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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Raveena Selvanarayanan Surendran Rajendran Venkatesh Jayaraman P. J Sathish Kumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Mycosphaerella Coffeeicola (Black Spot Disease) is the most destructive disease in coffee leaves and affects global production. Diseased coffee leaves can adversely affect coffee quality, discolor, distort, or rot the coffee leaves, and impair the coffee beans’ cherry, taste, and aroma. The disease can also affect the quality of the coffee beans, leading to lower market value. The management of the Coffee Production Department of India controls the disease through a combination of cultural practices, chemical control, and the use of resistant coffee varieties. In the existing work, using single-layer perceptron was trained and validated with 100 epochs to obtain an accuracy of $\mathbf{9 5. 7 1 \%}$. In the piece above of work, an infected leaf is accurately diagnosed using deep learning-based models by employing the Deep-Siamese-based CNN Approach, recognized by a deep Siamese Neural network architecture which is also called a Twin Neural network with high illness recognition precision and simplified hyper-parameters to maximize description capacity while avoiding overfitting concerns, using a network with high sickness detection precision and minimal hyper-parameters. The prototype was tested in the lab on natural plants for known sickness instances, and the results matched the model validation datasets. When compared to several deep-learning models, the suggested structure attained an accuracy of $\mathbf{9 6. 8 9 \%}$.

关键词： Training Resistance Accuracy Convolution Neural networks Prototypes Production Turning Convolutional neural networks Diseases

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Random Load Balancing in Mini-NDN Using Modified Inherent Topology 9

Random Load Balancing in Mini-NDN Using Modified Inherent To...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Pratama, Cendika Sahid Arrachman, Muhammad Iqbal Nurrasyid, Muhammad Shafly Zaki Hamidi, Eki Ahmad Negara, Ridha Muldina Ahdan, Syaiful Mayasari, Ratna Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Uin Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

Named Data Networking (NDN) is a newly developing networking method that focuses on information, compared to TCP/IP, which focuses on hosts. This study proposed a solution to the growing complexity of computer networks. The fundamental problem will still be able to be found, one of them being difficulty in accessing a node as data traffic is piling up. One of the solutions is load balancing, which is implementing a system that will redirect packets to different service providers to ease up the load and reduce the number of packets that are dropped due to timeouts. This paper analyses the impact of load balancing in an extreme case emulated environment using Mini-NDN and random load balancing strategy. From the results gathered, implementing random load balancing to the consumer nodes decreases-and in some cases eliminates-dropped packets. In addition, implementing load balancing stabilizes packets' RTT (Round Trip Time) but increases the average RTT compared to using best route forwarding in an unloaded environment. © 2023 IEEE.

关键词： Topology

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Exponential Stabilization of Linear Systems Using Nearest-Action Control with Countable Input Set

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SSRN 2024年

作者： Almuzakki, Muhammad Zaki Jayawardhana, Bayu Tanwani, Aneel Vakis, Antonis I. Department of Computer Science Faculty of Science and Computer Universitas Pertamina Jakarta Indonesia Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Netherlands CNRS Université de Toulouse France

This paper studies stabilization of linear time-invariant (LTI) systems when control actions can only be realized in finitely many directions where it is possible to actuate uniformly or logarithmically extended positive scaling factors in each direction. Furthermore, a nearest-action selection approach is used to map the continuous measurements to a realizable action where we show that the approach satisfies a weak sector condition for multiple-input multiple-output (MIMO) systems. Using the notion of input-to-state practical stability, under some assumptions imposed on the transfer function of the system, we show that the closed-loop system converges to the target ball exponentially fast. Moreover, when logarithmic extension for the scaling factors is realizable, the closed-loop system is able to achieve asymptotic stability instead of only practical stability. Finally, we present an example of the application that confirms our analysis. © 2024, The Authors. All rights reserved.

