Integration Of Artificial Intelligence Techniques Research Articles

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

The global iron ore price is influenced by numerous factors, thus showcasing a complex interplay among them. The collective expectations of market participants over time shape the variations and trends within the iron ore price time series. Consequently, devising a robust forecasting model for the volatility of iron ore prices, as well as for other assets connected to this commodity, is critical for guiding future investments and decision-making processes in mining companies. Within this framework, the integration of artificial intelligence techniques, encompassing both technical and fundamental analyses, is aimed at developing a comprehensive, autonomous hybrid system for decision support, which is specialized in iron ore asset management. This approach not only enhances the accuracy of predictions but also supports strategic planning in the mining sector.

Application of fuzzy logic in multi-sensor-based health service robot for condition monitoring during pandemic situations

PurposeThe purpose of this study is to plan and develop a cost-effective health-care robot for assisting and observing the patients in an accurate and effective way during pandemic situation like COVID-19. The purposed research work can help in better management of pandemic situations in rural areas as well as developing countries where medical facility is not easily available.Design/methodology/approachIt becomes very difficult for the medical staff to have a continuous check on patient’s condition in terms of symptoms and critical parameters during pandemic situations. For dealing with these situations, a service mobile robot with multiple sensors for measuring patients bodily indicators has been proposed and the prototype for the same has been developed that can monitor and aid the patient using the robotic arm. The fuzzy controller has also been incorporated with the mobile robot through which decisions on patient monitoring can be taken automatically. Mamdani implication method has been utilized for formulating mathematical expression of M number of “if and then condition based rules” with defined input Xj (j = 1, 2, ………. s), and output yi. The inputs and output variables are formed by the membership functions µAij(xj) and µCi(yi) to execute the Fuzzy Inference System controller. Here, Aij and Ci are the developed fuzzy sets.FindingsThe fuzzy-based prediction model has been tested with the output of medicines for the initial 27 runs and was validated by the correlation of predicted and actual values. The correlation coefficient has been found to be 0.989 with a mean square error value of 0.000174, signifying a strong relationship between the predicted values and the actual values. The proposed research work can handle multiple tasks like online consulting, continuous patient condition monitoring in general wards and ICUs, telemedicine services, hospital waste disposal and providing service to patients at regular time intervals.Originality/valueThe novelty of the proposed research work lies in the integration of artificial intelligence techniques like fuzzy logic with the multi-sensor-based service robot for easy decision-making and continuous patient monitoring in hospitals in rural areas and to reduce the work stress on medical staff during pandemic situation.

Current MR imaging of cartilage in the context of knee osteoarthritis (part1) : Principles and sequences

Magnetic resonance imaging (MRI) is the clinical method of choice for cartilage imaging in the context of degenerative and nondegenerative joint diseases. The MRI-based definitions of osteoarthritis rely on the detection of osteophytes, cartilage pathologies, bone marrow edema and meniscal lesions but currently ascientific consensus is lacking. In the clinical routine proton density-weighted, fat-suppressed 2D turbo spin echo sequences with echo times of 30-40 ms are predominantly used, which are sufficiently sensitive and specific for the assessment of cartilage. The additionally acquired T1-weighted sequences are primarily used for evaluating other intra-articular and periarticular structures. Diagnostically relevant artifacts include magic angle and chemical shift artifacts, which can lead to artificial signal enhancement in cartilage or incorrect representations of the subchondral lamina and its thickness. Although scientifically validated, high-resolution 3D gradient echo sequences (for cartilage segmentation) and compositional MR sequences (for quantification of physical tissue parameters) are currently reserved for scientific research questions. The future integration of artificial intelligence techniques in areas such as image reconstruction (to reduce scan times while maintaining image quality), image analysis (for automated identification of cartilage defects), and image postprocessing (for automated segmentation of cartilage in terms of volume and thickness) will significantly improve the diagnostic workflow and advance the field further.

