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2nd International Conference on Microwave, Antenna and Communication, MAC 2024

作者： Priya, C. Ranganathan, Chitra Sabapathy Vetriselvi, D. Vanakamamidi, Rama Krishna Dhanalakshmi, S. Srinivasan, C. Department of Electronics and Communication Engineering Andhra Pradesh Puttur India Mphasis Corporation Tamil Nadu Chennai India SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India HCL Technology Ltd Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350104

Effective connection quality is the basis of wireless network topology management and routing control. Effective link quality estimates may increase throughput and assure data transfer, extending the whole network's lifespan. Quality-of-service (QoS) routing is crucial in wireless networks to guarantee that data to be sent along a trusted channel and address the issue of energy balancing. This paper presents a Hybrid Cuckoo Search with a Fuzzy (HCSF) algorithm to improve the wireless network system. This mechanism uses the cuckoo search (CS) algorithm to select the neighbor nodes intelligently;as a result, it minimizes the routing overhead. Furthermore, it uses the fuzzy logic system to select the best forwarder from sender to receiver. Hence, it minimizes the network delay and packet loss ratio. The experimental outcomes using the Network Simulator tool significantly enhance the forward ratio and minimize the wireless network's routing overhead. © 2024 IEEE.

关键词： Quality of service

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Batik Classification in Indonesia: Exploring its Significance on Tourism and Economy

Batik Classification in Indonesia: Exploring its Significanc...

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2023 International Conference on Sustaining Heritage: Innovative and Digital Approaches, ICSH 2023

作者： Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Jain, Vishal Kumar, Anurag Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350398281

UNESCO has recognized the centuries-old art of batik weaving in Indonesia as a Masterpiece of the Oral and Intangible Heritage of Humanity. It has been passed down through the centuries in Indonesia, and its intricate designs and themes have won international praise. The classification of batik patterns according to factors such as their cultural background, manufacturing process, and symbolic meaning has been more popular in recent years. The purpose of this study is to investigate how batik categorization has affected Indonesia's tourism and economy. It looks at how batik has evolved through time and across various cultures, as well as its historical and cultural importance in Indonesia. The proper categorization of batik has been achieved with the use of fundamental machine learning and deep learning techniques (CNN and EfficientNet). An improved version of EfficientNet achieves 93.81 percent accuracy in training, according to the results. The importance of batik museums and batik festivals in boosting tourism in Indonesia is also explored in this study. This article suggests that categorizing batik might have far-reaching effects on Indonesia's tourism and economy. By promoting and preserving the rich cultural heritage of Batik, Indonesia can attract more tourists and generate greater economic benefits for the country. © 2023 IEEE.

关键词： Deep learning

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Precision Agriculture using Hybrid I-SVM and K-means Clustering to Improve Soil Fertility Level Prediction Accuracy

Precision Agriculture using Hybrid I-SVM and K-means Cluster...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Devi, T. Seetharaman, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Anna University Department of Ece CEG Campus Chennai India

ISBN: (纸本)9798350309140

Global demand for agricultural products will increase due to population expansion. Agriculture plays a major part in the financial system. Precision farming, often known as digital agriculture, and agriculture technology have emerged as new fields of study that use data-centric methods to increase production. The data generated by various sensors in today's agricultural operations allows for a better understanding of both the operation and the surrounding environment. The main goal of this research is to improve agricultural technologies. The yield of crops is predicted with the help of K-means clustering algorithm and Improved Support Vector Machine which includes analysis of the fertility level of soil. Based on previously archived yield data that was obtained from that region, a projection was created. The systems analyze the data, and using pattern mining based on many characteristics, they forecast the crop production. Using data mining techniques, rules are formed for training data classification, and test data predictions are put into practice. To enhance soil fertility level prediction accuracy through the integration of a Hybrid I-SVM (Incremental Support Vector Machine) and K-means Clustering approach which demonstrates exceptional performance, achieving an impressive accuracy rate of 97.7%. This research contributes valuable insights to precision agriculture, offering an advanced tool for accurate soil fertility assessment, crucial for optimizing crop management and agricultural productivity. The outcomes also demonstrate that the model accurately forecasts the phenomena and chooses the appropriate yield class. © 2024 IEEE.

关键词： Data accuracy

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Employing Convolutional Neural Network for IoT Healthcare Attack Detection in Intensive Care Unit 6

Employing Convolutional Neural Network for IoT Healthcare At...

