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

A Comprehensive Review of Advanced AI/ML Applications in Solar Radiation Prediction | Chapter 9 | Scientific Research, New Technologies and Applications Vol. 7

Recent advancements in AI/ML have focused on improving the robustness of solar radiation predictions through hybrid models, combining machine learning algorithms with traditional physical models, or blending multiple ML techniques. Solar energy is one of the most abundant renewable energy sources available globally, and accurate solar radiation prediction is crucial for efficient energy management, grid optimization, and the development of solar technologies. The advent of machine learning and artificial intelligence has revolutionized the field of solar radiation forecasting, offering more accurate, scalable, and adaptive models. In India, using AI/ML for solar radiation prediction is challenged by inconsistent data quality due to limited, fragmented measurement stations across diverse climates. Large-scale, high-quality datasets are often unavailable, especially in remote or rural areas. The high computational resource requirements further strain infrastructure, particularly in less developed regions. Additionally, interpreting AI/ML models in this context can be difficult, limiting trust in predictions and wider adoption across the country. This review provides a comprehensive overview of the state-of-the-art ML and AI approaches used for solar radiation prediction, including traditional models such as regression and time-series analysis, as well as advanced techniques like deep learning and ensemble models. Additionally, the article discusses the challenges, limitations, and future directions in this field. The future of ML and AI in solar radiation prediction looks promising, with potential advancements in quantum computing and the integration of edge computing for real-time forecasting.

 

Author (s) Details

 

R. Meenal
V.S.B Engineering College, Karur - 639111, TN, India.

 

R. Devprakash
Lincoln University College, Petaling Jaya, 47301, Malaysia.

 

E. Rajasekaran
V.S.B Engineering College, Karur - 639111, TN, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v7/2935

Bridging Gaps: Transformative Applications of Medical Communication in Healthcare Insurance | Chapter 2 | New Advances in Business, Management and Economics Vol. 1

Medical communication plays a significant role in the healthcare insurance industry, serving as a critical tool for accurate claims processing, patient education, and regulatory compliance. By translating complex medical information into clear and actionable formats, medical writers enhance transparency, reduce the risk of fraud, support efficient decision-making processes and enhance collaboration in healthcare communication. Emerging technologies such as digital health platforms, artificial intelligence (AI), and big data analytics are driving transformative changes in the industry. These innovations improve the precision and accessibility of medical documentation, while also enabling predictive analytics and more robust risk assessment models. Through structured communication, medical writers play a pivotal role in bridging gaps between healthcare providers, insurers, and patients, fostering trust and improving operational workflows. This study explores the diverse applications of medical writing within the insurance sector, emphasizing its critical role in addressing industry complexities, exploring future trends in medical writing and communication, and driving progress toward a more transparent, patient focused, and technology-driven insurance ecosystem.

 

Author (s) Details

 

Mitesh Mohan Hood
Medical Excellence, Pristyn Care; India.

 

Shaloo Varma
Medical Directorate, Pristyn Care; India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nabme/v1/3877

The Analysis of the Risk Management in Child Obesity Using Deep Learning Neural Network | Chapter 21 | Innovative Solutions: A Systematic Approach towards Sustainable Future

The research proposal is to manage and monitor the obesity in children by the huge database. The data base meticulously analyses the data and interpret it and derive complete details about the growth process in children by using the neural network model that identifies the obesity risk. The model then dutifully alerts parents when there is an observable increase in the child's weight, advising them proactively on what measures need to be taken to address and mitigate this weight gain. In addition to tracking the child's physical activity, the device provides parents with graphs and reports that show how the child has been performing.

The study aims at developing a neural network weights model to recognize the risk of obesity by considering the parameters like Body mass index, physical fitness level, normal heart beat rate, jumping points. The model will be trained and tested on a dataset of medical data. The performance of the model will be assessed in terms of accuracy, precision, and recall. The model will then be used to classify people into risk categories. The model will be trained using supervised learning techniques, with the relevant parameters as input and the obesity risk as the output. The model will then be tested on the dataset to evaluate the accuracy, precision, and recall. Finally, the model will be used to classify people into risk categories.