关键词： Asymptotic stability

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Deep reinforcement learning-based Smart Education System with wireless sensor network virtualization

Deep reinforcement learning-based Smart Education System wit...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Abdulkareem, Sarah Ali Jabbar, Kadim A. Ahmed, Ibrahem Zearah, Sajad Ali Diame, Hussein Alaa Sadiq, Sadiq Nabeel Al- Turath University College Computer Technologies Engineering Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Scientific Research Center Al-ayen University Thi-Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Nisour University College Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350303452

Create a smart classroom that uses many sensors, such as webcams, to monitor each student's progress and leverages deep reinforcement learning (DRL) algorithms to provide personalized instruction based on each student's unique sensing data. Smart learning suggestion systems also analyze students' pulse rates, quiz scores, and expressions to deduce their current levels of comprehension. They then use DRL to provide suggestions for pupils based on where they are in their studies. It is beneficial to employ virtual reality in the classroom. Software virtualization technologies are now the main focus of IT and systems management publications. Virtualization technology might make service upkeep simpler. Changes to the classroom setting, the teaching strategy, and individual student conditions may drastically alter the efficacy of a conventional classroom. A smarter way to manage the classroom that takes context into account based on wireless sensor network (WSN) technology utilizing a virtualization process with DRL (WV-DRL) is suggested and implemented in this research to improve learning efficiency. Wireless sensors monitor students and classroom conditions, sending data to a server-based management system. The system provides feedback to both students and instructors, as well as wireless sensor-controlled classroom equipment. It uses virtualization technology to make the most of expensive classroom equipment and ensure its reliability. Smart classrooms with several sensors may model a Markov decision process to provide personalized learning suggestions. Preliminary findings indicate that our intelligent learning recommendation system is effective. This research provides useful guidelines for doing so in the future, paving the way for developing an intelligent learning environment that facilitates higher levels of personalization. The experimental results show the proposed WV-DRL to achieve an accuracy ratio of 90.2%, recall ratio of 92.3%, score query 4.7%, the f-scor

关键词： Virtualization

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Efficient Waste Services in High-rise Buildings Leveraging LSTM Time Series Forecasting and IoT Automation 4

Efficient Waste Services in High-rise Buildings Leveraging L...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Ranganathan, Chitra Sabapathy Mohankumar, N. Juliet, P. Sudha Vijayanand, S. Yuvaraj, S. Sujatha, S. Mphasis Corporation Tamil Nadu Chennai India Maharashtra Nagpur India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Kattankulathur Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

The preservation of natural resources in the environment depends on effective waste management in high-rise buildings. Enhancing waste services in such buildings is the focus of this research, which presents a groundbreaking technique that integrates Internet of Things (IoT) automation with Long Short-Term Memory (LSTM) time series forecasting. By analyzing data on trash creation in the past, the LSTM model can accurately forecast how much garbage will be generated in the future. IoT sensors are embedded in waste cans to track when they are full and send that information to an advanced management system. This system seamlessly combines LSTM forecasting with IoT automation to provide real-time garbage collection schedule adjustments that are influenced by both present and expected demand. Waste management operating costs and energy usage may be significantly reduced with this proactive strategy, which optimizes collection routes and avoids wasteful collections. The system ensures effective resource usage and allows for prompt interventions with real-time monitoring and alarms enabled by IoT technology. Traditional waste collection systems may not be feasible or efficient in high-rise buildings, which present their own set of problems compared to the suggested solution. The system provides a sustainable and cost-effective waste management approach that is customized to the unique demands of high-rise settings via the integration of advanced forecasting algorithms and IoT automation. This technique improves the quality of life for people while contributing to the sustainability objectives of urban development. © 2024 IEEE.

