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Chaotic Hippopotamus based Energy Efficient Routing for Wireless Sensor Networks 4

Chaotic Hippopotamus based Energy Efficient Routing for Wire...

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4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Vedanarayanan, V. Jayaraman, Venkatesh Lakshmi, R.T.thivya Surendran, R. Sathyabama Institute of science and technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

the energy efficient routing in wireless sensor network (WSN) is crucial for enhancing the network longevity. For this, energy efficient routing protocol is introduced with cluster based routing. Initially, the sensor nodes are deployed randomly in the network area and are clustered together using the Chaotic Hippopotamus Optimization (ChHipO) Algorithm. the algorithm considers remaining energy, delay and distance as the multi-objective fitness factor for choosing the cluster head (CH) using the ChHipO Algorithm. the proposed ChHipO Algorithm integrates iterative chaotic mapping in the hippopotamus optimization algorithm for improving the rate of convergence. the analysis of ChHipO Algorithm based on residual energy, network lifetime, throughput and end-to-end delay acquired the values of 0.87, 99.728, 99.589 and 3.01 respectively. © 2024 ieee.

关键词： Sensor nodes

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Investigation on Deep Learning and Machine Learning Approaches for Antenna Design Based on Radar Signal processing 4

Investigation on Deep Learning and Machine Learning Approach...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Govindasamy, Santhakumar Bhushan, Kavi Nijhawan, Ginni Kumar, B Santhosh Chandrashekar, Rakesh Issa, Ali Ashoor Sri Krishna College of Technology and Tamilnadu Electronics and Communication Engineering India Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

this paper gives a complete review of the use of Deep Learning (DL) and Machine Learning (ML) approaches in the area of antenna design for radar data processing. It analyzes the potential of DL and ML to overcome the constraints of conventional antenna design methodologies, especially in the face of complicated environmental variables and the necessity for interference reduction. By employing sophisticated computational techniques, the research reveals how these AI-based approaches may considerably boost the design and performance optimization of radar antennas. the integration of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning into the design process offers promise for constructing adaptable and efficient radar systems. Empirical findings from the research illustrate the durability of ML models, notably Support Vector Machines (SVMs), in forecasting antenna performance, stressing its resilience even in high-noise environments. this investigation's results are crucial for the progress of intelligent radar systems and lay the way for future improvements in the industry. © 2024 ieee.

关键词： Support vector machines

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An Integrated Framework for Real-Time Disaster Data Acquisition, processing, and Resource Optimization in Crisis Management Systems 4

An Integrated Framework for Real-Time Disaster Data Acquisit...

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4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Saranya, G. Atchaya, M. Harshini, R. Choudhary, Kartik School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331529635

the rapid onset and unpredictable nature of disasters necessitate a quick, organized, and efficient response. this paper proposes a comprehensive disaster management platform that integrates real-time disaster information, weather forecasting, and resource management to enhance disaster preparedness and response. the platform collects disaster data from geophysical monitoring stations, processes the data for consistency, and stores it in a MongoDB database. Geospatial visualization tools, such as Folium, are employed to present disaster events on interactive maps, helping decision-makers assess the situation and respond accordingly. Additionally, the platform includes features for weather forecasting and resource management, allowing users to monitor conditions and contribute essential resources in real time. this research demonstrates how leveraging modern technologies can improve disaster management systems, providing a more effective and coordinated response to natural and man-made disasters. © 2024 ieee.

关键词： Weather forecasting

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Sparse Reduced-dimension Clutter Suppression Method Based on Multi-domain Joint processing 4

Sparse Reduced-dimension Clutter Suppression Method Based on...