An Exploration of the Pepper Robot’s Capabilities: Unveiling Its Potential

The application of social robots is increasing daily due to their various capabilities. In real settings, social robots have been successfully deployed in multiple domains, such as health, education, and the service industry. However, it is crucial to identify the strengths and limitations of a social robot before it can be employed in a real-life scenario. In this study, we explore and examine the capabilities of a humanoid robot, ‘Pepper’, which can be programmed to interact with humans. The present paper investigates five capabilities of Pepper: mapping and navigation, speech, hearing, object detection, and face detection. We attempt to study each of these capabilities in-depth with the help of experiments conducted in the laboratory. It has been identified that Pepper’s sound and speech recognition capabilities yielded satisfactory results, even with various accents. On the other hand, Pepper’s built-in SLAM navigation is unreliable, making it difficult to reach destinations accurately due to generated maps. Moreover, its object and face detection capabilities delivered inconsistent outcomes. This shows that Pepper has potential for improvement in its current capabilities. However, previous studies showed that with the integration of artificial intelligence techniques, a social robot’s capabilities can be enhanced significantly. In the future, we will focus on such integration in the Pepper robot, and the present study’s exploration will help to establish a baseline comprehension of the in-built artificial intelligence of Pepper. The findings of the present paper provide insights to researchers and practitioners planning to use the Pepper robot in their future work.

A Comprehensive Review of Artificial Intelligence Algorithms Applications for Distributed Generation Sizing and Location Optimization to Enhance Efficiency and Reliability Indices in Radial Distribution Systems

The research aimed to understand radial distribution systems by defining them and exploring their strategies, and to explore methods for applying artificial intelligence algorithms in radial distribution systems, It also aimed to clarify types of indicators in radial distribution systems, highlighting the importance of providing a scientific and applied framework for integrating artificial intelligence in the field of energy distribution. The significance of the research lies in presenting a comprehensive and up-to-date approach based on the applications of artificial intelligence algorithms to improve the design and organization of distributed production systems in radial distribution systems. By employing a descriptive and analytical approach, the researcher was able to reach several conclusions, with the most prominent being that the application of artificial intelligence algorithms in radial distribution systems contributes to improving energy efficiency by directing it more accurately and effectively, the ability of artificial intelligence to provide precise data-dependent analyses enhances decision-making processes in radial distribution systems, The integration of artificial intelligence techniques contributes to improving overall efficiency in radial distribution systems, reducing energy losses, and enhancing energy distribution effectively. The research recommends enhancing integration between artificial intelligence techniques and radial distribution systems to ensure effective and smooth interaction, thereby increasing efficiency, Priority should be given to the safety and protection of data, and effective cybersecurity measures should be implemented to address the growing challenges in this context.

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

AbstractPrecise monitoring and diagnosis of epilepsy by manual analysis of EEG signals are challenging due to the low doctor‐to‐patient ratio, and shortage of medical resources. To automate this diagnosis in real‐time, EEG based Brain–Computer Interface (BCI) system with integration of artificial intelligence techniques will prove to be propitious. This work proposes an end‐to‐end, one‐dimensional atrous conv‐net‐based architecture for automatic epilepsy diagnosis using EEG signals with a conceptual framework of the EEG‐BCI system for routine monitoring and clinical use. The proposed architecture has a robust backbone of six blocks of atrous convolutional layers activated with exponential linear unit functions. The six blocks are followed by the addition of a long short‐term memory layer for automatic feature extraction and sequential EEG data analysis. The efficacy of the proposed architecture has been verified on three publicly available EEG datasets using various evaluation metrics, feature maps, test set evaluation, and ablation studies. An average training and validation accuracy of 96.16% and 90.80% has been achieved upon multiple runs for the three datasets. Ablation experiments indicate that the addition of each block contributed to increasing 17%–25% accuracy scores during the classification of epileptic and non‐epileptic EEG signals. The real‐time EEG‐BCI has been analyzed using weight optimization of the proposed architecture through the NVIDIA Tensor RT framework on a 40 GB DGX A100 NVIDIA workstation. The proposed architecture has generalized well in comparison with the existing techniques for the three EEG datasets and achieved a low training and validation loss with optimum evaluation metrics. This makes the proposed architecture suitable for future EEG‐BCI system deployment in the automatic diagnosis of epilepsy.

