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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Gobi, N. Kapse, Vinod Mansiram Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

The primary objective of this research is to get a comprehensive understanding of Cloud Computing ontologies, their applications, and their specific areas of interest. The proposed research methodology for this systematic review comprises a well-defined research topic, criteria for including and excluding studies, and a comprehensive search strategy to identify relevant source papers for the review. The chosen research is categorized into four main areas: elucidation of cloud resources and services, elaboration of cloud security, clarity of cloud interoperability, and understanding of cloud services discovery and selection. Utilizing ontologies to improve the security and smooth integration of cloud computing services uncovers various challenges and identifies areas for more research. © 2024 IEEE.

关键词： Cloud Computing Cloud service ontology systematic review

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Influence of AI and MAS in Enhancing Energy Efficiency of 6G Communications

Influence of AI and MAS in Enhancing Energy Efficiency of 6G...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Negi, Divya Prakash, Febin Gupta, Anjali Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical And Electronics Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Next-generation Internet of Things (IoT) is progressing rapidly due to the introduction of beyond 5G (B5G) and the approaching arrival of 6G, which have improved the dependability, productivity, and profitability of both industrial and personal operations. Nevertheless, data overload is frequently caused by the abundance of wireless sensors in commercial 6G devices. Data mining gathers useful information in order to solve this. Meanwhile, in the 6G architecture, edge computing enables automated equipment operations and intelligent decision-making in real-time. Even so, despite the drawbacks of sensor-based energy sources, the integration of many technologies has led to an increase in energy. Using a multi-agent system (MAS) approach, a system model for industrial wireless sensor networks in 6G applications has been presented in order to combat this. Sensor node clustering locates and forecasts the locations of principal nodes by employing distributed artificial intelligence (DAI). Convolutional neural networks (CNN) and back-propagation neural networks (BPNN) are used in optimisation. Allocating resources to individual nodes efficiently is made possible by the analysis of inter-cluster correlations. The outcomes of the simulation show how well the approach works to reduce resource waste, increase overall energy efficiency, and protect important data. This strategy improves network performance in industrial 6G applications while simultaneously addressing concerns about energy consumption. © 2024 IEEE.

关键词： Multi agent systems

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The Firstever LTE Network on the Moon: Challenges and Prospects 5

The Firstever LTE Network on the Moon: Challenges and Prospe...

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5th IEEE International Conference on Recent Trends in computer Science and technology, ICRTCST 2024

作者： Routray, Sudhir K. Pappa, M. Singh, Monika Javali, Abhishek Sharmila, K.P. Tengshe, Richa CMR University Department of Computer Science and Engineering Bangalore India CMR Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350351378

Mobile cellular networks are very popular across the globe, and they are found at all places where human settlements are present. Long term evolution (LTE) is a popular standard for mobile wireless communications. It has been deployed in almost all the countries. These networks are deployed to support various services, such as voice, image, video, text, and data transmission. In 2024, NASA and Nokia together get ready to deploy an LTE network on the moon. This project was planned in 2020, and NASA selected Nokia as the vendor for this lunar LTE network deployment mission. The LTE network on the moon would help in the enhancement of lunar exploration. The common services of LTE are planned to be deployed on the lunar surface. In addition to that, some moon specific valueadded services will also be deployed to support the exploration tasks. The deployed network would send the pictures, videos, and other sensor measured data from the lunar surface back to earth. This lunar network will be supported by the orbiting satellites and other peripheral networks. In this paper, the basic features and characteristics of the first lunar LTE network and its utilities have been studied. © 2024 IEEE.

关键词： NASA

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Detection of Cardiovascular Activities of Multiple Patients using MIMO mmWave Radar

Detection of Cardiovascular Activities of Multiple Patients ...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Jena, Paramananda Sahu, Kedar Nath Behera, Sagarika Electronics and Radar Development Establishment DRDO Bengaluru India Dept. of Electronics and Communication Engineering Stanley College of Engineering and Technology for Women Hyderabad India Dept. of Computer Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350379693

This paper presents the detection and analysis of cardiovascular activities for localisation of multiple patients using Doppler signals associated with their chest wall motion, heart beat and sleep disorder. The modeling of the vital signs has been carried out considering the vibrating surface of human body illuminated by a radar. The main challenge of a medical radar is the noncontact diagnosis of multiple people. MIMO radar has enormous potential in two areas: micro Doppler detection and fine angular resolution. In this work, MIMO radar is designed and analysis of micro Doppler due to (i) chest movement, (ii) heartbeat and (iii) tidal volume of patients in their sleeping conditions has been carried out using MATLAB models. © 2024 IEEE.