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4th international conference on Neural Networks, Information and communication Engineering, NNICE 2024

作者： Wang, Qiang Min, Gang Xue, Bin Song, Dan Liu, Jianbin Zhang, Yani National University of Defense Technology Test Center Xi'an China National University of Defense Technology School of Information and Communication Wuhan China Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Tsinghua University Laboratory of Flexible Electronics Technology Beijing China

ISBN: (纸本)9798350394375

To improve the heterogeneous clutter suppression performance of airborne radar, a fast sparse reduced-dimension method based on multi-domain joint processing is proposed in this paper. Firstly, spatial diversity is adopted among transmitting sub-apertures to realize reduced dimension of spatial domain. Secondly, multiple equivalent samples are obtained by matching reconstruction. thirdly, the dimension of these samples is further reduced based on energy transform domain. Finally, the clutter is suppressed by joint sparse recovery, and simulations demonstrate the effectiveness of proposal. © 2024 ieee.

关键词： Clutter (information theory)

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Navigating the Gray Area: A three-Label Framework for Uncovering Uncertainty in Fake News 4

Navigating the Gray Area: A Three-Label Framework for Uncove...

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4th ieee international conference on Computing, communication, and intelligent Systems, ICCCIS 2023

作者： Jolly, Aman Kumar, Shailender Computer Science and Engineering Delhi Technological University Delhi India

ISBN: (纸本)9798350306118

Detecting the veracity of textual content, and distin-guishing between fake and real news, has been greatly improved with the advent of BERT-based transformers. However, an in-herent challenge arises when encountering news articles that fall within the borderline of the latent vector space between fake and real. In this paper, we propose a three-label framework to address this uncertainty issue and leverage an external data enrichment module, Google Fact Check, to handle such cases. Our approach utilizes a constraint dataset for training both the CT-BERT and BERT base models. Experimental results demonstrate that while the BERT base model achieves an accuracy of 80%, the accuracy of CT-BERT, our proposed framework, reaches 93%. this significant improvement in a ccuracy showcases the effectiveness of our approach in tackling uncertainty in fake news detection. © 2023 ieee.

关键词： Vector spaces

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Natural Language processing (NLP) based Phonetic Insights for Improving Voice Recognition and Synthesis 4

Natural Language Processing (NLP) based Phonetic Insights fo...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Priyadharshini, N. Subbulakshmi, N. Kalasalingam Academy of Research and Education Department of Computer Applications Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Computer Science and Engineering Krishnankoil India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Voice analysis in Natural Language processing (NLP) involves the processing of spoken language to develop applications such as speech recognition, synthesis, and speaker identification. this survey explores the phonetic intricacies of Tamil and other Dravidian languages, examining their impact on NLP. the extensive phonetic inventory of the Tamil Language is characterized by its complex vowel system, retroflex consonants, and the distinction between aspirated and unaspirated stops, which presents unique challenges and opportunities for computational linguistics. Phonetics helps to process the human language both accurately and naturally and it works as a bridge between the spoken and written communication forms. this paper emphasizes the necessity of developing specialized phonetic modeling techniques and robust algorithms. Additionally, it highlights the importance of creating comprehensive phonetic datasets and leveraging advancements in machine learning and deep learning to improve NLP tasks such as speech recognition and synthesis. A brief comparison on algorithms and applications associated with voice-assisted technologies. By advocating for interdisciplinary collaboration among linguists, computer scientists, and language technologists, this survey aims to inspire further research and innovation in processing Tamil and other Dravidian languages, ultimately contributing to more inclusive and efficient NLP systems for these linguistically rich languages. © 2024 ieee.

关键词： Deep learning

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Cloud Load Balancing Algorithms Assessment by Response Time 4

Cloud Load Balancing Algorithms Assessment by Response Time

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4th ieee international conference on Computing, communication, and intelligent Systems, ICCCIS 2023

作者： Sharma, Anjali Sharma, K.K. IIMT University Department of Computer Science Meerut India

ISBN: (纸本)9798350306118

Cloud technology is a way of computing that works like a distribution system and behaves like parallel plus grid computing in the most popular era, which is the digital period. this technology takes advantage of resource distribution to boost association as well as decrease system overhead. Cloud computing is a fresh skill that is continually being improved. Load balancing concept of distributing the node's entire existing load in order to improve efficiency in work and response time, which will create a bond in terms of customer satisfaction. there are various well-known approaches utilised in the cloud to optimise job schedules. this research paper is an assessment of some of the priority round-robin algorithms, Equally Spread Current Execution, and throttled algorithms, which will focus response time and also be estimated and evaluated with the tool. © 2023 ieee.

