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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： S H Annie Silviya R Anitha P Gayathri N Hiranmaie R Harini D Hareene Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Machine Learning Rajalakshmi Engineering college Chennai India

This study introduces a transformative approach to primary programming education, leveraging Augmented Reality (AR) teaching methodologies through the innovative EnvisionAR platform. In response to the dynamic technological landscape, this research addresses the need for engaging, immersive learning experiences for young learners. The EnvisionAR platform, a pioneering fusion of AR technology and tailored curriculum, caters to the cognitive development stages of primary school students. By seamlessly integrating virtual elements into the physical environment, students engage in interactive, hands-on learning that cultivates creativity, problem solving skills, and a profound understanding of programming concepts. This research signifies a broader potential for AR integration in diverse educational disciplines.

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Grammar Error Correction Using Natural Language Processing

Grammar Error Correction Using Natural Language Processing

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Geetha, M. Vijaya, K. Subramanian, K.M. Rajesh Kanna, P. Rajeshkumar, G. Karthika, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Kongu Engineering College Department of Computer Science and Design Erode India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institue of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India

ISBN: (纸本)9798350313987

In the digital era, mos t of the communication is happening through online platforms. English plays a vital role for better understanding. Good grammar helps in framing a better sentence. This demands for the development of an automatic Grammar Error Correction (GEC) tool. Many research works have been initiated to make Grammar Error Correction possible by using various machine learning algorithms. Some of the algorithms are Naive Bayes, BERT, Support Vector machine, and ULMFIT. Among these algorithms BERT gives a comparatively best accuracy rate. The fundamental goal of this research work is to compare these algorithms with respect to accuracy Here, NLP is used for analyzing the grammatical corrections in the sentence. Further, this study can be extended to overcome the drawbacks faced by the BERT algorithm such as increase in time cons umption for training and cos t. © 2023 IEEE.

关键词： Bidirectional Encipher Representations from Instruments Grammar Error Correction Natural Language Processing Universal Language Model Fine-Tuning

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Deepfake Image and Video Detection Using Deep learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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Multi-Agent Systems for Collaborative intelligence (ICMSCI), International Conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks artificial intelligence Research and development

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Ensemble IDPS With Tripartite Firewall Solution for Enhancing Healthcare Data Security

Ensemble IDPS With Tripartite Firewall Solution for Enhancin...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Vishnu Kurnala Aniketh Reddy Adireddy Department of Computer Science and Engineering(AI&ML) Vardhaman College of Engineering Department of Artificial Intelligence and Machine Learning Vardhaman College of Engineering

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

An innovative security mechanism to thwart growing cyberattacks aimed at healthcare data is presented in this research. By use of sophisticated cryptographic techniques, the system combines a three-layer firewall with an ensemble Intrusion Detection and Protection System (IDPS). To minimize false positives and identify and counteract a range of cyberattacks, including advanced persistent threats and ransomware, it integrates signature-based, anomaly-based, and behavior-based detection techniques. Key healthcare data is better protected by the dynamic three-layer firewall, which adjusts to new threats. Tested extensively in simulated healthcare settings, the technology has shown to be highly effective in preserving data security and integrity. This framework is easily deployable across different healthcare IT infrastructures and offers a revolutionary solution to healthcare cybersecurity that tackles present and future security issues.

关键词： Firewalls (computing) Prevention and mitigation Intrusion detection Medical services Communications technology Ransomware Cryptography

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Localization for V2X communication with noisy distance measurement

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International Journal of Intelligent Networks 2023年第1期4卷 355-360页

作者： Javed, Iram Tang, Xianlun Saleem, Muhammad Asim Javed, Ashir Zia, Muhammad Azam Shoukat, Ijaz Ali School of Computer Science and Technology Chongqing University of Posts and Telecommunications China Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Aging Research Center Karolinska Institutet Stockholm171 65 Sweden Department of Computer Science University of Agriculture Faisalabad Pakistan Riphah College of Computing Riphah International University Faisalabad Campus Pakistan

Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47. © 2023 The Authors

关键词： Extended Kalman filters

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An Intelligent Deep learning Strategy for Breast Cancer prediction using feature ensemble learning

An Intelligent Deep Learning Strategy for Breast Cancer pred...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, artificial intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prakash, E.P. Satheshkumar, K. Anandhasilambarasan, D. Prasath, B. Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350305708

One in six women will be diagnosed with breast cancer (BC) by the time they reach their forties, making it the leading cause of death among this demographic. It's the leading killer in the world, yet it's hard to spot and diagnose in its early stages. Among women, BC is a foremost origin of death in third world countries. The prevention and treatment of these tumors can be greatly aided by screening and early diagnosis. At the moment, BC is the leading malignancy in females, even more so than ovarian cancer. When researchers get access to patient medical records, they can uncover hidden patterns in healthcare. The National-Cancer-Institute (NCI) reports that mortality rates from breast cancer can be reduced with early detection. In order to accurately identify patients with cancer, this work presents a voting classifier based on ensemble learning that integrates the logistic-regression(LR) &stochastic-gradient-descent(SGD) with deep-convoluted features. The ensemble voting classifier is fed deeply-convoluted features mined from the microscopic characteristics. This concept offers a refined framework for differentiating amid benign and malignant tumors. The results show that the highest level of classification accuracy, 100%, may be attained when utilizing voting classifiers with complex features. When compared to the traditional methods, the proposed method showed an improvement in accuracy. Thus, the research aids doctors in diagnosing and avoiding breast cancer. © 2023 IEEE.

关键词： breast-cancer classification deep-convoluted-features ensemble learning healthcare prediction

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Designing a Smart Healthcare framework based on disease prediction using Optimizer based AI 3

Designing a Smart Healthcare framework based on disease pred...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Vyas, Garvit Kalshetty, Jagadevi N Pareek, Piyush Kumar Birla Institute of Technology and Science Department of Electrical and Electronics Engineering Pilani - Hyderabad Campus India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350340358

In light of the rapid shift towards an ageing population and the corresponding rise in healthcare spending, disease prediction and control through various healthcare devices and services have lately garnered considerable interest. The development of smart healthcare services is a direct result of the advancements in the Internet of Medical Things (IoMT), wearable sensors, and communication technologies that have occurred in this technological era. The Internet of Things (IoT) in healthcare also helps bring down the cost of many different diseases by analysing data quickly and thoroughly. One exciting new area of medical research is the use of machine learning to make illness predictions from patient symptoms. In order to collect signals, the framework makes use of (IoT) sensors, which then transmit them to a web server in the cloud. In order to ascertain the patients' conditions, these signals are further analysed by a deep learning network. By taking both spatial and temporal considerations into account, the given method shortens the time it takes to discover anomalies by minimising the vanishing gradient problem of Recurrent Neural Networks (RNN). Someone in the medical field has access to patients' medical records and the results of any processing that may be necessary in the event of an emergency. Heart Failure Clinical Records, a repository at UCI, provided the dataset utilised in this investigation. It has thirteen characteristics. The suggested model outperformed competing deep learning and ML models in the experiments, which yielded an accuracy value of 0.9417. © 2023 IEEE.

关键词： Recurrent neural networks

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An Optimized Elliptical Curve Cryptography based Crypto-Watermarking Model in Smart Healthcare Application 2

An Optimized Elliptical Curve Cryptography based Crypto-Wate...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Murthy, G. N. Keshava Manjula, G. Harshitha, P. Achyutha Prasad, N. Siddaganga Institute Of Technology Department Of Electronics And Instrumentation Engineering Karnataka Tumakuru India Bgs College Of Engineering And Technology Department Of Cse Bangalore India Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