关键词： Doppler radar

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Perfomance comparison of Machine Learning (ML) based fault detection scheme for a compensated transmission line

Perfomance comparison of Machine Learning (ML) based fault d...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Mazibuko, N. Akindeji, K.T. Moloi, K. University of Technology Smartgrid Research Center Durban Department of Electrical Power Engineering Department of Electronics and Computer Engineering Durban South Africa

ISBN: (纸本)9798350389388

Compensated transmission lines are employed in power system networks to increase energy transmission capacity, improve system stability, decrease transmission losses, and provide more flexibility in power management. However, the effectiveness of protection relays in detecting, classifying, and locating faults in such networks is significantly impacted. This is due to changes in impedance within the fault loop caused by devices used in compensated networks, which alters the apparent impedance seen by the relay. This study introduces three different fault detection and classification schemes for compensated transmission lines. Initially, an IEEE 9-bus system with series compensation at line 4-6 is utilized for data collection. Feature extraction using Principal Component Analysis (PCA) is then conducted, followed by the implementation of fine k-nearest neighbors (KNN), Medium Nuero network (MNN), and SQVM classifiers. A comprehensive performance analysis is carried out, revealing that SQVM achieves the highest overall accuracy of 99.3%, while the fine tree outperforms SQVM in single-line fault detection. © 2024 IEEE.

关键词： Power transmission lines

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Experimental Fire Detection and Alarm System Based on the Integration of IoT and Image Processing

Experimental Fire Detection and Alarm System Based on the In...

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2024 IEEE International Conference on Consumer electronics-Asia, ICCE-Asia 2024

作者： Vuong, Dat Tran, Van Vu Tran, Van Thanh Nhat Tran, Huynh Anh Hao Le, Pham Hoang Son, Tran The Vietnam-Korea University of Information and Communication Technology Faculty of Computer Engineering and Electronics Da Nang Viet Nam

ISBN: (纸本)9798331530839

Fires are considered a risk that causes human death and property loss. To reduce fire damage, fires must be detected and warned promptly. This paper aims to design and experimentally implement a fully-function fire detection and warning system. The developed system is based on a Wireless Sensor Network (WSN) that includes various nodes distributed across the city. Each node has two main blocks which are the image processing and communication blocks. The image processing block applies the EfficientN et B0 model to detect the fire and smoke, whereas the communication block uses the Module 4G to contact the house owner over a mobile phone network and LoRa gateway to send the signal warning to the fire center over the a LoRaWAN. The Module 4G is responsible for sending captured images of suspected fire if any to the house owner's device for getting his/her acknowledgement. Then an urgent alarm message consisting of fire position and risk level are sent to the fire center if the fire is acknowledged by the house owner, otherwise, the alarm is ceased. Besides, the proposed system is installed the gas sensor, smoke sensor, water spray system and light-sound alarm to augment the reliability of fire detection. The proposed system is experimentally demonstratedand achieved positive results: Delay time sending from the proposed fire detection system to the user is recorded as 5 seconds and from the user to the fire center is recorded as 10 seconds, within a LoRaWAN coverage of 1.5km. © 2024 IEEE.

关键词： Sensor nodes

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LSTM-QDCNN: long short-term memory and quantum dilated convolutional neural network enabled occlusion percentage prediction

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Australian Journal of Electrical and electronics engineering 2025年第1期22卷 1-14页

作者： V. Sokashe Ghorpade, Shital Arjunsing Pardeshi, Sanjay Department of Electronics and Computer Engineering Sharad Institute of Technology College of Engineering Yadrav Maharashtra Ichalkaranji India Government Residence Women’s Polytechnic Tasgaon Dist-Sangli416 312 India Shivaji University Maharashtra Kolhapur India

