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Optimized Deep Neural Network for Accurate Detection of Malignant and Benign Brain Tumors 3

Optimized Deep Neural Network for Accurate Detection of Mali...

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3rd international conference on Artificial intelligence For Internet of Things, AIIoT 2024

作者： Visumathi, J. Kalaivani, N. Hemanth, S.V. Balasubramanian, K. Karthikeyan, S. Amirthayogam, G. Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai600062 India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Mathematics Tamilnadu Chennai600062 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India Kalasalingam Academy of Research and Education Deemed to be University Department of Computer Applications Tamil Nadu Krishnankoil626126 India Arunai Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvannamalai606603 India Hindustan Institute of Technology and Science Deemed To Be University Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

The rapid development of medical technology has ushered in a new age of crucial medical records. A brain tumor is an aberrant mass of rapidly expanding brain cells, some of which have the potential to metastasize. Brain tumors are often diagnosed in clinical practice using magnetic resonance imaging (MRI) scans. These tumors appear as clusters of irregular cells that could endanger patients by invading nearby tissues. However, manually spotting and segmenting brain tumors via MRI scans present complex challenges and are prone to mistakes. Thus, analysis of such data has influenced the prognosis, monitoring, diagnosis, and treatment of tumor-related conditions. To overcome these issues, we propose an Optimized Deep Neural Network (ODNN) method to accurately predict benign or malignant brain tumor diseases. Moreover, pre-processing uses an Adaptive Histogram Contrast Normalization (AHCN) technique to enhance the distribution and contrast of MRI image density. Afterward, features can be extracted using a Texture-Based Gray Level Co-Occurrence Matrix (TGLCM) method to separate the dark and bright parts of the image. Finally, a Machine Learning (ML) based ODNN method is employed to classify benign or malignant diseases. Utilizing sensitivity, specificity, recall, and accuracy metrics, the simulation results of the recommended ODNN approach are contrasted with those of preexisting approaches. © 2024 IEEE.

关键词： Deep neural networks

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Abnormal Scene Image Recognition Method for Intelligent Operation and Maintenance of Electrical Equipment in Substations 3

Abnormal Scene Image Recognition Method for Intelligent Oper...

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3rd international conference on Energy Engineering and Power Systems, EEPS 2023

作者： Yang, Youhang Wang, Shaorong Ren, Jiaxuan Zhang, Yanyan Huazhong University of Science and Technology School of Electrical and Electronic Engineering Wuhan China China Southern Power Grid Dongguan Power Supply Bureau Dongguan China

ISBN: (纸本)9798350313857

The utilization of robot inspections and accurate, rapid identification of fault targets in images through artificial intelligence technology represents an important research area in the field of intelligent operation and maintenance for substations. To address the issue of massive image data collected by robot inspections, an abnormal scene image recognition method is proposed for intelligent operation and maintenance of electrical equipment in substations, with consideration for both the accuracy and real-time performance of object detection. Firstly, the research status and development trend of intelligent operation and maintenance technology of substation electrical equipment is elaborated. Subsequently, a fault detection model for intelligent operation and maintenance of electrical equipment in substations is proposed based on YOLO v5 deep neural network. To improve the detection speed of the model, Cross Stage Partial (CSP) and Spatial Pyramid Pooling Fast (SPPF) structures are utilised to reduce model parameters. Finally, the effectiveness of the proposed model is validated through two application scenarios: substation foreign object suspension image recognition and thermal defect detection based on infrared images. The experimental results demonstrate that the model achieves a detection accuracy of 99.8% in foreign object suspension detection for electrical equipment, with an average frame rate of 149 frames/s. Moreover, the model has an accuracy of 98.9% for the detection of thermal defects in infrared images of electrical equipment, with an average frame rate of 123 frames/s. These results satisfy the application requirements for real-time monitoring. © 2023 IEEE.

关键词： Object detection

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Detection of Early Fault in Power Electronic Converters through Machine Learning and Data Mining Techniques 3

Detection of Early Fault in Power Electronic Converters thro...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Pradeep, K.V. Gowrishankar, K. Sivanantham, E. Rao, Katta Subba Kiran, Nagulapati Vimal, A. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Amet University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Anil Neerukonda Institute of Technology and Sciences Department of Electrical and Electronics Engineering Andhra Pradesh Visakhapatnam India Sri Eshwar College of Engineering Department of Mechanical Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665462167

The Power electronic system plays a significant role in versatile applications. The power electronic converters are largely used in energy conversion mechanisms. A fault is defined as the abnormal condition of the system that results in various consequences. The important constraints in the modelling of power electronic systems involve losses, Electromagnetic Interference (EMI) and harmonics. This includes the fault detection in the power electronic converters that includes three phase rectifier, d-dc converter and single-phase inverter. These parameter affects the overall efficiency and quality of the system. To overcome the fault in the power electronic converters, the machine learning with data mining techniques is adopted. This helps to predict the early fault and helps to increase the efficiency of the system. © 2023 IEEE.

