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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sreevani, M. Latha, R. St.Peter's Institute of Higher Education and Research Department of Computer Science and Applications Chennai600054 India

ISBN: (纸本)9798350370249

The early and remote identification of chronic diseases is now achievable for the first time. In this study, mammography images from the Cancer Imaging Archive (TCIA) were used to build a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The enhanced medical treatment supplied by the smart healthcare system can benefit modern e-healthcare. Accurate diagnoses based on Internet of Things-based medical images are required for early detection and effective treatment of breast cancer. However, noise in these images can significantly reduce the precision of the diagnostic process. In this paper, we propose a new Triplet Attention Model (TAM) to improve the accuracy of breast cancer diagnosis from noisy medical images. The TAM use attention mechanisms and triplet loss to learn distinguishing features from noisy images. The triplet loss ensures that related images are closer together in the learned feature space, and the attention mechanism assists the model in focusing on those areas. As a result, categorization accuracy improves. Extensive tests are run on a large and diverse dataset of noisy breast medical images, and the results are compared to state-of-the-art approaches. As demonstrated in our trials, our proposed Triplet Attention Model surpasses earlier techniques in a number of significant ways, including its capacity to accurately classify noisy breast medical images for cancer diagnosis. © 2024 IEEE.

关键词： Breast cancer Deeplearning Image classification Triplet Attention Model

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Machine Learning Approach for Anomaly-Based Intrusion Detection Systems Using Isolation Forest Model and Support Vector Machine 5

Machine Learning Approach for Anomaly-Based Intrusion Detect...

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5th International Conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Shanthi, K. Maruthi, R. Ponnaiyah Ramajayam Institute of Science and Technology Department of Computer Science Tamil Nadu Thanjavur India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350321425

Cyber Security plays a significant role in almost all the applications in the networks including host protection, network protection and cloud infrastructure protection. Designing an approach to intrusion detection Systems (IDS) in all levels is challenging and it requires more complex algorithms in today's world. Mainly it is very difficult to find abnormal behaviour in the cyber world because of the large amount of data. A precise and robust method for IDS has to be designed to overcome this issue to effectively secure the functioning of cloud Infrastructure. This study analyses the anomaly based intrusion detection Systems (AIDS) using Machine learning (ML) models and suggests an effective model for anomaly detection to deal with the challenges in handling complex data. © 2023 IEEE.

关键词： Intrusion detection

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Hybrid K-Means Clustering for Training Special Children using Utility Pattern Mining

Hybrid K-Means Clustering for Training Special Children usin...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Jeyachidra, J. Logesh, T. Nandhini, K. Krithiga, R. Department of Computer Science and Applications Periyar Maniammai Institute of Science and Technology Thanjavur Vallam India Department of Computer Science Indira Gandhi College of Arts and Science Kathirkamam Puducherry605009 India

ISBN: (纸本)9798350334357

In this Hybrid K-Means Clustering for Grouping research work, the k-means methodology is a well-known process for grouping things together. Most of the time, this algorithm sorts the objects into a set number of clusters, but in this case, the user gives the number k. At first, it picks cluster centres at random and measures how far apart k points are. This kind of cluster centre is called k centroids, and it will keep changing until there are no more changes. When making applications that use machine intelligence, a machine should be able to think like a person and make the right choices. In this case, it's not possible to get k-points from the user. So, the Genetic Algorithm (GA) is used to search with heuristics to find the initial cluster centres. The goal of this research work is to look at how k-means clustering with GA can be used to optimise. The performance evaluation of hybrid k means method illustrates the precision and accuracy of the selected clustering methods. The result states that precision was 78.35% and its accuracy was 72.67% found while using the approach. The ROC curve analysis is performed with sensitivity and specificity data. The result states that the area under curve of the approach is 84.0%. © 2023 IEEE.

关键词： Genetic algorithms

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Virtual Reality: Emerging Technologies and Future Directions 15

Virtual Reality: Emerging Technologies and Future Directions

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kale, Prachi Shyamrao Deshmukh, Tejaswini Kiran Bhoite, Sachin Department of Computer Science and Applications Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune India

ISBN: (纸本)9798331300579

Recent advancements in technology, notably immersive virtual reality (IVR) and the Meta-verse, are indicating novel trajectories for the future. The integration of animation and multimedia into learning has evolved with the provision of complete Virtual Reality Learning Environments. This shift underscores a transformation in Web-based education, moving away from traditional multimedia towards a more immersive, interactive, and intuitive VR learning landscape. VR, utilizing 3D models, emulates real-world scenarios, re-discussion on effective pedagogy and leveraging technological innovations is reignited. This paper introduces the application of Web-based 3D technologies with a focus on VR features, identifying forthcoming trends aimed at enhancing immersive experiences, augmenting interactivity, and exploring additional pedagogical value and application contexts. Additionally, it emphasizes the importance of anticipating future emerging trends and applications of VR in education, underscoring its potential to revolutionize learning experiences. The conclusion presents guidelines for the efficient utilization of VR, addressing limitations and discussing implications for future research in VR education. © Grenze Scientific Society, 2024.

关键词： Virtual environments

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Advanced Intrusion Detection Using Multilayer Perceptron Neural Networks Performance Benchmarking and Validation 1

Advanced Intrusion Detection Using Multilayer Perceptron Neu...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Saranya, R. Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350367720

Due to their reliance on signature-based techniques, which lead to high false positive rates and limited flexibility to changing threats, traditional intrusion detection systems (IDS) are inadequate and represent a persistent challenge in cybersecurity. The paper proposes an advanced intrusion detection method that uses Multilayer Perceptron Neural Networks (MLPNNs) to overcome these drawbacks. The technique reduces false positives and improves detection accuracy by combining advanced feature extraction techniques with deep learning algorithms. The proposed system outperforms existing systems in terms of performance metrics due to careful data collecting, preprocessing, model architecture design, and validation. The system's effectiveness in precisely recognizing intrusions while lowering false alarms is demonstrated by the results, which show a detection accuracy of 98.5%, a false positive rate of 2.1%, and an AUC of 0.975. The proposed system also shows reduced durations for training and validation in addition to a more thorough evaluation methodology, confirming its dependability and efficacy in enhancing network security against changing cyber threats. © 2024 IEEE.