Occlusion detection is vital in longer video sequences of real-time scenarios, as deformable objects generally occlude themselves or be occluded by exterior objects eventually. Occlusion can affect accuracy of the entire model significantly, because optical flow may reveal random attributes in occluded as well as neighbourhood areas. Occlusion is a challenging issue in real-world visual object tracking. Several techniques learn imprecise appearance of target while it becomes occluded by another object in the scene. Occlusion is a complicated issue that causes target loss in tracking procedure. Here, long short-term memory fused quantum dilated convolutional neural network (LSTM-QDCNN) is presented for occlusion percentage prediction. Initially, video frames are extracted from considered input video. The extracted frame is pre-processed by adaptive fuzzy filtering, and object localisation is accomplished by employing sparse fuzzy c-means with local optimal oriented pattern feature. Thereafter, occlusion category detection is conducted by deep Q-network. Lastly, occlusion percentage prediction is done by utilising LSTM-QDCNN. Additionally, LSTM-QDCNN is designed by merging deep LSTM (DLSTM) with quantum dilated convolutional neural network (QDCNN). Furthermore LSTM-QDCNN achieved maximum multiple-object tracking precision of 0.892 and minimum tracking distance of 3.714. In addition, LSTM-QDCNN achieved tracking number about 18. ©, Engineers Australia.

关键词： Long short-term memory

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Generative Artificial Intelligence: Principles, Potentials and Challenges 2

Generative Artificial Intelligence: Principles, Potentials a...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Routray, Sudhir K. Jha, Mahesh K. Sharmila, K.P. Javali, Abhishek Pappa, M. Singh, Monika Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350368413

In recent years, we have witnessed the raise of several generative artificial intelligence (AI) products and services which have strong disruptive capabilities. Majority of these AI-based products and services are also very popular since their public availability. These generative AI products and services have a lot of potentials to do the common office works, writing codes, doing accounting tasks, predicting common patterns, generating creative contents, marketing products and services, increasing efficiencies in the systems, translating to different languages, designing products, and many such things. These effects of generative AI are phenomenal and it is going to start a large scale automation in the product design and service sectors. In addition to the benefits, several challenges are associated with generative AI. Starting from the bias to security risks to fake content creation, there are many challenges with these products and services. In this paper, we go through the basic principles of generative AI and then study its present and future prospects. We analyze its perilous effects and the common challenges with a few examples. Overall, generative AI can be beneficial tool if it is used positively under appropriate regulatory systems. © 2024 IEEE.

关键词： Generative adversarial networks

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Evaluation of the Explainable AI-NLP Framework for Text Categorization

Evaluation of the Explainable AI-NLP Framework for Text Cate...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Singh, Amar Preet Gupta, Gaurav Soumya, K. Noida Institute Of Engineering And Technology Department Of Electrical & Electronics Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India

ISBN: (纸本)9798350366846

Although there has been a decrease in interpretability, significant progress has lately been achieved in developing state-of-the-art models. This assessment provides a thorough analysis of Explainable AI (XAI) within the framework of Natural Language Processing (NLP). This study examines the fundamental classification of descriptions in addition to the many techniques for producing and displaying explanations. The purpose of the survey is to provide a comprehensive study of the many approaches and strategies used to clarify the models' predictions, making it a helpful tool for people who develop NLP models. Additionally, we draw attention to the gaps in the current literature and urge further investigation into potential avenues for this crucial field of study. An experiment is also conducted and the results are discussed in this article. © 2024 IEEE.

关键词： Natural language processing systems

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A Hierarchical Aggregation Network with Edge Guidance for Sturdy Medical Image Segmentation 2

A Hierarchical Aggregation Network with Edge Guidance for St...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Rasheedha, A. Reethika, A. Rajeshwari, R. Remya, R. Kumar, M. Suresh Saranya, M.D. Department of Biomedical Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

Convolutional neural networks (CNNs) are essential for medical picture segmentation in clinical analysis and illness detection. On the other hand, noise levels and intraclass variability are considerable in medical photos, but interclass differences are negligible. It is difficult to extract strong contextual information and combine discriminative characteristics for fine-grained segmentation. Current techniques are unable to produce precise mask edges, resulting in fuzzy borders and decreased segmentation precision. In this research, a new A Hierarchical Aggregation Network with Edge Guidance that maintains precise spatial features and gathers rich contextual information is presented. The Efficient Fusion Attention Module is used by the proposed model to highlight important spatial and channel information, the Adaptive Hierarchical Feature Aggregation Module to maximize feature fusion, and the Inter-layer Edge-aware Module to improve edge prediction accuracy. Experimentally and qualitatively, the proposed method will perform better than six other cutting-edge segmentation techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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