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IEEE Conference on Decision and Control

作者： Mohammad Al Saaideh Yazan M. Al-Rawashdeh Natheer Alatawneh Khaled Aljanaideh Almuatazbellah boker Mohammad Al Janaideh Department of Electrical and Computer Engineering Memorial University St. John’s NL Canada Electrical Engineering Department at Al-Zaytoonah University of Jordan 130 Amman Jordan Department of Mechanical and Mechatronics Engineering Memorial University St. John’s NL A1B 3X5 Canada Department of Aeronautical Engineering Faculty of Engineering Jordan University of Science and Technology Irbid Jordan Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA School of Engineering University of Guelph Guelph Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Reluctance actuators (RA) can replace the current Lorentz actuators in the next generation of positioning and scanning motion systems, such as the wafer stage in lithography machines. However, the nonlinear output force characteristic and the gap dependency of the RA are the main challenges in using the RA to drive motion systems. In this paper, we design a two-loop control approach for a reluctance actuator motion system (RAMS) to achieve tracking performance for a desired motion profile. First, the current control loop linearizes the RA under different conditions. Next, the position control loop is designed using a PID control based on an extended-high gain observer to achieve a desired motion profile considering unknown dynamics in the system. The simulation results show the efficiency of the proposed current control in linearizing the dynamic behavior under different desired forces and nominal air gaps and achieving a frequency response similar to the spring-mass-damper system. Moreover, the position control can achieve different long-stroke and short-stroke motion profiles with different amplitudes and ranges of motion.

关键词： Current control Actuators Tracking loops Tracking Dynamics Air gaps Force Position control Random access memory Frequency response

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brain Tumor Detection Using MRI Image Processing techniques

Brain Tumor Detection Using MRI Image Processing Techniques

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Innovation and Novelty in engineering and technology (INNOVA), International Conference on

作者： Ambaji. S. Jadhav Rashmi Pawar Sunilkumar. M. Hattaraki Shwetha bagali Rajeshwari S. Patil bhagyashree Patil Department of Elecrtical and Electronics Engineering B.L.D.E. As.V.P. Dr. P.G.H. College of Engg. and Tech. Vijayaapura India Department of Electronics and Communication Engineering B.L.D.E. As. V.P. Dr. P.G.H. College of Engg. and Tech. Vijayaapura India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bengaluru India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. brain tumors, which disrupt normal brain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose brain tumors accurately and quickly using MRI images. Image pre-processing is done using CLAHE and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MSD-CNN classifies the tumor, capturing both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection.

关键词： Image segmentation technological innovation Accuracy Thresholding (Imaging) Magnetic resonance imaging Noise brain tumors Training data X-rays Robustness

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Challenges in Cyber Physical Social Systems and Internet of Things

Challenges in Cyber Physical Social Systems and Internet of ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Revathi J. Raja M Mohanraj K. Malathi balasubbareddy Mallala R.G. Vidhya Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Eshwar college of engineering Coimbatore India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of ECE HKBK College of Engineering Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research analyses the complex dynamics of Cyber-Physical-Social Systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. by exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems.

关键词： Data privacy Social networking (online) Shape Media Real-time systems Internet of Things Protection Predictive analytics Monitoring Resilience

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Forecasting the Remaining Useful Life of Proton Exchange Membrane Fuel Cells by Utilizing Nonlinear Autoregressive Exogenous Networks Enhanced by Genetic Algorithms

SSRN

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SSRN 2023年

作者： Shen, Yitong Alzayed, Mohamad Chaoui, Hicham the Department of Electronics Carleton University OttawaONK1S 5B6 Canada The Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States

The Proton Exchange Membrane Fuel Cell (PEMFC), known for its efficient energy conversion, minimal electrolyte leakage, and low operating temperature, shows great potential as a clean energy source. However, its lifespan is limited due to degradation during normal operation, which, if uncontrolled, can result in dangerous failures such as explosions. Hence, accurately estimating the remaining useful life (RUL) is vital. In this research, a combined prediction method using genetic algorithms (GA) and nonlinear autoregressive neural networks (NARX) with external inputs is proposed. The method's performance was trained and validated using the 2014 IEEE PHM Data Challenge dataset, and it was compared to two commonly used artificial neural network algorithms: GA-based backpropagation neural network (GA-bPNN) and GA-based time delay neural network (GA-TDNN). The findings demonstrate that the proposed approach surpasses the other two artificial neural network algorithms in terms of prediction accuracy. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Jellyfish Search Optimizer with Deep Learning Driven Cervical Cancer Recognition and Classification Model on biomedical Images

Jellyfish Search Optimizer with Deep Learning Driven Cervica...