 

Author (s) Details

 

Ramesh
Department of Civil, Sri Siddhartha Institute of Technology, Tumkur, India.

 

Divyashree D V
Department of Management studies, MES Institute of Management, Bangalore, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49238-47-3/CH21

Artificial Intelligence (AI) and Its Application in Pharmacy | Chapter 12 | Advances and Challenges in Science and Technology Vol. 9

 The AI is an active tool for data excavating based on the gigantic pharmacological data and machine learning process. Therefore, AI has been used in again drug design, activity scoring, in essence screening and in silico judgment in the properties (absorption, classification, metabolism, excretion and toxicity) of a drug particle. AI has made important contributions to the healthcare industry in any of areas, including the administration and storage of data and news about patient medical histories, cure stocks, sale records, and more; automated appliance; software and computer uses; and diagnostic finishes like CT and MRI diagnostics. All of these have been grown to support and streamline healthcare procedures. As expected, artificial intelligence (AI) has transformed healthcare expected more effective and adept, and the pharmaceutical industry is not exempt. All along the past few years, a substantial amount of increasing interest in the uses of AI science has been identified for resolving as well as interpreting few important fields of pharmacy like drug finding, dosage form crafty, poly pharmacology, and hospital pharmacy. AI-located solutions have been labeled which involve platforms that can make use of a difference of data types viz. manifestations reported for one patients, biometrics, imaging, biomarkers, etc. We engaged to produce a thorough report that would aid every undertaking pharmacist in understanding the major progresses made likely by the application of machine intelligence (AI), in light of the field's expanding meaning. In order to conclude the outbreaks of COVID-19, Zika, Ebola, and seasonal influenza, deep education and neural networks were utilized. Accompanying the advancement of AI technologies, the experimental community concede possibility witness rapid and cost-effective healthcare and drug research as well as provide upgraded service to the society.

Author(s) Details:

R. Radha,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

V. Neelima,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

M. P. VedaVarshan,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

S. Saqib Basha,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

U. Jeevitha,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

M. A. KapilKumar,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

G. Sateesh Kumar,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

Please see the link here: https://stm.bookpi.org/ACST-V9/article/view/12634

Artificial Intelligence Plus Plus (AI++): A Novel Approach | Chapter 4 | Recent Advances in Mathematical Research and Computer Science Vol. 1

The term "Artificial Intelligence Plus Plus (AI++)" was coined, invented, and defined in this chapter. Super Computational Intelligence is a sub-field of Artificial Intelligence Plus Plus.


Author (S) Details

Satish Gajawada
Alumnus, Indian Institute of Technology, Roorkee, Uttaranchal, India.

Hassan M. H. Mustafa
Benha University, Egypt.

View Book :- https://stm.bookpi.org/RAMRCS-V1/article/view/4334

Feasibility of Artificial Insemination Network for Egyptian Buffalo Development: A Literature Reviews | Chapter 13 | Research Aspects in Agriculture and Veterinary Sciences Vol. 1

 Egypt has a competitive advantage in milk production rather than red meat production, particularly from buffalo, according to literature evaluations. Furthermore, water resources are becoming increasingly restricted, limiting fodder acreage horizontal expansion. Furthermore, there is strong competition for available agricultural land resources between food and feed needs. As a result, horizontal dairy buffalo stock expansion is difficult. As a result, vertical expansion through improved milk output is the sole option for buffalo development in Egypt to address the existing domestic milk production gap. The Egyptian customer loves buffalo milk because of its vibrant colour and high total solids content, notably fat. Buffalo milk is more expensive than cow milk, yet its production is growing at a quicker rate than cow milk's. An artificial insemination (AI) network is being used to speed up the anticipated genetic improvement of buffalo milk output. A recent study [1] showed that the return on genetic investment in dairy buffalo might be achieved (IRR = 19.71%). Egypt, on the other hand, has only two AI-centers for selected buffalo sires, supplying four AI-units, according to official figures. As a result, the purpose of this research was to determine the feasibility of constructing an AI network in Egypt by evaluating (NPV, IRR, and payback period) as well as the sensitivity of the proposed programme to negative developments. An AI-unit of buffaloes' semen and an AI-Center for raising buffalo sires in the Nile Delta were used in the study, as well as data from a field survey. While the Egyptian economy's average discount rate was 17.5 percent, the predicted IRR for one AI-unit was roughly 35 percent under the most likely scenario, according to the data. IRRs of roughly 28 percent and 31 percent, respectively, would arise from a 10% fall in semen price and a 10% increase in insemination costs. The AI-projected center's IRR under the most likely circumstances was around 31%. A 10% decrease in the price of sperm and a 10% increase in feed costs or the price of the sire would result in 26 percent, 30 percent, or 28 percent savings, respectively. As a result, the lowest semen dose sale price is the most effective variable on the IRR. Unfavorable adjustments, on the other hand, would keep high-return investments in building a viable AI-Network for significantly expanding dairy buffalo milk yield.

