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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI

Mathematical Biosciences and engineering 2024年第12期21卷 7830-7853页

作者： Nisha, M. Kannan, T. Sivasankari, K. Department of Computer Science and Engineering Akshaya College of Engineering and Technology Tamil Nadu Coimbatore India Department of Mechanical Engineering Amrita College of Engineering and Technology Tamil Nadu Nagercoil India Department of Electronics and Engineering Akshaya College of Engineering and Technology Tamil Nadu Coimbatore India

The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain’s medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates. This segmentation technique is significantly faster than the manual segmentation methods used in clinics. Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. Thus, the proposed segmentation techniques outperform the existing techniques and produce the desired result so that an accurate diagnosis is made at the earliest stage to save human lives and to increase their life span. © 2024 the Author(s),

关键词： Convolutional neural networks

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Accelerating computer Architecture Development using Formal Methods 15

Accelerating Computer Architecture Development using Formal ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Vijay Kumar Gowrishankar, J. Kashyap, Sneha Vivekananda Global University Department of Mechanical Engineering Jaipur India Department of Computer Science Engineering Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

Formal strategies are getting more and more crucial within the design, verification, and validation of computer architectures and future hardware systems. Formal strategies provide a scientific approach to specification, verification, and validation of PC architectures, which allow engineers to carefully prove residences of laptop architectures that do not rely upon their specific implementation. Via using formal strategies, designers can rapidly and effectively increase computer architectures;by lowering the want for manual debugging, designers can reduce time to market and growth performance. This paper proposes a formal strategies-based approach for the development of PC architectures and future hardware systems. The technique gives a unified framework for the automated verification of laptop architectures that mixes formal good judgment, hardware synthesis, and verification. It is finished by the usage of summary models of the designs to offer an executable form of the design that's used for verification and validation. The method is then carried out in detail to the Intel IA-sixty-four architecture, an example of computer architecture. The formal models are used to derive hardware from the layout automatically and to check that the generated hardware satisfies the specs. The outcomes of the paintings suggest that formal techniques may be used to boost the improvement of computer architectures with improved accuracy and performance. Furthermore, the automation of the verification process makes it quicker and simpler to develop computer architectures, allowing engineers. © 2024 IEEE.

关键词： Architectures computer Design Hardware Systems Judgment Performance

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Blockchain-Powered Student Feedback for Enhanced Learning Experiences

Blockchain-Powered Student Feedback for Enhanced Learning Ex...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Naik, Girish R. Naik, Poornima G. KIT's College of Engineering Department of Mechanical Engineering Kolhapur India Csiber School of Computer Science and Applications Kolhapur India

ISBN: (纸本)9798350319019

In the contemporary landscape of education, the invaluable input provided by students through feedback is widely acknowledged as pivotal in elevating the quality of teaching and learning experiences. Yet, conventional feedback systems encounter notable constraints, notably in the realms of data security and transparency. Current research is dedicated to exploring the integration of blockchain technology as a robust and secure framework for collecting student feedback. Blockchain's distinct features, including its assurance of data integrity, transparency mechanisms, and the utilization of smart contracts, hold the promise of significantly refining the feedback process. This research meticulously outlines the academic significance of student feedback systems, defines essential components for effective feedback mechanisms, and elucidates the role of blockchain in constructing such a system. Moreover, it presents a conceptual model design, details the system's architecture, and elucidates the functionalities of smart contracts developed using Solidity. By leveraging blockchain, this innovative approach ensures the security, transparency, and streamlined efficiency of the feedback process, proclaiming unprecedented potential for transformative advancements in educational methodologies. © 2024 IEEE.

关键词： Smart contract

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A fuzzy TOPSIS method for surface integrity criteria ranking using the wire electrical discharge machining process

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Journal of engineering and Applied science 2023年第1期70卷 120页

作者： Sawyerr, Babatunde Alade Fasina, Ebun Adedeji, Wasiu Oyediran Adeniran, Mofoluso Kehinde Oke, Sunday Ayoola Rajan, John Department of Computer Science University of Lagos Lagos Nigeria Department of Mechanical Engineering Osun State University Osogbo Nigeria Department of Mechanical Engineering University of Lagos Lagos Nigeria Department of Manufacturing Engineering Vellore Institute of Technology Chennai India