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International Conference on Advances in Information technology (ICAIT)

作者： M. Suresh Anand S. Venkatramulu Sesha Vidhya S W T Chembian Yashaswini b M Magesh Kumar b Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur Department of CSE kakatiya Institute of Technology and science Warangal Department of ECE RMK College of Engineering and Technology Department of CSE Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Morai Avadi Electronics & Communication Engineering JSS Academy of Technical Education JSSATE-B Campus Bengaluru Karnataka Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The emergence of Cervical Cancer (CC) Recognition and Classification Model for biomedical Images is a main advancement in fields of medical imaging and diagnostics. Leveraging state-of-the-art image processing and machine learning algorithms, this model can accurately detect and classify CC-related abnormalities in medical images, namely colposcopy or Pap smear images. by providing automated and precise assessments, the model empowers medical experts to make timely and informed decisions, which leads to earlier interference and potential life-saving treatment. With its potential to improve diagnostic accuracy and reduce human error, the model holds great promise in the fight against CC, especially in resource-constrained healthcare settings where medical practitioners may be limited. This study presents a Jellyfish Search Optimizer with Deep Learning Driven CC Recognition and Classification (JSODL-CCRC) model on biomedical Images. The JSODL-CCRC technique is mainly developed to investigate the medical images for detection and classification of CC. The JSODL-CCRC approach comprises median filtering (MF) for noise reduction, followed by feature extraction utilizing Inceptionv3 to derive complex image patterns. besides, the JSO based hyperparameter selection process is performed for the Inception v3 model. At last, Extreme Learning Machine (ELM) algorithm is applied to categorize the images which validates efficacy in handling complex biomedical image data. The experimental results stated that the JSODL-CCRC model enables effective detection results over other models.

关键词： Deep learning Image recognition Jellyfish biological system modeling Noise reduction Medical services Feature extraction Medical diagnostic imaging Cervical cancer Tuning

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Energy Proficient and Dependable Cluster Routing in Wireless Sensor Network

Energy Proficient and Dependable Cluster Routing in Wireless...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Hemavathi Jeya Ganesan J G Geetha Kishore Kumar K R. Kohila Department of Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India Department of Computer Science Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Engineering (Cyber Security) Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed by an energy depletion observing system through the wireless sensor network (WSN). Conventional security approaches can face external attacks. Nevertheless, they cannot proficiently solve interior attacks triggered by the captured nodes. Energy-efficient and Dependable Cluster Routing (EPDC) in WSN. This approach aims to reduce energy expenditure and enhance reliable routing in WSNs. The highest energy and highest available bandwidth are selected as a cluster head (CH). In every cluster, the selected CH has received the observed information from the cluster member and then forwarded it to the bS through a relay node. The forwarder relay node is selected based on the average energy faith and transmission faith during data transmission. The whole faith is greater than the average value that node is selected as a data forwarder from cluster to bS in the WSN. Furthermore, the Path Optimization Algorithm (POA) is used to detect the obstacle and build the obstacle-aware route efficiently. The simulation results explain why the proposed method has better residual energy. In addition, it minimized both the network delay and the packet loss rate in the WSN.

关键词： Wireless sensor networks Simulation Packet loss bandwidth Routing Energy efficiency Delays

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IDOL: IoT based intrusion detection using Conv- biLSTM

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Australian Journal of Electrical and Electronics engineering 2025年

作者： C, Vijayalakshmi V, Padmajothi R, Suguna Devi Whitin, Priscilla S, Rajakumar Rajaretnam, Subraja Department of Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India

Internet of Things (IoT) networks have produced copious amounts of data that can be used to identify intrusions. Network Intrusion Detection Systems are crucial for spotting attacks before they cause any damage as part of a layered defence strategy. In this paper, a novel Intrusion Detection using Optimized deep Learning (IDOL) has been proposed to detect the intrusions like DDoS attacks, botnet attacks, Convergence attacks, and Ransomware attacks. Initially the IXIA PerfectStorm tool display data with malicious attack from the Network Environment and the data are converted in the data Pre-Processing method. Then the needed data are extracted in the feature extraction process by using boW technique. From the Extracted data the Feature selection process will select the essential data using Ant Colony Optimization. Finally, the Conv-biLSTM model will classify the attack into four classes namely DDoS attack, botnet attack, Convergence attack, and Ransomware attack. The proposed IDOL method achieves the highest accuracy rate of 98.93%. Using the UNSW-Nb15 and NSL-KDD datasets, the suggested model achieves an accuracy rate of 98.92% and 98.94% which is greater than the existing techniques such as ML-IMID, IDS-SLoEL, and POS-LightGRM respectively. © 2025 Engineers Australia.