Harnessing artificial intelligence for enhanced Parkinson’s disease management: Pathways, treatment, and prospects

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the accumulation of misfolded proteins and impaired protein degradation mechanisms. Dysregulation of protein degradation processes, including autophagy and the ubiquitin-proteasome system, has been implicated in the pathogenesis of PD. Recently, artificial intelligence (AI) has emerged as a powerful tool to enhance our understanding of protein degradation in PD. This abstract provides an overview of the advancements in studying protein degradation in PD with the aid of AI. The integration of AI techniques, such as machine learning and data mining, has enabled the identification and characterization of protein degradation pathways involved in PD. By analyzing large-scale protein-protein interaction networks, AI algorithms have revealed key interactions and pathways underlying protein degradation dysfunction in PD. Furthermore, AI models can predict the efficiency of protein degradation processes and identify potential targets for enhancing protein degradation in PD, aiding in the development of novel therapeutic interventions.AI-based approaches have also been instrumental in drug discovery and target identification, as they can screen vast databases of compounds to identify potential drugs or small molecules that modulate protein degradation pathways relevant to PD. Additionally, deep learning algorithms have facilitated the analysis of protein structures, predicting protein stability and folding patterns that impact protein degradation. Moreover, AI has played a crucial role in the identification of protein biomarkers associated with protein degradation dysfunction in PD. These biomarkers can aid in early diagnosis and monitoring of the disease, enabling timely intervention and personalized treatment strategies. The advancements presented in this abstract highlight the transformative potential of AI in elucidating the intricate mechanisms of protein degradation in PD. Collaborations between AI researchers, biologists, and clinicians are essential to translate these findings into effective diagnostic tools and therapeutic interventions for PD patients.

AI-driven software engineering

The intersection of artificial intelligence (AI) and software engineering marks a transformative phase in the technology industry. This paper delves into AI-driven software engineering, exploring its methodologies, implications, challenges, and benefits. Drawing from data sources such as GitHub and Bitbucket and insights from industry experts, the study offers a comprehensive view of the current landscape. While the results indicate a promising uptrend in the integration of AI techniques in software development, challenges like model interpretability, ethical concerns, and integration complexities emerge as significant. Nevertheless, the transformative potential of AI within software engineering is profound, ushering in new paradigms of efficiency, innovation, and user experience. The study concludes by emphasizing the need for further research, better tooling, ethical guidelines, and education to fully harness the potential of AI-driven software engineering.

Retina Oculomics in Neurodegenerative Disease.

Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocular imaging biomarkers to provide insights into systemic diseases. Advances in diagnostic and imaging technologies including electroretinography, optical coherence tomography (OCT), confocal scanning laser ophthalmoscopy, fluorescence lifetime imaging ophthalmoscopy, and OCT angiography have revolutionized the ability to understand systemic diseases and even detect them earlier than clinical manifestations for earlier intervention. With the advent of increasingly large ophthalmic imaging datasets, machine learning models can be integrated into these ocular imaging biomarkers to provide further insights and prognostic predictions of neurodegenerative disease. In this manuscript, we review the use of ophthalmic imaging to provide insights into neurodegenerative diseases including Alzheimer Disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, and Huntington Disease. We discuss recent advances in ophthalmic technology including eye-tracking technology and integration of artificial intelligence techniques to further provide insights into these neurodegenerative diseases. Ultimately, oculomics opens the opportunity to detect and monitor systemic diseases at a higher acuity. Thus, earlier detection of systemic diseases may allow for timely intervention for improving the quality of life in patients with neurodegenerative disease.

Analysis of Intelligent Decision Support Systems and a Multi Criteria Framework for Assessment

The act of decision-making lies at the core of human existence and shapes our interactions with the surrounding environment. This article investigates the utilization of artificial intelligence (AI) techniques in the advancement of intelligent decision support systems (IDSS). It builds upon prior research conducted in the decision-making field and the subsequent development of decision support systems (DSS) based on that knowledge. The initial establishment of the fundamental principles of classical DSS is undertaken. The subsequent emphasis is directed towards the integration of artificial intelligence techniques within IDSS. The evaluation of an IDSS, as well as any other DSS, is a crucial undertaking in order to gain insights into the system's capabilities and identify areas that require enhancement. This article presents a review conducted on this significant yet insufficiently investigated subject matter. When utilized in conjunction with DSS, AI techniques such as intelligent agents, artificial neural networks (ANN), evolutionary computing, case-based reasoning, and fuzzy logic provide valuable assistance in defining complex practical challenges, which are mostly time-critical, encompass extensive and scattered data, and can derive advantages from sophisticated reasoning.

Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review.

The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.

Evolutionary artificial neural network for temperature control in a batch polymerization reactor

The integration of artificial intelligence techniques introduces fresh perspectives in the implementation of these methods. This paper presents the combination of neural networks and evolutionary strategies to create what is known as evolutionary artificial neural networks (EANNs). In the process, the excitation function of neurons was modified to allow asexual reproduction. As a result, neurons evolved and developed significantly. The technique of a batch polymerization reactor temperature controller to produce polymethylmethacrylate (PMMA) by free radicals was compared with two different controls, such as PID and GMC, demonstrating that artificial intelligence-based controllers can be applied. These controllers provide better results than conventional controllers without creating transfer functions to the control process represented.

Applications of Federated Learning in Mobile Health: Scoping Review.

The proliferation of mobile health (mHealth) applications is partly driven by the advancements in sensing and communication technologies, as well as the integration of artificial intelligence techniques. Data collected from mHealth applications, for example, on sensor devices carried by patients, can be mined and analyzed using artificial intelligence-based solutions to facilitate remote and (near) real-time decision-making in health care settings. However, such data often sit in data silos, and patients are often concerned about the privacy implications of sharing their raw data. Federated learning (FL) is a potential solution, as it allows multiple data owners to collaboratively train a machine learning model without requiring access to each other's raw data. The goal of this scoping review is to gain an understanding of FL and its potential in dealing with sensitive and heterogeneous data in mHealth applications. Through this review, various stakeholders, such as health care providers, practitioners, and policy makers, can gain insight into the limitations and challenges associated with using FL in mHealth and make informed decisions when considering implementing FL-based solutions. We conducted a scoping review following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched 7 commonly used databases. The included studies were analyzed and summarized to identify the possible real-world applications and associated challenges of using FL in mHealth settings. A total of 1095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were included in the review. The analysis of these articles revealed 2 main application areas for FL in mHealth, that is, remote monitoring and diagnostic and treatment support. More specifically, FL was found to be commonly used for monitoring self-care ability, health status, and disease progression, as well as in diagnosis and treatment support of diseases. The review also identified several challenges (eg, expensive communication, statistical heterogeneity, and system heterogeneity) and potential solutions (eg, compression schemes, model personalization, and active sampling). This scoping review has highlighted the potential of FL as a privacy-preserving approach in mHealth applications and identified the technical limitations associated with its use. The challenges and opportunities outlined in this review can inform the research agenda for future studies in this field, to overcome these limitations and further advance the use of FL in mHealth.

Integration of Multiple Bayesian Optimized Machine Learning Techniques and Conventional Well Logs for Accurate Prediction of Porosity in Carbonate Reservoirs

The accurate estimation of reservoir porosity plays a vital role in estimating the amount of hydrocarbon reserves and evaluating the economic potential of a reservoir. It also aids decision making during the exploration and development phases of oil and gas fields. This study evaluates the integration of artificial intelligence techniques, conventional well logs, and core analysis for the accurate prediction of porosity in carbonate reservoirs. In general, carbonate reservoirs are characterized by their complex pore systems, with the wide spatial variation and highly nonlinear nature of their petrophysical properties. Therefore, they require detailed well-log interpretations to accurately estimate their properties, making them good candidates for the application of machine learning techniques. Accordingly, a large database of (2100) well-log records and core-porosity measurements were integrated with four state-of-the-art machine learning techniques (multilayer perceptron artificial neural network, MLP-ANN; Gaussian process regression, GPR; least squares gradient boosting ensemble, LS-Boost; and radial basis function neural network, RBF-NN) for the prediction of reservoir porosity. The well-log data used in this study include sonic acoustic travel time, Gamma-ray, and bulk density log records, which were carefully collected from five wells in a carbonate reservoir. This study revealed that all the artificial intelligence models achieved high accuracy, with R-squared values exceeding 90% during both the training and blind-testing phases. Among the AI models examined, the GPR model outperformed the others in terms of the R-squared values, root-mean-square error (RMSE), and coefficient of variation of the root-mean-square error (CVRMSE). Furthermore, this study introduces an artificially intelligent AI-based correlation for the estimation of reservoir porosity from well-log data; this correlation was developed using an in-house, Fortran-coded MLP-ANN model presented herein. This AI-based correlation gave a promising level of accuracy, with R-squared values of 92% and 90% for the training and blind-testing datasets, respectively. This correlation can serve as an accurate and easy-to-use tool for porosity prediction without any prior experience in utilizing or implementing machine learning models.

Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

The integration of Artificial Intelligence techniques into Decision Support Systems yields effective solutions to decision problems, especially when complex scenarios are at hand. However, the use of intelligent black-box models can hinder the decision support system’s potential to be fully adopted because opaque processes raise suspicions and doubts among careful decision makers. Moreover, appropriate and comprehensible explanations may foster trustworthiness and allow for reasonable adjustments or even corrections. This work proposed an approach that incorporates three reasonability aspects into Decision Systems: feasibility, rationality, and plausibility. Thus, by providing decision makers with reasonable candidate solutions for a complex problem, they are expected to perform their tasks more effectively (i.e. decide with more efficiency as well as efficacy). The new approach is accompanied by two proofs of concept in the health and public security areas. Comparative results using random and rational approaches, including the simulation of distinct user profiles, are presented. The proposed approach achieved superior metrics with regard to feasibility and plausibility, suggesting that this proposition can be applied to real-world applications.

A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

Integration of Artificial Intelligence Techniques in Mechatronic Systems for Smart Manufacturing

The integration of artificial intelligence (AI) techniques in mechatronic systems has emerged as a significant research area in the field of smart manufacturing. This paper presents a comprehensive overview of the various AI techniques that are being employed to enhance the capabilities and performance of mechatronic systems in the context of smart manufacturing. The aim is to explore how AI techniques can be effectively integrated into mechatronic systems to optimize production processes, improve product quality, and enable adaptive and intelligent manufacturing systems.The paper begins by discussing the fundamental concepts of mechatronic systems and their importance in smart manufacturing. Mechatronic systems combine mechanical, electrical, and computer engineering disciplines to design and develop intelligent systems with enhanced functionality and performance. The integration of AI techniques in mechatronics brings together the power of AI algorithms and control systems to create intelligent and autonomous systems capable of making real-time decisions and adaptations. The integration of AI techniques in mechatronic systems holds great potential for revolutionizing smart manufacturing. By leveraging machine learning, deep learning, expert systems, fuzzy logic, and genetic algorithms, mechatronic systems can achieve higher levels of intelligence, adaptability, and autonomy, leading to improved efficiency, productivity, and product quality in manufacturing processes. However, addressing the associated challenges and focusing on future research directions will be crucial in realizing the full potential of AI integration in mechatronic systems for smart manufacturing.