Author (s) Details

Ibrahim Soliman
Department of Agricultural Economics, Faculty of Agriculture, Zagazig University, Zagazig, Egypt.

Ahmed F. Mashhour
Department of Agricultural Economics, Faculty of Agriculture, Zagazig University, Zagazig, Egypt.

View Book :- https://stm.bookpi.org/RAAVS-V1/article/view/2427

Enhancing the Elementary School’s Students’ Analytic Thinking and Decision-making Abilities through AI-assisted Program in the GM5 Game Playing Activities: Recent Development | Chapter 5 | Modern Perspectives in Language, Literature and Education Vol. 6

 The GM5 game is intended to assist children in overcoming computational thinking and decision-making difficulties. Through step-by-step examination, pupils' thinking, calculating, and identifying solutions to avoid defeat by opponents can be deeply systemically designed in their minds. Selecting cards, analysing card combinations' risk vectors, appraising opponents, planning allocations, and deceiving opponents are the five time-limited processes in this game. The game's logic analysis, mathematical processes, and strategic/tactical moves form a sophisticated chain reaction. As a result, this game has the potential to increase the player's analytical strategic thinking and decision-making skills dramatically. To reach more significant educational goals, however, a structured learning plan and support aids are required.

As a result, this study creates an AI software to help students reflexively rethink and improve their game strategies, as well as a 5-E learning cycle enabling students to gain systemic game-playing abilities, experiences, and mindsets. This will be an excellent teaching strategy for teachers who wish to prepare kids to learn independently.

Author (s) Details

Wu Pei-Yu
Taipei Municipal Kuanfu Primary School, Taipei, Taiwan.

Chen Chien-Chi
Taipei Municipal Kuanfu Primary School, Taipei, Taiwan.

Lai Ah-Fur
University of Taipei, Taipei, Taiwan.

View Book :- https://stm.bookpi.org/MPLLE-V6/article/view/2308

A Study on Human Capital: A Focus Study on Schools of Business (SOBS) in Telangana & Karnataka | Chapter 2 | Current Strategies in Economics and Management Vol.6

 

In the Digi-Era of Internet of Things (IOT), Virtual and Viral Technologies, Artificial Intelligence (AI),
the Analytics reshaped and redefined the Science of Workforce. By the Year 2025, 75% of the Total
Workforce will be Millennials, yet, large percentage of Millennials prefer Non-Profits, Small Business
or Entrepreneurship. (www.oracle.com). An Intuit Global Study predicts that by 2020, the 40% of the
Gig Economy will be the Contingent Workers. The Golden Age of Human Capital Management shifts
from Employee Engagement to Employee Experience enroute Employee Turnover. According to
Bersin by Deloitte Research (2018), the Average Voluntary Turnover Rate is 13% and it impairs the
growth indices (PwC, 2017). Lack of analytic acumen or skills among HR Professionals (HBR) has
caught fire recently with a welter of reports, books and commentary. In the light of the above, Human
Capital Talentum Analytics (HCTA) emerged as one of the most predictive and a powerful tool that
attracts, hires and retains talents resulting increased revenue, customer satisfaction or market share.

Author (s) Details

Dr. K. Bhanu Prakash
Professor& CFO, MN INFRA PVT., LTD., Tadepalligudem, India.

Mr. P. Siva Reddy
Assistant Professor, School of Management Studies, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/254