The objective of this study is to propose a method called the fuzzy technique for order preference by similarity to the ideal solution (F-TOPSIS) to select parameters of the wire electrical discharge machining (WEDM) process. Consequently, the parameters principally influencing the outputs of the WEDM process were identified and examined using the F-TOPSIS framework where the inputs of three decision makers, representing their opinions are incorporated into the analysis. The idea of parametric selection in a WEDM process is multicriteria-based when the production of nitinol-60 smart memory alloy (SMA) is critically considered. Current approaches of TOPSIS and analytic hierarchy process (AHP) evaluations to select the WEDM parameters fail because of the constraints of linguistic evaluations, but the use of F-TOPSIS crosses over the restriction to choose the best parameters in a WEDM process for nitinol-60 SMA. In this work, the experimental results obtained from published research were utilised to validate the proposed method. The validation of the suitability of F-TOPSIS, aided by the published work on the WEDM process, analysed the surface integrity of nitinol-60 SMA. From the five outputs, the closeness coefficients of the best and worst are found to be 0.7567 and 0.2838, respectively. This research aids the process engineer in optimising the outputs in the WEDM process, to select the best output. Hence, the research showcased how the WEDM process could select outputs efficiently, thus aiding process engineers to maintain the process to optimise parametric resource allocations and guarantee utmost productivity. © 2023, Faculty of engineering, Cairo University.

关键词： Analytic hierarchy process

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Unleashing the Potential of IoT Integration for Energy Optimization in Smart Homes 3

Unleashing the Potential of IoT Integration for Energy Optim...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Tiwari, Shivam Bhushan, Aditya Singh, Ashutosh Kumar Yadav, Rakesh Kumar United College of Engineering & Research Department of Mechanical Engineering Prayagraj India United College of Engineering & Research Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350309409

This research paper explores the transformative potential of integrating Internet of Things (IoT) devices into smart home technologies, ushering in a new era of energy-efficient living. Focusing on energy optimization, the paper examines the intricate network of interconnected devices shaping efficient residential ecosystems. Beginning with an investigates specific devices such as smart thermostats and intelligent lighting systems. Through meticulous analysis of user behavior, it unveils how these devices influence and mold household energy consumption. Spotlighting data analytics, the paper reveals the power of information processing for personalized, efficient energy management. Addressing challenges like privacy and interoperability, it proposes innovative solutions for seamless IoT integration. The research concludes with an environmental assessment, emphasizing the symbiotic relationship between IoT and smart homes in fostering a sustainable and eco-friendly future. © 2024 IEEE.

关键词： Internet of things

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Machine Learning for Predictive Modeling and Personalized Treatment in Magnesium-based Biomedical Applications 8

Machine Learning for Predictive Modeling and Personalized Tr...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Jnyanadeep, Bandaru Sahana, S. Suresh, Rohit Roopa, T.S. Angadi, Gangadhar Bengaluru India Rv College of Engineering® Department of Computer Science and Engineering Bengaluru India Rv College of Engineering® Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9798331505462

This paper explores the integration of machine learning (ML) techniques with magnesium-based biomedical applications, focusing on predictive modeling and personalized treatment strategies. Magnesium's biocompatibility and biodegradability make it an attractive material for various medical applications, including implants and drug delivery systems. However, optimizing its use for individual patients remains challenging. In this paper it is proposed that ML can bridge this gap by enabling more accurate predictions of treatment outcomes and facilitating personalized approaches. Through a comprehensive review of existing literature and analysis of case studies, it is demonstrated that how supervised learning, unsupervised learning, and deep learning techniques can be applied to improve patient outcomes in magnesium-based treatments. The paper also addresses challenges such as data privacy and model interpretability, proposing solutions for integrating ML into clinical workflows. Our findings suggest that the synergy between ML and magnesium-based biomedical applications has the potential to significantly advance personalized medicine, opening new avenues for research and clinical practice. © 2024 IEEE.

关键词： Self-supervised learning

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Evaluation Of Deviations Due To Robot Configuration For Robot-based Incremental Sheet Metal Forming 23

Evaluation Of Deviations Due To Robot Configuration For Robo...