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

作者： Sharma, Ishu Kaushik, Keshav Pahuja, Vanshika Chhabra, Gunjan Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science University of Petroleum and Energy Studies Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350304480

The Internet of Things helps the healthcare sector to embrace the methodologies with emerging technologies and facilitate technological change from traditional to highly equipped technology. Some of the frequent uses of the Internet of Things in healthcare include monitoring patient healthcare, enhancing proper healthcare and searching medical resources. This digitalization and increase in the use of internet-connected sensors in healthcare have taken to a new height through advancement in technology. But security and efficient use of IoT in healthcare is still a critical challenge. Cyber-attacks are very common at present with the main motive of hacking private patient data and stealing other health information from the devices equipped for fetching patient's well-being. This research paper shows the employment of a convolutional neural network for detecting attacks on Intensive care units in hospitals. The results achieved by the convolutional neural network technique prove that deep learning methodologies are a promising solution to provide reliable and trustworthy communication in healthcare sectors with the Internet of Things devices. © 2023 IEEE.

关键词： Convolutional Neural Networks Health care Intensive Care Unit Internet of Things Security

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Transformers for Image-Goal Navigation

arXiv

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arXiv 2024年

作者： Pelluri, Nikhilanj V. Minnesota Robotics Institute Department of Computer Science and Engineering University of Minnesota Twin Cities United States

Visual perception and navigation have emerged as major focus areas in the field of embodied artificial intelligence. We consider the task of image-goal navigation, where an agent is tasked to navigate to a goal specified by an image, relying only on images from an onboard camera. This task is particularly challenging since it demands robust scene understanding, goal-oriented planning and long-horizon navigation. Most existing approaches typically learn navigation policies reliant on recurrent neural networks trained via online reinforcement learning. However, training such policies requires substantial computational resources and time, and performance of these models is not reliable on long-horizon navigation. In this work, we present a generative Transformer based model that jointly models image goals, camera observations and the robot’s past actions to predict future actions. We use state-of-the-art perception models and navigation policies to learn robust goal conditioned policies without the need for real-time interaction with the environment. Our model demonstrates capability in capturing and associating visual information across long time horizons, helping in effective navigation. © 2024, CC BY.

关键词： Navigation

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Advancing AI: Deep Learning Techniques for Handwritten Digit Recognition

Advancing AI: Deep Learning Techniques for Handwritten Digit...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Gohad, Ameya Chadalwada, Dev Agrawal, Poorva Gupta, Gopal Kumar Yadav, Uma Singh, Ajit K. Nagpur Campus Pune India Ramdeobaba University Computer Science and Engineering Nagpur India School of Technology Management and Engineering SVKM's Narsee Monjee Institute of Management Studies Indore India

ISBN: (纸本)9798331540821

computer science and having to deal with written documents by hand are the most important issues when considering digitising data that is written manually. This work deals with a case study of automatic recognition of handwritten documents, embracing techniques and algorithms from both qualitative and quantitative sides. The challenge of the handwritten documents' content, which also involves, mathematical expressions, text, diagrams, numerals, and tables renders the task of individualisation for recognition algorithms difficult. Thus, different kinds of identification systems are invented every day each of which may be a good fit for a particular situation but also could be incompetent for another one. The analysis starts with a bibliometric survey of the literature that was done using the Scopus database and it focuses on metrics such as the amount of published articles, the number of times they have been cited, the countries of the origin and the writers that are most productive in the field of handwritten text recognition. Then, we look into an analysis, which includes the learning technologies currently used. These applications involve digit recognition, mathematical expression recognition text recognition, table recognition, etc. The approaches are combined to address heterogeneous elements in handwritten documents such as images. Last but not least, the study reveals the scope for further studies on handwritten document recognition. Aspects of major interest are the boost of reliability and credibility in the recognition systems, dealing with diverse inter-language factors and handwriting styles as well as with data complexity including diagrams and tables, and the utilization of further machine learning and AI modernization in the development of sophisticated recognition algorithms. © 2024 IEEE.