Telemedicine uses current communication and information technology to provide clinical treatment remotely. Given the ubiquitous usage of DWT and DCT are used for watermarking, ECC-FFA is used for encryption. The only cryptographic algorithms used in the current approaches for watermarking are AES, DES, and RSA, and the DCT and DWT are typically used separately. But because we integrated the ECC-FFA cryptographic method with the DWT and DCT watermarking technologies, the recommended solution is more effective than earlier ones.. A strong and lossless patient medical information exchange system employing crypto-watermarking is suggested in order to secure medical data. For the watermarking approximate, the discrete wavelet alter (DWT) and discrete cosine alter (DCT) are combined, and the elliptical curve cryptography (ECC) algorithm is utilized. At the same time, metaheuristic algorithms may be used to solve the optimum key creation issue in cryptography, which is viewed as an optimisation challenge. As a stem, the master nucleus of this masterpiece is on the development of cryptographic encryption for picture security based on metaheuristic optimization algorithms. According to this perspective, this study creates the ECC-FFA model, an effective produce fruit fly optimisation (FFO) technique with ECC for picture protection. The suggested model uses the ECC technique to carry out the encryption and decryption procedure. Various medical photos were used in the experiment, and the peak signal to noise ratio was used to assess how well the suggested method worked. © 2024 IEEE.

关键词： Optimization

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Brain tumor Segmentation and Classification using SMA based Modified HBTNet Model from MRI images 3

Brain tumor Segmentation and Classification using SMA based ...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Vyas, Garvit Kalshetty, Jagadevi N Pareek, Piyush Kumar Birla Institute of Technology and Science Pilani - Hyderabad Campus Department of Electrical and Electronics Engineering India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350340358

When it comes to diagnosing and treating brain tumors, the ability to segregate them using multimodal MRI is crucial. Segmenting brain tumors relies heavily on multimodal information. The majority of current approaches, however, disregard some aspects that are particularly relevant to the segmentation problem in favor of extracting and selecting deep semantic features. This study suggests employing structural multimodal magnetic resonance imaging (mMRI) for brain tumor segmentation and subtype classification using context aware deep learning. The paper begins by suggesting a deep learning process for tumor segmentation, one that takes into account the ambiguity of tumor localization in the subregions of radiology mMRI images. The research then uses the tumor segments to classify the tumor subtype using the Human Brain Tumor Network (HBTNet). By using the Slime Module Optimization Algorithm (SMOA), the HBTNet's learning rate is optimized. This work applies the suggested approaches to the Multimodal Brain Tumor Segmentation Challenge 2018 (BraTS 2018) dataset for tumor segmentation and classification in order to assess their performance. Utilizing several measures for segmentation and classification, the study is confirmed. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Brain Tumor Detection Using VGG 16 & Efficient Net B3 Model 10

Brain Tumor Detection Using VGG 16 & Efficient Net B3 Model

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Menagadevi, M. Sharmila, S. Thiyagarajan, D. Madian, Nirmala Devaraj, Somasundaram School of Engineering Malla Reddy University Department of Computer Science Engineering Hyderabad India Dr.N.G.P Institute of Technology Department of Biomedical Engineering Coimbatore India School of Engineering Malla Reddy University Department of Artificial intelligence and Machine Learning Hyderabad India Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350384369

A brain tumor is a growth of cells that begins in the brain and develops uncontrollably and unnaturally. Benign, malignant, and pituitary tumors are the different types of brain tumors. The life expectancy of the patients should be increased by appropriate care, careful planning and precise diagnostics. The above knowledge will help in early and accurate Detection of tumor. Magnetic Resonance Imaging (MRI) is generally utilized for diagnosing brain tumors. MRI examination is performed based on injecting contrast agents for enhancing the visibility of tumor. For recognition of brain tumors, machine learning and deep learning algorithms are performed to predictthe brain tumor with high accuracy. In this work modified EfficientN etB3 and VGG16 is proposed to detect the type and location of tumor. Proposed model gives higher prediction accuracy for different types of tumors. VGG 16 gives training accuracy of 95.1 %, testing accuracy of 94.3 % and validation accuracy of 94%. EfficientNetB3 training accuracy of 98.8%, testing accuracy of 98.4% and validation accuracy of 97.8%. EfficientN etB3 have better accuracy for tumor prediction and classification. © 2024 IEEE.

关键词： Deep learning

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