关键词： Customer satisfaction

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DDoS Attack Detection Method Based on Improved Bagging 4

DDoS Attack Detection Method Based on Improved Bagging

引用

4th international conference on communication Technology and Information Technology, ICCTIT 2024

作者： Zhi, Haiyou Li, Jingxian Yu, Mengyue Wang, Jin Hu, Ziyan Li, Yihan Wuhan Donghu University School of Computer Science Wuhan China

ISBN: (纸本)9798331528973

In view of the complexity and concealment of distributed denial of service attacks, a DDoS attack detection method based on improved Bagging ensemble learning is proposed. In order to effectively deal with the changing attack modes and improve the stability and accuracy of the detection model, this method enhances the recognition ability of DDoS attacks by introducing a weighted voting mechanism and combining multiple machine learning algorithms. Experiments on the CIC-DDoS2019 dataset show that after preprocessing steps such as feature selection, data normalization and feature correlation analysis, the improved Bagging model using RF, DT and KNN as base learners has an accuracy of 99.98% on the test set, which is significantly better than the traditional Bagging method and other detection models. this study shows that the improved Bagging method not only improves the accuracy and robustness of DDoS attack detection, but also provides new ideas for solving challenges such as high-dimensional data processing and dynamic network traffic analysis. © 2024 ieee.

关键词： Denial-of-service attack

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IoT-based automatic road accident detection systems: A review 4

IoT-based automatic road accident detection systems: A revie...

引用

4th international conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Prabu, C.R. Namitha, Kalakunnath Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798331515720

With the increasing number of vehicles on roads, the occurrence of road accidents has become a major concern globally. Automatic road accident detection systems based on Internet of things (IoT) technologies have emerged as a promising solution to improve emergency response times and reduce the severity of accidents. the review encompasses various aspects including the underlying technologies, sensor networks, data processing algorithms, communication protocols, and system architectures used in these systems. Furthermore, the paper discusses the challenges and limitations of current approaches such as sensor accuracy, system scalability, and real-Time data processing. It also identifies future research directions including the integration of advanced sensor technologies, machine learning algorithms for accident prediction, and the development of standardized communication protocols for interoperability among different systems. To improve road safety, researchers and practitioners can use the ideas to help build IoT-based autonomous road accident detection systems that are more effective and efficient. © 2024 ieee.

关键词： Interoperability

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Design of human-computer interaction system based on virtual reality and its application in the dissemination of study lodge culture 4

Design of human-computer interaction system based on virtual...

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4th international conference on intelligent Design, ICID 2023

作者： Liu, Yiyi Shanghai Jiao Tong University School of Design Department of Design Shanghai200240 China

ISBN: (纸本)9798350342574

Virtual reality (VR) is an emerging computer environment that utilizes virtual environments to achieve virtual assembly. It has broad application prospects and important value in multiple fields. Interactivity is one of the main characteristics of virtual environments. In order to improve the intelligence of the cultural experience process in the "study lodge"and meet the dissemination of traditional Chinese culture among a wider audience, VR technology is introduced to design and apply an intelligent human-computer interaction system for the "study lodge"from both hardware and software aspects. Firstly, the hardware structure of the system was designed and MEMS measurement and communication equipment was determined. Secondly, with the support of hardware devices, VR technology is matched with PC terminals to establish an indoor 3D environment on the terminal devices. communication between different servers and terminals is established to achieve the web embedding and "study lodge"communication control in 3D scenes. Using touch screen interaction as the interactive end of the human-computer interaction system, we adopt the method of developing human-computer interaction interfaces to design good interaction between multiple terminals. the user interface interaction function and virtual handwriting function were implemented in the Unity engine. Finally, the features of the collected data were extracted through human-computer interaction method, and the extracted features were input into the classifier for feature recognition, achieving human-computer interaction. © 2023 ieee.

关键词： Virtual reality

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