ATSS Deep Learning-Based Approach to Detect Apple Fruits

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. Specifically, in fruit detection problems, several recent works were developed using Deep Learning (DL) methods applied in images acquired in different acquisition levels. However, the increasing use of anti-hail plastic net cover in commercial orchards highlights the importance of terrestrial remote sensing systems. Apples are one of the most highly-challenging fruits to be detected in images, mainly because of the target occlusion problem occurrence. Additionally, the introduction of high-density apple tree orchards makes the identification of single fruits a real challenge. To support farmers to detect apple fruits efficiently, this paper presents an approach based on the Adaptive Training Sample Selection (ATSS) deep learning method applied to close-range and low-cost terrestrial RGB images. The correct identification supports apple production forecasting and gives local producers a better idea of forthcoming management practices. The main advantage of the ATSS method is that only the center point of the objects is labeled, which is much more practicable and realistic than bounding-box annotations in heavily dense fruit orchards. Additionally, we evaluated other object detection methods such as RetinaNet, Libra Regions with Convolutional Neural Network (R-CNN), Cascade R-CNN, Faster R-CNN, Feature Selective Anchor-Free (FSAF), and High-Resolution Network (HRNet). The study area is a highly-dense apple orchard consisting of Fuji Suprema apple fruits (Malus domestica Borkh) located in a smallholder farm in the state of Santa Catarina (southern Brazil). A total of 398 terrestrial images were taken nearly perpendicularly in front of the trees by a professional camera, assuring both a good vertical coverage of the apple trees in terms of heights and overlapping between picture frames. After, the high-resolution RGB images were divided into several patches for helping the detection of small and/or occluded apples. A total of 3119, 840, and 2010 patches were used for training, validation, and testing, respectively. Moreover, the proposed method’s generalization capability was assessed by applying simulated image corruptions to the test set images with different severity levels, including noise, blurs, weather, and digital processing. Experiments were also conducted by varying the bounding box size (80, 100, 120, 140, 160, and 180 pixels) in the image original for the proposed approach. Our results showed that the ATSS-based method slightly outperformed all other deep learning methods, between 2.4% and 0.3%. Also, we verified that the best result was obtained with a bounding box size of 160 × 160 pixels. The proposed method was robust regarding most of the corruption, except for snow, frost, and fog weather conditions. Finally, a benchmark of the reported dataset is also generated and publicly available.

An Improved Segmentation and Classifier Approach Based on HMM for Brain Cancer Detection

Introduction: Brain tumors are fatal diseases that are spread worldwide and affect all types of age groups. Due to its direct impact on the central nervous system, if tumor cells prevail at certain locations in the brain, the overall functionality of the body is disturbed and chances of a person approaching death are high. Tumors can be cancerous or non-cancerous but in many cases, the chances of complete recovery are less and as a result death rate has increased all over the world despite recent advancements in technology, equipment and awareness. So the main concern is to detect brain related diseases at early stages so that they do not spread into vital parts of brain and disrupt body functions. Also, more precise and accurate technologies are required to serve as aid in the diagnosis, treatment and surgery of brain. Aims & Objectives: Therefore, its high alarming time to monitor mortality statistics and develop faster and accurate methods to curb the situation by simulating tissue deformation and locating cancerous nodes which is currently the prominent area of interest. Methods: A brain tumor is used to design the deformation model. Early stage detection of tumors is difficult from images. Moreover, the accuracy involved is low. Keeping all this into consideration, a machine learning approach has been developed for classification of cancerous and non-cancerous tissues so that the tissues having risk of future problem can also be recognized. The patient’s deformation model can be designed and brain tumor patterns are given as input on the basis of which tumor in the brain is marked. The proposed method of segmentation is based on a statistical model called Hidden Markov Model (HMM) which extricates the cancerous portion out of fed input MRI image along with the calculation of parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), fault rate dust detection and accuracy. Results &Discussion: The results obtained from parametric analysis show that HMM has performed better than the technique of Support Vector Regression (SVR) for brain cancer segmentation in terms of PSNR, MSE, fault rate dust detection and accuracy. So image processing is used in combination with Hidden Markov Model for classification and analysis to which MRI images are fed as input. Conclusion: In this way, integration of artificial intelligence techniques with image processing can serve as a good way for segmentation of tumors and for classification purposes with good accuracy.

New Rules Generation From Measurement Data Using an Expert System in a Power Station

The integration of artificial-intelligence techniques in traditional real-time systems is a promising approach to cope with the growing complexity of real-world applications. Real-time expert systems are online knowledge-based systems that combine analytical process models with conventional process control to monitor complex industrial processes and to assist in problem identification. The expert system interfaces with the external distributed control system (DCS) via an object linking and embedding for process control module which acquires measurement data and identifies processes alarms for diagnosis. This paper proposes generating new rules from the plant measurement data using a learning engine. We present an efficient algorithm that generates all significant rules based on the data. The association-based algorithms were compared and those best suited for this process application were selected. The application for the learning system is studied in a powerplant application This innovative approach should assist in sustainable growth of automation in the power sector.