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Deployment Schemes of In-Band Management Mode Based on Industrial SDN Network 7

Deployment Schemes of In-Band Management Mode Based on Indus...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mittal, Mudit Al-Jawahry, Hassan M. Varshney, Neeraj Kumar, Sunil Prashanth Michaelson, Jacob Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In order to properly manage and monitor their networks in industrial environments, industrial Software-Defined Networking (SDN) networks need to have an in-band administration mode. This research investigates a wide variety of alternative deployment options for in-band management mode in industrial SDN networks in order to increase the performance of the network, as well as its dependability and level of network security. We study how the performance of the network differs depending on whatever coupling of in-band management and industrial SDN architecture we choose. We analyse a range of potential pairings of in-band management with industrial SDN design. We also discuss the advantages and disadvantages of each deployment technique, providing insights into the creation and management of robust Industrial SDN networks. It provides useful recommendations for increasing the efficacy and security of particular network infrastructures, which might be valuable to both corporate groups and network administrators. The adoption of SDN technology and the installation of tailored management modes are becoming more and more significant as industrial settings continue to expand. This is of the utmost significance in order to guarantee the highest possible standards of both operational excellence and safety. © 2024 IEEE.

关键词： Deployment Schemes In-band Management Industrial SDN Network Network Control Network Monitoring Network Security

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Big Data and Reinforcement Learning, Emg Clustering 6

Big Data and Reinforcement Learning, Emg Clustering

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

作者： Naaz, Rohaila Bajpai, Yashi Kumar Verma, Pushpendra Balaji, N.V. Ramesh, P.N. Aher, Vijaya N. Teerthankar Mahaveer University Uttar Pradesh Moradabad India Invertis University Department of Computer Science & Engineering Uttar Pradesh Bareilly India School of Computer Science and Applications IIMT University Uttar Pradesh Meerut India Karpagam Academy of Higher Education Department of Computer Science Tamilnadu Coimbatore India Karpagam Institute of Technology Department of Computer Science Engineering Tamilnadu Coimbatore India Vishwakarma Institute of Information Technology E & TC Maharashtra Pune India

ISBN: (纸本)9798350304480

The need of creating improved that are better equipped to manage 'big data'has significantly grown due to the growing volume of data in electromyographic signal study. As a result, increasingly sophisticated created. This essay starts with a succinct overview of the key driving forces behind the expansion of big data age, then moves on to discuss the recent development of current shared data sets. After that, we cover current research and advancements in pattern recognition techniques that can be used for big data analytics. These new signal analysis approaches may be classified into two primary categories engineering utilizing a potential large specific focus on 'deep learning'. Finally, suggestions for further pattern research are presented and explored. © 2023 IEEE.

关键词： Big data Classification accuracy Convolutional neural networks (CNN) Deep learning Electromyographic signal study EMG clustering EMG pattern recognition Feature engineering High-density surface EMG Pattern recognition techniques Recurrent neural network (RNN) Reinforcement learning Surface EMG

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Humanoid Robots that Move on Two Feet and Are Led by Vision Systems 3

Humanoid Robots that Move on Two Feet and Are Led by Vision ...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kumar, Santosh Yadav, Satya Prakash School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jaipuria Institute of Management Department of Management Jaipur302033 India Glbitm Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350342338