关键词： Network intrusion

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Twitter sentiments analysis using machine learninig methods

Twitter sentiments analysis using machine learninig methods

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2020 International Conference for Emerging technology, INCET 2020

作者： Mandloi, Lokesh Patel, Ruchi Meidcaps University B.Tech Computer Science and Engineering Indore India Meidcaps University Dept. Computer Science and Engineering Indore India

ISBN: (纸本)9781728162218

Analysis of sentiments is the method of deciding whether the sentiments in the text is positive, negative or neutral. It is also known as material polarity or mining of opinions. The growth and advancement in social media platforms engaged a huge number of users. Social media platform like twitter where users can post their tweets in 280 characters. because of the limited number of characters in tweets, it becomes easy for the sentiment analysis. On Twitter 550 millions of tweets are posted daily. Twitter also represents all age group people and also a fair representation of gender. Therefore, the sentiment analysis of twitter data becomes somewhat general sentiments of society. In this paper, we will compare various Machine Learning methods like the Naïve bayes Classification method, Support Vector Machine Classification Method and Maximum Entropy Classification method. We will see how sentiments analysis is done by this classification algorithm and what is the accuracy and precision in these cases. © 2020 IEEE.

关键词： Sentiment analysis

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Design and Optimization of Heterogeneous Coded Distributed Computing with Nonuniform File Popularity

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IEEE Transactions on Mobile Computing 2025年

作者： Deng, Yong Dong, Min Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Toronto Edward S. Rogers Sr. Department of Electrical and Computer Engineering TorontoONM5S 3G4 Canada Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C5 Canada

This paper studies MapReduce-based heterogeneous coded distributed computing (CDC) where, besides different computing capabilities at workers, input files to be accessed by computing jobs have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of input files. Furthermore, we design a nested coded shuffling strategy that can efficiently manage the nonuniformity of file popularity to maximize the coded multicasting opportunity. We then formulate the joint optimization of the proposed file placement and nested shuffling design variables to optimize the proposed CDC scheme. To reduce the high computational complexity in solving the resulting mixed-integer linear programming (MILP) problem, we propose a simple two-file-group-based file placement approach to obtain an approximate solution. both numerical studies and experimental tests show that the optimized CDC scheme outperforms other alternatives. Also, the proposed two-file-group-based approach achieves nearly the same performance as the conventional branch-and-cut method in solving the MILP problem but with substantially lower computational complexity that is scalable over the number of files and workers. For computing jobs with aggregate target functions that commonly appear in machine learning applications, we propose a heterogeneous compressed CDC (C-CDC) scheme to further improve the shuffling efficiency. The C-CDC scheme uses a local data aggregation technique to compress the data to be shuffled for the shuffling load reduction. We again optimize the proposed C-CDC scheme and explore the two-file-group-based low-complexity approach for an approximate solution. Numerical studies show the proposed C-CDC scheme provides a considerable shuffling load reduction over the CDC scheme, and also, the two-file-group-based file placement approach maintains good performance. © 2002-2012 IEEE.

关键词： Cloud computing

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An IoT and Random Forest-DT-based Early Enhanced Forest Fire Detection in Large and Small Targets

An IoT and Random Forest-DT-Based Early Enhanced Forest Fire...

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Innovation in technology (INOCON), IEEE International Conference for

作者： Manjula Prabakaran Omesh Wadhwani V. Pandiaraj Kaneez Zainab Neerav Nishant Justin Z Department of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Information Technology Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science & Engineering B.N. College of Engineering and Technology Lucknow India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India Department of Science and Humanities (English) RMK College of Engineering and Technology Thiruvalluvar India

The world is confronted with more complex and severe environmental concerns than in the past. Extreme weather events have become more common and destructive in the last several decades, endangering forests and urban forests around the globe. Massive forest fires are among the most catastrophic natural catastrophes that affect Earth’s climate and the diversity of life on it. Thus, in order to minimize harmful impacts on ecosystems and people, it is vital to have well-planned and coordinated plans for early warning, prevention, and reaction. Image preprocessing, feature extraction, and training the model are the initial steps in the suggested methodology. Histogram matching and picture smoothing are part of the image preparation procedure. The gray histogram is a way to see how many pixels fall into each grayscale level, and image smoothing is a way to enhance a large region of a picture using image processing. The GLCM and LbP histograms are employed in the feature extraction process. Training RF-Multivariate ARS models requires feature extraction as a prerequisite. The system improved accuracy to 97.29 percent after applying the approach.

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