关键词： digit recognition digitization document classification handwritten document recognition learning techniques mathematical expression recognition qualitative analysis quantitative analysis

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Personalized Care for Multiple Sclerosis with IoT Wearables and Deep Learning

Personalized Care for Multiple Sclerosis with IoT Wearables ...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Vetriselvi, D. Sampathrajan, Rajeshkumar Deepa, A. Sujatha, V. Deepa, P. Pandi Murugan, S. Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Agni College of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

This research work presents a new method for improving individualized treatment for patients with Multiple Sclerosis (MS) by combining Internet of Things (IoT) wearables with deep learning (DL) methods, namely Long Short-Term Memory (LSTM) networks. MS is a long-term autoimmune disorder that affects the central nervous system. It is marked by unexpected episodes of worsening symptoms and periods of improvement, highlighting the need for tailored therapy. The widespread use of IoT devices presents unique possibilities for the ongoing monitoring of many physiological and activity measurements important to the development and symptoms of MS. Using LSTM networks, which are very proficient in analyzing temporal sequences, create a prediction model that may anticipate exacerbations of MS symptoms and customize therapies appropriately. Through the analysis of longitudinal data obtained from IoT devices, our model acquires knowledge about unique patterns of illness development and treatment response. This knowledge allows us to create customized care plans for individuals. These paths include proactive activities such as adjusting medication, making lifestyle changes, and seeking early medical advice. The goal is to reduce the worsening of symptoms, improve the quality of life, and optimize treatment results for individuals with MS. The proposed method shows potential for transforming the treatment of MS by offering proactive and patient-centered therapy adaptable to individual requirements and the dynamics of the illness. © 2024 IEEE.

关键词： Patient treatment

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Enhanced Extremist Reviewer Group Detection in Online Product Reviews using RNN in comparison with CNN 9

Enhanced Extremist Reviewer Group Detection in Online Produc...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Ajay, K. Loganayagi, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350384208

The study aims to improve the accuracy of detecting extremist reviewer groups in e-commerce product reviews by utilizing algorithmic techniques. This research utilizes Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with various grounding categories to predict the detection of extremist reviews The article uses data from CSV files that are available on the Kaggle website. A GPower test was conducted, resulting in an 80% power level using α=0.05 and power=0.80 as the parameters Predicting the extremist reviewer groups in e-commerce with improved accuracy rate is performed by Recurrent Neural Network (RNN) whereas samples count (N=10) and Convolutional Neural Network CNN where count of sample iteration values (N=10). The Recurrent Neural Network has demonstrated greater potential for increased accuracy value of 94.89% compared to the Convolutional Neural Network (CNN) at 93.33%, as determined by independent sample-t-test significance value the obtained p-value of 0.01 (p © 2023 IEEE.

关键词： Recurrent neural networks

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Node Mobility and Encounter Rate Metrics to Enhance Stability in MANET 2

Node Mobility and Encounter Rate Metrics to Enhance Stabilit...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajaram, Gnanajeyaraman Archana Vishveswari, R.S. Deepa, R. Packialatha, A. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Machine Learning Tamil Nadu Chennai India Varuvan Vadivelan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Dharmapuri India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Mobile Ad hoc Networks (MANETs) have been broadly functional in a wide variety of scenarios, for example, in disaster recovery, health care, video conferencing, and battlefield transmissions. MANET is an independent and peer-to-peer network which builds a chain of nodes to enable data transmission from sender to receiver. Hop count is measured as the most significant metric in MANET. Though several metrics have been introduced as the replacement for the hop count metric, there is a need for outperformance in mobile scenarios. The node mobility will affect the link stability, and it creates congestion. To solve these issues, a Node Mobility and Encounter Rate metrics (NMER) to enhance the stability of the network. It selects the route based on the node mobility and Encounter Rate (ER) metrics. The congestion ingredient measures the congestion level in the MANET. The objective of this route has the least cost for forward data packets and minimizes congestion. The NMER approach simulation results demonstrate that the NMER approach minimizes the packet loss ratio and reduces the network delay. © 2023 IEEE.

关键词： Mobile ad hoc networks

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Image Processing Based on Inter-frame Color Feature Matching for Visible Light Positioning 2

Image Processing Based on Inter-frame Color Feature Matching...

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Yu, Baozhu Liu, Xiangyu Chen, Yaoheng Guo, Lei Bao, Jianbo Sun, Jiandong Shenyang Ligong University School of Information Science and Engineering Shenyang China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing China Nari Group Corporation State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9798350368888

The indoor imaging visible light positioning (VLP) technology based on the image sensor (IS) utilizes existing indoor lighting infrastructure to provide high-precision indoor positioning services. However, due to the high complexity of traditional image processing algorithms, existing VLP systems are unable to provide real-time positioning services. In response to the above issue, we propose an image processing mechanism based on the inter-frame color feature matching method, which includes the equidistant sampling processing, low complexity decoding algorithm, and color feature matching-based LED region of interest (ROI) determination method. The results show that the system average positioning delay is 80.2ms, indicating better system real-time performance. © 2024 IEEE.

关键词： Color image processing

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