At the convergence of robotics and computer vision lies a crucial research field that focuses on the development of vision-guided walking for humanoid robots. The ability of humanoid robots to walk independently while simultaneously seeing and comprehending their surroundings via the use of visual inputs offers tremendous potential. Humanoid robots are developed to reproduce human-like qualities and skills. This abstract discusses current developments in the area of vision-guided walking for humanoid robots as well as the approaches that have been used. Through the use of computer vision methods in conjunction with walking algorithms, humanoid robots are given the capacity to traverse situations that are both complex and dynamic while exhibiting increased flexibility. For this, real-time sensing of the environment, object identification and avoidance of obstacles, as well as analysis of the terrain, are required in order to arrive at intelligent judgements while moving. Vision-guided walking not only makes robotic locomotion more secure and effective, but it also paves the way for a wide range of potential applications, including exploration, human-robot interaction, search and rescue, and many more. Visual sensing systems, sensor fusion procedures, gait generation methods, motion planning, and control strategies. These topics are discussed in relation to vision-guided walking. Different types of sensors, such as cameras, depth sensors, and LiDAR, are being investigated to see how they can best provide a holistic understanding of the surrounding environment. In addition, the abstract draws attention to the difficulties caused by sensor noise, occlusions, fluctuations in illumination, and the need for real-time processing. In places an emphasis on the role that methods of machine learning play in the improvement of vision-guided walking. The use of deep learning algorithms gives robots the ability to learn from data, hence enhancing their capacity to understand and r

关键词： Head movement in robot Humanoid robots Planning and control Proprioceptive sensors Sensors Vision guided

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Blockchain for 5G-Enabled IoHT—A Framework for Secure Healthcare Automation

Blockchain for 5G-Enabled IoHT—A Framework for Secure Healt...

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Alam, Md Imran Ahmad, Md Oqail Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Khan, Haneef Qidwai, Khalid Ali Department of Computer and Network Engineering Jazan University Jazan45142 Saudi Arabia Department of Computer Applications B.S Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai600048 India Department of Computer Science Jazan University Jazan45142 Saudi Arabia

ISBN: (纸本)9789811976148

Medical care has evolved into a lifeline for many people;due to this, there is an explosion in the field of medical big data. Wearable technology based on the Internet of Things (IoT) is being used by healthcare professionals to speed up diagnosis and treatment. There are now numerous sensors and devices, which are connected to the Internet. Internet of HealthThings (IoHT) can be used in many different ways in healthcare. Sensors and remote-monitored medical devices can help patients get more accurate and efficient diagnostics. This paper focuses on healthcare automation using the Internet of Things (IoT)-enabled devices. IoT-driven healthcare models can keep patients healthy and safe, enabling doctors to provide better care. In addition, healthcare frameworks significantly reduce healthcare costs and improve performance. Blockchain systems are transparent and thus safe to store and share private information. Data collected by medical devices attached to the body is kept in a cloud server alongside relevant medical reports as part of the proposed framework. The use of blockchain and 5G technology means that patients’ data can be securely transmitted at high speeds and with rapid response times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Real Time Plant Disease Detection Model using Deep Learning 6

Real Time Plant Disease Detection Model using Deep Learning

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

作者： Sharma, Ashish Aswal, Upendra Singh Rana, Ajay Vani, V Divya Sankhyan, Akhil Shekhar Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Graphic Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Uttar Pradesh Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Uttar Pradesh Greater Noida India Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

Convolutional neural networks (CNNs) have had remarkable success in classifying a variety of plant diseases through deep learning. However, just a few researches have shed light on the inference process, leaving it as an unsolvable mystery. In addition to guaranteeing the learnt feature's dependability, revealing the CNN to extract it in an understandable form enables human intervention-based verification of the model's veracity and the training dataset. Using a CNN that had been trained using a public ally accessible collection of images depicting plant diseases, several neuron-wise and layer-wise visualisation techniques were used in this study. We demonstrated that neural networks can, when diagnosing an illness, capture the hues and textures of lesions particular to that disease, which is similar to human judgement. The most critical aspect of agriculture is striking a balance between produce and population. Due to a variety of issues, including natural disasters, unforeseen rainfall, nutrient deficiencies in the soil, etc., the majority of farmers failed to produce and balance the crops. The main issue, however, is pest infection, which is the root of all the issues. To learn about plant diseases, several researchers employed a variety of methods. Convolutional neural network-based deep learning techniques are frequently utilised to solve image-oriented problems. A powerful and successful method for image analysis is the CNN (ConvNet) neural network model of deep learning. In this study, various models for plant disease detection with CNN are compared. The research report concludes by summarising its findings, identifying its limitations, and making recommendations for classification © 2023 IEEE.