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6th International Conference on Advances in Robotics, AIR 2023

作者： Paul, Eldho Bharti, Sahil Uthama, Anandu Boby, Riby Abraham Krishnaswamy, Hariharan Klimchik, Alexandr Mechanical Engineering Iit Madras India Mechanical Engineering Iitm India School of Computer Science University of Linkcoln United Kingdom

ISBN: (纸本)9781450399807

Industrial robot-based Incremental Sheet metal Forming (ISF) is known as Roboforming. Industrial robots are being adopted for forming operations because they allow higher tool flexibility in terms of positioning and orientating the tool and a larger workspace at a minimum cost compared to CNC-based ISF. However, the lower stiffness of the robots leads to undesirable geometrical anomalies and deviations in the formed part. Along with the externally applied forces, robot configuration changes also impact the tool's positional accuracy. An attempt has been made to study the influence of robot configurations on overall part deviations in Roboforming due to robot compliance. Understanding changes in compliance or stiffness changes owing to different robot poses is a necessary step for optimizing the transition from a traditional CNC-based ISF to a robot-assisted ISF. Robot stiffness-based deviations are evaluated using an analytical approach, and configuration-based deflections are studied using FEM-based approaches for their contribution to the overall part deviations. Results are compared to the sheet metal forming experiments conducted over conventional 3-axis CNC for forming a cone-shaped profile using a spiral toolpath. It is highlighted that the overall deviations in the formed components are influenced by robot compliance and pose. © 2023 ACM.

关键词： Stiffness

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An Improved Deep Learning Framework Approach for Detecting the Psoriasis Disease

An Improved Deep Learning Framework Approach for Detecting t...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Singh, Anuj Kumar, K.C. Nithin Kumar, M. Anand Negi, Harendra Singh Department of Computer Science Dehradun India Department of Mechanical Engineering Dehradun India

ISBN: (纸本)9798350334357

Normal skin is necessary for a healthy lifestyle since it is the most crucial component of the human body. However, it has several dermatological complications. One of these skin disorders is psoriasis. The research proposes a unique deep learning framework that assesses three separate scenarios, namely Visual Geometry Group-16, Visual Geometry Group-19, and Inception v3, and examines some cutting-edge deep learning algorithms using several measures, including 'AUC,' CA, Precision, F1, and Recall. Based on the results, Visual Geometry Group-19 is chosen as the feature extractor and Logistic Regression is chosen as the classifier. The 'AUC' value is 0.990, and the CA is 94.2%. © 2023 IEEE.

关键词： Geometry

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A Study on the Mental Health Among Indian Population in the Post COVID-19 Pandemic Using Computational Intelligence 1

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Kumari, Nancy Acharjya, D.P. Ma, Yan School of Computer Science and Engineering VIT Bhopal University Bhopal India School of Computer Science and Engineering VIT Vellore India College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

Preventing mental health problems is an essential task for national and international organizations. Such entails the establishment of active information network indicators to assess the mental health of populations. About 21% of the global people suffering from depression, anxiety, isolation, loss of income, and fear are triggering mental health problems due to the COVID-19 pandemic. Therefore, it is essential to find out various factors that affect the mental health of people in India the post COVID-19 pandemic. A hybridized rough and whale optimization technique is used to study the mental health of people in the post pandemic of India. The proposed design contains three stages of implementation. In the very beginning, the data collected from primary sources are validated using structural equation modeling and partial least square. Further, in the second phase, the proposed rough whale optimization is employed to identify the chief parameters of the decision system. The reduced decision system is analyzed further using a rough set in the final phase. It is observed that the evaluations have drawn attention to the importance of mental disorders for public health. It, in turn, helps to reduce the risk of getting a mental disorder. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Prediction of Air Pollution Using Advanced Machine Learning Techniques

Prediction of Air Pollution Using Advanced Machine Learning ...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Bhaumik, Rabin Bardhan, Shawli Roga, Sukanta Debbarma, Swapan National Institute of Technology Agartala Department of Computer Science and Engineering Agartala India Techno College of Engineering Agartala Department of Computer Science and Engineering Agartala India Visvesvaraya National Institute of Technology Department of Mechanical Engineering Nagpur India

ISBN: (纸本)9798331540821

Air pollution presents significant health and ecological dangers, especially in densely populated urban regions. This study looks at the current models that are used to guess the Air Quality Index (AQI). It does this by using datasets of important pollutants like PM2.5, PM10, SO2, NO2, CO, and O3, all of which are linked to breathing and heart problems. We assess the effectiveness of various forecasting algorithms on air pollution data, highlighting that the LightGBM model consistently demonstrates superior performance across multiple metrics. Furthermore, this research underscores the need for integrated approaches to tackle air pollution. While it advocates for targeted measures to safeguard vulnerable groups, enhance public health, and promote sustainable urban growth, this study provides actionable insights aimed at informing the scientific community and supporting future research in the development of robust air quality management strategies. © 2024 IEEE.

关键词： Air pollution detection environmental pollutants time series

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