engineering, Acharya Institute of Technology, Bengaluru, Karnataka -560107, India.

 

Keerthana K S
Department of Information Science and Engineering, Acharya Institute of Technology, Bengaluru, Karnataka -560107, India.

 

Navyashree R
Department of Information Science and Engineering, Acharya Institute of Technology, Bengaluru, Karnataka -560107, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48859-98-3/CH16

Application of Machine Learning for the Tracing of Jellyfish Attack in MANETs | Chapter 4 | Science and Technology: Developments and Applications Vol. 2

Mobile ad-hoc networks (MANETS) are one of the emerging fields that have seamless applications in the field of emergency situations like rescue operations, commercial applications like virtual classrooms, medical like disease diagnosis etc. In the era of driverless vehicles, mobile ad-hoc network (MANET) finds a useful place in discoveries. However, MANETS are vulnerable to a number of attacks because of properties like non-existing infrastructure, dynamic topology, multihop network etc. A lot of previous works have focused on the impact of various attacks on routing protocols like jellyfish attacks, blackhole attacks or selfish node attacks. The area becomes more vulnerable to attacks as a network is in use for a limited short time as the topology is highly dynamic and time-specific. However, the use of machine learning and deep learning algorithms has given a new edge to MANETS in driverless vehicles. In literature, malicious node/ selfish node detection (passage of wrong information/ blockage of information) was done by using various supervised or unsupervised machine learning algorithms like KNN, SVM, CNN or recurrent networks. This paper presents the detection of various faulty nodes using the NS2 simulator and using machine learning algorithms. The study concluded that the application of machine learning and deep learning algorithms has increased the faulty node detection accuracy as compared to simulated code. The increased accuracy may be helpful in the formation of short-term and highly dynamic MANETS like driverless cars or military networks. In future, these networks may be connected with satellite images so as to forecast whether information like various DANA in Orissa, and SARA in US pandemics and take precautionary actions accordingly.

 

Author (s) Details

Bhawna Singla
Geeta University, India.

A. K. Verma
Thapar University, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v2/3679

Artificial Intelligence in Dental Implant Identification: A Comprehensive Overview | Chapter 4 | Medical Science: Trends and Innovations Vol. 7

Background: Dental implantology has significantly transformed the field of restorative dentistry, providing patients with long-term, functional, and aesthetic solutions for missing teeth. As the demand for implants increases globally, the need for effective and accurate implant fixture identification has become more crucial.

Aim: This review aims to explore the role of artificial intelligence (AI) in dental implant identification, focusing on its applications, benefits, and challenges in clinical practice. The study examines AI-driven tools and their impact on diagnostic accuracy, clinical decision-making, and treatment planning.

Methodology: A comprehensive literature review was conducted using Medline (PubMed) and Google Scholar databases in January 2025. The search targeted studies and reviews on AI applications in dental implant identification, analyzing technological advancements and their clinical implications. A total of 28 relevant articles were selected for assessment.

Results: AI-powered tools, such as Spotimplant.com, Implantif.ai, and AI2D, have demonstrated high accuracy in identifying dental implants from radiographic images. Studies have shown that AI-based systems can improve identification precision by up to 25% compared to traditional methods. These technologies streamline the identification process, reduce human error, and enhance treatment planning. However, challenges remain, including database limitations, difficulties in complex cases, and the need for regulatory compliance.

Conclusion: AI-driven implant identification offers significant advantages in improving diagnostic accuracy and clinical efficiency. While current AI tools present challenges related to data quality, regulatory frameworks, and integration into clinical workflows, ongoing advancements are expected to enhance their reliability and applicability. Future research should focus on expanding AI training datasets, optimizing deep learning models, and integrating AI into digital dental workflows for personalized treatment planning.

 

Author (s) Details

 

Hanen Boukhris
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Ghada Bouslama
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Hajer Zidani
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Kawther Bel Haj Salah
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Souha BenYoussef
Department of Dental Medicine, University Hospital Farhat Hached Sousse, LR12SP10, University of Sousse, Tunisia.

 

Please see the book here:- https://doi.org/10.9734/bpi/msti/v7/4409

Federated Learning and Deep Learning: A New Frontier for Real-World Applications | Chapter 9 | Science and Technology: Developments and Applications Vol. 3

Both federated learning and deep learning are two of the most discussed, maybe biggest technologies in real-world application development across many industries and full of business significance, so in this chapter we will take a look at these interdisciplinary research aspects and explain their relationship. Federated learning provides a decentralized framework where models can be trained collaboratively without having to disclose sensitive data. The combination of federated learning and deep le data in diverse domains like healthcare, finance, smart devices, autonomous vehicles, and retail but much more useful due to the fact that deep learning can automatically extract effective and complex feature representation with a sufficient amount of data. The chapter highlights the importance of these two paradigms in solving critical data privacy issues and promoting innovations by examining how they work together, their benefits and drawbacks. It also offers recommendations for future research and applications, stressing the need for ethics in AI and the spirit of interdisciplinary collaboration. This convergence, therefore, represents another frontier in artificial intelligence that can provide solutions that are more secure, inclusive, and impactful in a world that is increasingly driven by data.