关键词： Signal interference

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Institutional Modelling: A Case Study of the Swedish Organ Donation System 3

Institutional Modelling: A Case Study of the Swedish Organ D...

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3rd international conference on Hybrid Human-Artificial intelligence, HHAI 2024

作者： Fabris, Bertilla Belfrage, Michael Lorig, Fabian Department of Computer Science and Media Technology Malmö University Sweden Internet of Things and People Research Center Malmö University Sweden

ISBN: (纸本)9781643685229

Understanding the potential impact of policy changes before implementation is vital, and can be achieved through modelling and simulation. To adequately model stakeholders and regulative constraints, we propose the use of Institutional Grammar to facilitate institutional modelling in Agent-based Social Simulations. We present an early-stage case study exploring the Swedish organ donation system. © 2024 The Authors.

关键词： Agent-Based Social Simulation Model Formalisation Policy Support

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A Detailed Study Analysis of Artificial intelligence Implementation in Social Media applications 3

A Detailed Study Analysis of Artificial Intelligence Impleme...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Alawneh, Yousef J. Al-Momani, Taghreed Salman, Falsten N. Al-Ahmad, Suleiman D. Kaddumi, Thair A. Al-Dlalah, Mohamad Al-Zaytoonah University of Jordan Curriculum and Instruction in Mathematics Education Jordan Al-Quds Open University Israel Al-Zaytoonah University of Jordan Educational Administration Jordan Applied Science Private University Faculty of Business Department of Finance and Banking Sciences Amman Jordan Isra University Pakistan

ISBN: (纸本)9798350399264

Artificial intelligence (AI) refers to a field of computer science that focuses on developing intelligent machines capable of performing tasks that require human-level cognition, such as natural language processing and object recognition. This technology has the potential to address some of society's most challenging problems. Social media platforms have become ubiquitous in our daily lives, with platforms like Facebook, Twitter, Instagram, Pinterest, and YouTube facilitating connections and content sharing among users. To function properly, these platforms are increasingly reliant on AI technology. AI is transforming the way social media operates, and its use in this context is growing at an unprecedented rate. This paper aims to examine the various AI tools currently available and their impact on social media companies, paving the way for a deeper understanding of the role of AI in the social media landscape. © 2023 IEEE.

关键词： Object recognition

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Efficient Selection of Optimal Sewage Treatment Method for Industries Using Fuzzy TOPSIS and Fuzzy AHP 3

Efficient Selection of Optimal Sewage Treatment Method for I...

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3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Prajwal, N. Rohini, B.R. Kushal, B.S. Gargi, N. Kodipalli, Ashwini Soma, Bhavani Global Academy of Technology Dept. of Artificial Intelligence and Data Science Karnataka Bangalore India Global Academy of Technology Dept. Artificial Intelligence and Data Science Karnataka Bangalore India

ISBN: (纸本)9798350340358

The management of wastewater from industrial processes is a substantial challenge when striving for sustainable industrial practices. The process of determining the most suitable method for treating wastewater in an industrial context necessitates a systematic and comprehensive approach due to the multifaceted nature of this decision-making endeavor. This research addresses the crucial challenge of selecting optimal sewage treatment methods for industrial applications. Employing a novel approach that integrates Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) and Analytic Hierarchy Process (AHP), our methodology systematically prioritizes criteria such as energy consumption, efficiency, cost, process duration, environmental impact, and reuse *** study not only contributes to sustainable industrial practices but also provides a practical decision support tool, empowering industries to make informed choices in wastewater management which safeguards public health by eliminating pollutants, preventing waterborne diseases, and ensures the ecological balance. Moreover, it fosters environmental sustainability through the reuse of treated water, conserving resources, and mitigating pollution impact. © 2023 IEEE.

关键词： Energy utilization

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Sustainable Security Solutions for IoT: Enhancing Intrusion Detection Using AI and Machine Learning 3

Sustainable Security Solutions for IoT: Enhancing Intrusion ...

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3rd international conference on Sustainable Mobility applications, Renewables and technology

作者： Khan, Inaya Imtiyaz Kanaparthi, Yadidiah Ruchandani, Yash Rizwan, Aliya Canadian Univ Dubai Dept Comp Sci Dubai U Arab Emirates Manipal Univ Dubai Dept Comp Engn Dubai U Arab Emirates

ISBN: (纸本)9798331532932;9798331532925

The rapid expansion of the Internet of Things (IoT) has significantly increased the complexity and vulnerability of interconnected systems, posing a challenge to sustainable cybersecurity. With an emphasis on sustainable security practices, this article offers a thorough analysis of machine learning (ML) and artificial intelligence (AI) approaches for intrusion detection systems (IDS) in Internet of Things (IoT) contexts. We examine various AI-ML-based IDS approaches, emphasizing resource-efficient algorithms, energy-saving data processing techniques, and scalable solutions for real-time anomaly detection. Our review includes a comparison of methods based on criteria such as processing efficiency, energy consumption, and integration with existing sustainable practices in IoT ecosystems. The paper highlights the potential of AI-ML techniques to not only improve intrusion detection accuracy but also enhance the sustainability of IoT security through optimized computational resources and eco-friendly approaches. We identify current research gaps and suggest future directions for developing sustainable IDS solutions, including lightweight hybrid classifiers and privacy-preserving techniques that minimize data transfer.