关键词： classification deep learning image processing machine learning

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An Intelligent Framework for Movie Recommendation Through Online Social Media 6th

An Intelligent Framework for Movie Recommendation Through On...

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6th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Agarwal, Gaurav Dinkar, Shail Kumar Agarwal, Ajay Department of Computer Science and Engineering Veer Madho Singh Bhandari Uttarakhand Technical University Uttarakhand Dehradun248007 India Department of Computer Science and Applications Govind Ballabh Pant Institute of Engineering and Technology Pauri Uttarakhand Garhwal246194 India Department of Information Technology Krishna Institute of Engineering and Technology Muradnagar U.P. Ghaziabad201206 India

ISBN: (纸本)9789811995118

The production, promotion, and distribution of content in the electronic media and entertainment sector must adapt to new strategies. The reason for this is that current customers can search for and access content on any device, at any time, from anywhere. The world has entered a media-rich information period due to the proliferation of online services and flexible innovations. An efficient recommender system always makes sure to record the users’ actual preferences and only makes recommendations for items that the user genuinely wants. Recommender systems have been used for two decades to recommend goods, contents, and services to online users in a variety of applications. Although recommender systems have been successful in many application domains, there are still a number of problems that limit their effectiveness. This paper suggests a hybridized algorithm for a movie recommendation system that uses a particle swarm optimization-based crow search algorithm. The suggested model considered both the most recent and previous user ratings and conducted statistical analysis on a real dataset. Additionally, when performing particle swarm optimization, the items’ contents are taken into consideration. As a result, the recommender system’s data sparsity issue is greatly diminished. More individualized movie track recommendations are made possible by the concept of hybridization. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Statistical methods

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Video Surveillance-Based Intrusion Detection System in Edge Cloud Environment 3rd

Video Surveillance-Based Intrusion Detection System in Edge ...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Annu Devasenapathy, Deepa Raja, M. Shadrach, Finney Daniel Shirgire, Anil Arun, R. Yau, Thomas Moh Shan Department of Computer Science and Applications Rajarajeshwari College of Engineering Karnataka Bengaluru560074 India Instructor-II/Computing and Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Fort Myers United States Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bengaluru560037 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Civil Engineering Dr. D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Director of Centre of University Courses and Innovative Learning University of Technology Sarawak Jalan Universiti Sibu Sarawak96000 Malaysia

ISBN: (纸本)9789819986453

Video surveillance is mostly used for monitoring distant videos and the cloud memory in the system provides more secure to the data. But, in the existing methodologies poor intrusion detection accuracy as well as poor performance rate is considered as the main demerits. So, to overcome the above-mentioned demerits, a novel video surveillance-based Intrusion Detection System (IDS) (VS-IDS) in the edge cloud environment via deep learning method has been developed in this research. Different algorithms are used for object detection and intrusion detection. Subsequently, the proposed model can be implemented and verified in NVIDIA Jetson TX2 platform. The proposed model attains better rate of performance than the existing models. The Performance of the proposed model can be validated with accuracy, precision, recall, MSE and F-measure. Higher performance rate indicates that the system can detect intrusion more accurately than the existing mechanisms. 99% of detection accuracy can be attained through the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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A Machine Learning Approach for Predicting Air Quality Index in Smart Cities 5

A Machine Learning Approach for Predicting Air Quality Index...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Swamynathan, S. Sneha, N. Ramesh, S.P. Niranjana, R. Ponkumar, D. David Neels Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science & Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