Recent research has emerged in the form of federated learning of deep neural networks (DL), which showcases its potential for data privacy in a diverse range of applications, including healthcare, finance, smart devices, autonomous vehicles, and retail.

 

Author (s) Details

Sridhar K.
Department of CSE, Vaageswari College of Engineering, Karimnagar, Telangana, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v3/3199

Leveraging Artificial Intelligence for Brain Tumor Classification | Chapter 3 | Science and Technology: Developments and Applications Vol. 5

Brain cancer, characterized by the uncontrolled growth of abnormal cells in the brain, is a severe neurological disorder that can be either primary or metastatic. Early detection and accurate classification of brain tumors are crucial for effective management and improved patient outcomes. Brain tumors are classified based on various factors such as their nature, cell origin, grade, and progression stage. Traditional methods of detection, segmentation, and classification are time-consuming, require extensive expertise, and are prone to errors. Artificial Intelligence (AI), including its subtypes Machine Learning (ML) and Deep Learning (DL), holds promise for improving accuracy and expediting detection. AI-based technologies can be categorized into binary classification (e.g., determining whether a tumor is malignant or benign) and multimodal classification (e.g., categorizing tumors into various types). Most AI applications in brain tumor classification focus on radiological images, particularly Magnetic Resonance Imaging (MRI).

AI-based technologies must achieve high accuracy to be effectively integrated into real-life clinical practice. This chapter summarizes the current advances in AI techniques for brain tumor classification, highlighting their potential and ongoing challenges.

 

Author (s) Details

 

Adham Al-Rahbi
Sultan Qaboos University, College of Medicine and Health Sciences, Muscat, P.O. Box-35, Postal Code 123, Sultanate of Oman.

Tariq Al-Saadi
Department of Neurosurgery, Khoula Hospital, Muscat, P.O. Box-35, Postal Code 123, Sultanate of Oman and Department of Neurosurgery, Cedars-Sinai Medical Centre, 8700 Beverly Blvd, Los Angeles, CA 90048, USA.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v5/1964

Optimizing Quinoline Derivatives for ABCB1 Inhibition: A Machine Learning Approach to Combat Multidrug Resistance in Cancer | Chapter 10 | Current Innovations in Chemical and Materials Sciences Vol. 9

 

A vast array of human tumors contain multidrug resistance (MDR) proteins linked to the ATP-binding cassette family, which lead to treatment failure. One of the mechanisms of multiple drug resistance is the overexpression of efflux pumps, like ABCB1. In order to predict the inhibitory biological activity towards ABCB1, the goal of this paper is to develop a robust quantitative structure-activity relationship (QSAR) model that best describes the correlation between the activity and the molecular structures. Using various linear and non-linear machine learning (ML) regression techniques, such as k-nearest neighbors (KNN), decision trees (DT), back propagation neural networks (BPNN), and gradient boosting-based (GB) methods, a series of quinoline derivatives of eighteen compounds were examined in this regard. Their goal is to identify the source of these compounds' activity in order to create new quinoline derivatives that have a stronger effect on ABCB1. A total of sixteen machine learning (ML) predictive models were created using varying numbers of 2D and 3D descriptors. The statistical metrics root mean square error (RMSE) and coefficient of determination (R2) were used to assess the models. With one descriptor, represented by R2 and RMSE of 95% and 0.283, respectively, a GB-based model, specifically catboost, achieved the highest predictive quality among all developed models. The outward-facing p-glycoprotein (6C0V) was the target crystal structure for molecular docking studies, and the results showed strong binding affinities via both hydrophobic and H-bond interactions with the relevant compounds. At -9.22 kcal/mol, the 17 has the highest binding energy. As a result, it is possible that structure 17 will prove to be a useful potential lead structure for the synthesis and design of more effective P-glycoprotein inhibitors that can be combined with anti-cancer medications to manage cancer multidrug resistance.

 

Author(s) Details:

Mouad Lahyaoui,
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

Riham Sghyar
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

 

Yousra Seqqat
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

Fouad Ouazzani Chahdi
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

Ahmed Mazzah
University of Lille, CNRS, USR 3290, MSAP, Miniaturization for Synthesis, Analysis and Proteomics, Lille, France.


Amal Haoudi
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

Taoufiq Saffaj
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

Youssef Kandri Rodi
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

Please see the link here: https://stm.bookpi.org/CICMS-V9/article/view/14341