关键词： Cost effectiveness

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Identifying Early Biomarkers of Multiple Sclerosis with Feature Selection Algorithms 10

Identifying Early Biomarkers of Multiple Sclerosis with Feat...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Dixit, Krishna Kant Aswal, Upendra Singh Deepak, A. Mayuri, K. Sararswat, Manish Srivastava, Amit Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department_of Computer Science and Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

The project has done literature used to recognize the early biomarkers of Multiple Sclerosis with several feature selection algorithms using ML. It has also explained critical assessment is focused here on Multiple Sclerosis with the use of feature selection algorithms. The project has discussed research that mainly focuses on the understanding of complex neurological disorders and it encompasses several clinical presentations with a diverse range of symptoms by making early detection and intervention for making the long-term scheme of critical patients' well-being. It has also applied sophisticated algorithms for choosing features to a varied dataset made up of medical, and genetic, in addition, neuroimaging scans allowed researchers to find discriminatory biomarkers for the neurological disorder multiple sclerosis (MS). biomarkers, journal article, knowledge, data collection, model development © 2023 IEEE.

关键词： DXA data fractures learning medical imaging osteoporosis risk

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Analysis of Blockchain technology to Protect Data Access Using Intelligent Contract Mechanism for 5G Networks 6

Analysis of Blockchain Technology to Protect Data Access Usi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： William, P. Rai, Anjani Kumar Madan, Parul Kumar, C Praveen Shrivastava, Anurag Rana, Ajay Sanjivani College of Engineering Department of Information Technology Savitribai Phule Pune University Maharashtra Pune India Institute of Engineering & Technology GLA University Uttar Pradesh Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

With constantly changing internet models automating and digitising countless residential and industrial applications, the world of today has made significant progress in the sharing of healthcare data and the implementation of crucial actions. The two most crucial factors in the setting of healthcare data exchange today are privacy and data integrity. In order to prevent unauthorised individuals from viewing or accessing sensitive data, such as medical information, encryption is essential. The intermediary nodes shouldn't be given too much weight because conventional encryption techniques could not be deployable through them. The current security alternatives for IoT-driven healthcare monitoring frameworks are discussed in this article along with the numerous security challenges that exist in the current 5G network design and how blockchain technology may be used to encrypt data between nodes. The suggested approach outperformed the alternatives and the present strategies for IoT-driven healthcare monitoring frameworks'security solutions, according to a variety of performance factors utilised to test it. A number of performance parameters were used to test the suggested technique, and the results showed that it performed better than the alternatives. © 2023 IEEE.

关键词： Block chain Data Integrity Encryption Healthcare IoT Security

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Research on Computing Offloading Methods Based on Edge Computing and Reinforcement Learning in The Industrial Internet of Things

Research on Computing Offloading Methods Based on Edge Compu...

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International Conference on Internet of Things and Machine Learning, IoTML 2023

作者： Wang, Xian Wan, Qiang Li, JianCheng Faculty of Intelligent Manufacturing Wuyi University Jiangmen529020 China School ofComputer Science and Engineering South China University of Technology Guangdong GuangZhou510000 China School of Computer Science and Technology Guangdong University of Technology GuangZhou528300 China

ISBN: (纸本)9781510671805

Most of the existing research focuses on a single MEC server scenario, and there are few studies on computing offloading and resource allocation in multi-MEC server scenarios. Therefore, this paper proposes a dynamic computation offloading and resource allocation algorithm based on hybrid decision-making deep reinforcement learning, which utilizes deep deterministic policy gradients and dueling dual deep Q-networks to enhance the Actor-Critic structure. By calling the Actor part of DDPG to deal with continuous power allocation, and then combining the Critc part of DDPG with D3QN, the discrete MID is associated with the MEC server to solve the mixed decision-making problem in multi-MEC server multi-user scenarios. From the simulation results, we can see that the proposed algorithm has faster convergence and better stability than baseline algorithms such as DQN. © 2023 SPIE.