One essential natural resource, the air quality index has been deteriorated by economic activities. Many studies have been conducted about the forecasting of periods of unsafe atmospheric conditions;however, the majority of these studies are limited in their capacity to include seasonal and other factors due to insufficient longitudinal data. India noise pollution has compiled a 6-year dataset that has been used to build several prediction models. There are encouraging outcomes when AQI levels are predicted using machine learning methodologies such as support vector machines (SVMs) artificial neural networks (ANNs), and Naive Bayes (NB). However, in several tests involving datasets from three different locations, NB, ANN, and SVM performed well to maximize prediction performance. When it comes to mean absolute error (MAE), R-squared (R2), and Root-mean-square deviation (RMSE), the combined performance of stacks is unsurpassed. In the related work section, summarize the limitations of existing models, such as inadequate data preprocessing, low spatialtemporal resolution, limited feature selection, or overfitting in complex models. Highlight gaps in scalability, real-time adaptability, and generalization across regions, emphasizing the need for robust, efficient models tailored to air quality prediction. © 2024 IEEE.

关键词： Support vector machines

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Graph Theory and Spectral Clustering in Multi-Objective Optimal Power Flow Analysis

Graph Theory and Spectral Clustering in Multi-Objective Opti...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Sumalatha, Ippa Asha, V. Renamala, Madhu Takuli, Arun Kumar Thethi, H. Pal Alwan, Adil Abbas Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Computer Applications Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gl Bajaj Institute of Technology & Management U.P. Greater Noida India Lovely Professional University Phagwara India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

Optimal Power Flow (OPF) analysis remains a cornerstone in modern power system operations, ensuring efficient and reliable power delivery. With the integration of diverse energy sources and the complexity introduced by emerging grid technologies, conventional OPF techniques can be limiting. This paper presents a groundbreaking approach by integrating graph theory and spectral clustering to handle multi-objective OPF challenges. Graph theory provides a structured framework to represent and analyze power systems while spectral clustering offers a powerful tool to delineate distinct operational zones, allowing for enhanced localization in solving OPF problems. The proposed methodology decouples larger networks into subnetworks, achieving a balance between global and local optimization objectives. Results from simulations on standard power system test cases demonstrate the superiority of the method in terms of computational efficiency and solution quality, compared to traditional OPF solutions. By synergistically merging these mathematical tools, this research paves the way for a more resilient and adaptable power system operational framework in the face of increasing grid intricacies. © 2023 IEEE.

关键词： Graph Theory Multi-objective Optimization Optimal Power Flow Power System Operations. Top of Form Spectral Clustering

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An Autonomous Emotion Recognition Strategy Employing Deep Learning for Self-Learning 3

An Autonomous Emotion Recognition Strategy Employing Deep Le...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

ISBN: (纸本)9798350342338

Because of their individuality and diversity, facial expressions are crucial to deciphering human communication and humour. Predicting a person's facial expression accurately not only helps you understand their emotional state but also gives you greater control over how you engage with them. Since personalised interactions are shown to enhance development and rapport., this ability to autonomously recognise human facial emotions has tremendous promise in fields like finance and education. In this study., we provide a new method for identifying human emotions based on Convolutional Neural Networks (CNNs). This method departs from conventional approaches by harnessing the potential of modern machine learning methods that make use of face datasets. When implemented in the TensorFlow framework., this method might significantly alter the way grammatical constructions are understood. Convolutional Neural Networks are at the core of our technique since they are a complicated design that can easily deal with complex picture data. We overcome the limits of previous methods by using CNNs to extract complex characteristics from face photos, which we access by abandoning traditional paradigms. To ensure the efficacy of our approach., we ran trials using the Facial Expression Recognition Challenge (FERC-2013) Dataset., a collection of 32 thousand different human faces. Surprisingly., our method achieved classification rates over 65%., demonstrating its efficiency in deciphering human emotions across a wide range of photos. Our approach is built on the incorporation of the AlexNet Architecture., a powerful algorithm that acts as the process's conductor. One interesting feature is that it can be trained in batches., which speeds up the learning process and requires less resources. This effectiveness not only aids in faster learning., but also makes the method more widely applicable. Our work essentially advances facial emotion identification into the cutting edge of machine learni

关键词： Affective computing Deep learning Emotion recognition Facial expression analysis Machine learning Neural networks Self-learning

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