关键词： intrusion detection systems artificial intelligence sustainability machine learning internet of things anomaly detection cyber security feature selection model training hybrid classifiers IoT security real-time detection computational complexity dataset analysis

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Forecast Model for Scheduling an HPC Application on CPU and GPU Architecture 3

Forecast Model for Scheduling an HPC Application on CPU and ...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Chandrashekhar, B.N. Mohan, M. Geetha, V. BMS Institute of Technology & Management Artificial Intelligence and Machine Learning Benguluru India Nitte Meenakshi Institute of Technology Information Science & Engineering Benguluru India Reva University School of Management Bengaluru India

ISBN: (纸本)9798350338607

Process scheduling is an essential part of multiprogramming operating systems. Scheduling is a process that allows one process to use the processing unit while the execution of another process is on hold (in a waiting state) due to the unavailability of any resource like I/O, thereby making full use of CPU or GPU. The major issue of scheduling is to make the system efficient, fast, and fair. This work focuses on developing a Forecast model and constructing scheduling strategies to schedule parallel applications on CPU and GPU. During the design of the Forecast model, we will consider the history of the actual execution time set of processes, then we compute the average time of individual sets of processes by considering parameters such as complete execution time, the sum of processes, and the number of threads assigned to individual processes. Then we will evaluate the Prediction time of CPU and GPU for individual sets of processes. By considering parameters such as the average time of the previous set of processes, the weight of processes, and the number of processes. Then based on the prediction time we will develop a scheduling strategy. As the minimum prediction time required set of process resources is assigned to the CPU and the GPU is assigned by the maximum predicted timed resource of the process. In this work we utilized the CPU and GPU resources effectively for stream benchmark application, our experiment shows that less than 20% average percentage prediction error in all cases. © 2023 IEEE.

关键词： Forecasting

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The Application of Machine Vision Algorithm in Obstacle Recognition and Navigation of Logistics robots 3

The Application of Machine Vision Algorithm in Obstacle Reco...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Liu, Lixun Zhou, Rongcheng Zhuhai College of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798350317022

In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitively query and obtain information. Due to the lack of a deep understanding of natural language in the hard coded matching rule method, the query results do not match the user's intentions and are difficult to meet practical application needs. Therefore, this article introduces machine vision algorithms for optimization and improvement. Through two sets of simulation experiments, the following conclusion can be drawn: compared with traditional methods, the comprehensive average improvement of obstacle accuracy index is about 11.5%, while the comprehensive average improvement of navigation efficiency index is 13.35%. This study injects innovative momentum into the logistics industry, enabling logistics robots to quickly and accurately perceive and navigate obstacles in complex environments, thereby enhancing their intelligence level in tasks such as warehousing and sorting, and making substantial contributions to improving logistics efficiency and reducing costs. © 2023 IEEE.

关键词： Logistics robot Machine Vision Algorithm Navigation and Positioning Obstacle Recognition

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Comprehensive Review of AI, IoT, and ML in Enhancing Urban Mobility and Reducing Carbon Footprints 3

Comprehensive Review of AI, IoT, and ML in Enhancing Urban M...

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3rd international conference on Sustainable Mobility applications, Renewables and technology

作者： Hossain, Mahmood Khalid, Hamad Rao, Avent Prakasa Lootah, Mohammad Al-Mohammedi, Salah Salim Khalaf Majeed, Salih Rashid Canadian Univ Dubai Sch Engn Appl Sci & Technol City Walk Dubai U Arab Emirates

ISBN: (纸本)9798331532932;9798331532925

The rise in vehicle ownership and urbanization, which exacerbates traffic congestion and carbon emissions, are significant barriers to sustainable urban development. This study investigates how artificial intelligence (AI), machine learning (ML), and the internet of things (IoT) could enhance urban transport systems, reduce traffic, and lower urban regions' carbon footprints. The study examines how urban infrastructures can be made more efficient and flexible through the use of technologies including demand-responsive transport systems, integrated mobility solutions, predictive maintenance, and personalized trip planning. A recent study of educators, students, and other participants at Canadian University Dubai (CUD) found that people are becoming increasingly accepting of AI-driven solutions that reduce fuel consumption and travel time. These technologies have promise even if there are still challenges to be solved, such as the requirement for ongoing validation in dynamic urban contexts and the reliance on historical data. The study concludes with recommendations for integrating AI, IoT, and ML to develop responsive urban mobility solutions and emphasizes the importance of practical implementations to realize these benefits.

关键词： Artificial intelligence (AI) Internet of Things (IoTs) Machine Learning (ML) Carbon Emission (CE) Urban Mobility (UM)

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