关键词： Reinforcement learning

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Explainable AI-Based Humerus Fracture Detection and Classification from X-Ray Images 26

Explainable AI-Based Humerus Fracture Detection and Classifi...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Barua, Koushick Mahmud, Tanjim Barua, Anik Sharmen, Nahed Basnin, Nanziba Islam, Dilshad Hossain, Mohammad Shahadat Andersson, Karl Hossain, Sazzad Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Chattogram Maa-O-Shishu Hospital Medical College Dept. of Obstetrics and Gynecology Chittagong4100 Bangladesh Leeds Beckett University Department of Computer Science and Engineering United Kingdom Chattogram Veterinary and Animal Sciences University Dept. of Physical and Mathematical Sciences Chittagong4202 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå971 87 Sweden Department of Computer Science and Engineering Dhaka1207 Bangladesh

ISBN: (纸本)9798350359015

The human skeletal framework relies heavily on bones, and one such crucial component is the 'Humerus.' Positioned in the upper arm, extending from the shoulder to the elbow junction, the Humerus provides essential structural support for muscles and facilitates upper-body movement, particularly in the arms and hands. Consequently, Humerus fractures significantly impact daily life, causing disruptions and limitations. This paper presents a thorough exploration of an Explainable AI-based Humerus Fracture Detection and Classification system, employing various deep learning models. Leveraging a dataset of 1266 X-ray images, encompassing fractured and non-fractured humerus bones from the publicly available 'MURA' dataset, our research evaluates the effectiveness of Convolutional Neural Networks (CNN), VGG16, VGG19, DenseNet121, and DenseNet169 in detecting fractures. After 30 epochs of training, we assessed their performance using critical metrics: accuracy, precision, recall, and F1 score. Notably, DenseNet121 and DenseNet169 exhibited superior accuracy, precision, and recall, laying a robust foundation for automated humerus fracture diagnosis. We also introduced two ensemble models, "Ensemble-1 (VGG16 and VGG19)"and "Ensemble-2 (DenseNet121 and DenseNet169),"which delivered substantial improvements in accuracy, precision, recall, and F1 score, showcasing the potential of ensemble techniques in clinical settings. Furthermore, we enhanced model interpretability and transparency by incorporating Saliency Maps and GRAD-CAM (Gradient-weighted Class Activation Mapping) for Explainable AI (XAI). This visualization allowed us to identify regions of interest in X-ray images contributing to the model's predictions, providing valuable insights for medical practitioners. © 2023 IEEE.

关键词： Convolutional neural network Explainable AI (XAI) Humerus X-ray diagnosis

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Precise Identification of Anomalies in Brats MR Brain Images using Metaheuristic Optimization Techniques 7

Precise Identification of Anomalies in Brats MR Brain Images...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rosaline, S. Maheswari, K. Dhanasakkaravarthi, B. Vaishali, M. Kannan, Sathish Selvaraju, Nandakumar R.M.K. Engineering College Department of Electronics And Communication Engineering Thiruvallur Tamil Nadu Kavaraipettai601206 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Agni College of Technology Department of Mechanical Engineering Chennai Tamil Nadu Thazhambur603103 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641 202 India Saveetha School of Engineering Saveetha Institute of Medical And Techinical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Hindustan Institute of Technology And Science Department of Automobile Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

A condition known as an anomaly occurs when some brain features behave improperly and cause either minor or significant harm to brain cells. Brain tumors, vascular malformations, and inflammatory lesions are among the most well-known abnormalities. A variety of Machine Learning (ML) and Deep Learning (DL) methods are employed in the analysis and detection of brain irregularities. The use of metaheuristic optimization is one such method. Three optimization processes have been chosen for this investigation. The study examines and documents anomaly detection efficiency. A public dataset comprising brain scan images from disparate institutions is employed in the research to analyze the effectiveness of the detection procedure. The three algorithms used are Sequential Minimal Optimization (SMO), Krill Herd Optimization (KHO), and Particle Swarm Optimization (PSO). The three algorithms are fed the preprocessed images for training and testing. From the simulation result, it is established that the KHO algorithm is the best method for identifying brain abnormalities. The KHO achieved an accuracy of 0.96, whereas the competitive model like PSO scored an accuracy value of 0.93 and SMO attained 0.95. The KHO method is quite accurate in detecting the presence of any kind of abnormality. © 2024 IEEE.

关键词： Anomaly detection

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