关键词： Network intrusion

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Dynamic Hand Gesture Recognition and Real-Time Utterance Generation for Virtual Assistants Using Machine Learning

Dynamic Hand Gesture Recognition and Real-Time Utterance Gen...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kavitha, M. Rajakumari, S. Brintha Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

A novel virtual assistant machine learning approach based on the study is known as dynamic hand gesture recognition using real-time vocal generation. This single method solution for the same uses a deep learning model CNN, which actually recognizes the real-time hands and maps that to recognize the real-time vocal generator with the training of models that produce responses that are pertinent to context by using this gesture data. This system attempts to allow for the creation of logical, spoken responses through natural, instinctive communications by decoding gestures such as volume control requests, play, and stop. Accuracy was improved in gesture recognition as well as the latency of generating a response in order to satisfy the requirement of real-time interaction with a system latency of 100 ms and a high rate of 94% for the identification of gestures. In a nutshell, it has been proved that the method proposed reaches the goal of communication simplification and diversification for virtual assistants and it also shows how gesture-based interaction might replace text or voice commands in specific contexts. © 2024 IEEE.

关键词： Gesture recognition

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Human Movement Recognition with Machine Learning Techniques

Human Movement Recognition with Machine Learning Techniques

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2024 International Conference on Advances in Computing Research on science Engineering and Technology, ACROSET 2024

作者： Sinha, Raj Sinha, Pankaj Chandra Tiwari, Meena School of Computer Applications Lovely Professional University Phagwara India Jayoti Vidyapeeth Women's University Department of Computer Science Jaipur India Srist College Department of Cse Jabalpur India

ISBN: (纸本)9798350388800

Human MR (HMR) use of IAs has generated much attention on their applications on various campuses, including health services, deportations, security, and security situations. This research focuses on creating powerful computations and models for data mining and collecting accurate human information as a source of information obtained from sensors, accelerometers, sensors, and wearable devices. Artificial intelligence techniques including deep learning, support vector machines (SVMs), and irregular plots are used to process and analyze sensor information, extraction, and meaningful aspects to separate the results, such as a fireplace, adjustment, senders, parse, and movements compels. The goal is to create HMR brands trusted for preparation to realize accurate information about constant and precise actions, carry out mass medical surveillance, provide Bienstar personalized customization, and create the highest association between people and computers in an intelligent environment © 2024 IEEE.

关键词： Accelerometers

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Model-based recognition in robot vision for monitoring built environments

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Multimedia Tools and applications 2025年第12期84卷 10137-10154页

作者： Khan, Asif Varish, Naushad Pandey, Dhirendra Rizvi, Syed Qasim Afser Mehrotra, Shashi Parveen, Nikhat Department of Computer Applications Integral University Dasauli Bas-ha Kursi Road Uttar Pradesh Lucknow226026 India Hyderabad Campus Telangana Hyderabad502329 India Department of Information Technology Babasaheb Bhimrao Ambedkar University Lucknow Lucknow India Guangdong Guangzhou510006 China Department of Computer Science and Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad244001 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Gunur522302 India

In contrast to the machine, people have strong perception to determine the multiple objects easily in various environments under certain conditions. It is evident that the objects may exist in different orientations, scales and/or shapes, however it does not affect our ability to correct object recognition accuracy. The computer vision based object recognition is one of the most tedious tasks. So, objective is to design and development of the robust object recognition system to recognize the multiple objects effectively since different variations in the natural images are existed. In this paper, authors have presented comparative analysis and studies of some state of art object-recognition models for robot vision based problems, where the main goal is to recognize position, orientations and the identity of objects/parts, where parts have been taken from the industry. In the dynamic environment problem, the parts are recognized in a complex scenario. This paper presented based on the object representations using the recognition algorithms. Three steps are discussed and examined in details which are common to each and every category, namely, feature extraction, modeling, and matching. Test results confirmed that proposed model-based recognition in robot vision is fast and provides 93.00% accuracy. The proposed algorithm for object-recognition model has been compared with existing industrial part-recognition models and has, provided insights for progress toward future robot vision systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object recognition

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IoT-Based Smart Mask Protection against the Waves of COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11153-11164页

作者： Goar, Vishal Sharma, Aditi Yadav, Nagendra Singh Chowdhury, Subrata Hu, Yu-Chen Govt. Engineering College Bikaner Rajasthan Bikaner India Department of Computer Science and Engineering Parul Institute of Technology Parul University Gujarat Vadodara India Department of Computer Science & Applications SVCET Engineering College Andra Pradesh Chittoor India Department of Computer Science and Information Management Providence University 200 Sec. 7 Boulevard Shalu Dist Taichung City43301 Taiwan

In the year 2020, the word "pandemic" has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a "Pandemic". The outbreak pattern differs widely across the globe based on the findings discovered so far;however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images;the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology;therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytic

关键词： COVID-19

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Blockchain-Enabled Secure and Fair Online Lottery Systems Using Smart Contracts

Blockchain-Enabled Secure and Fair Online Lottery Systems Us...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Datta, Suseta Mondal, Rituparna Roy, Rajdeep Banerjee, Sourav Biswas, Utpal Narula Institute of Technology Department of Computer Application West Bengal Kolkata India Techno India University Department of Computer Applications West Bengal Kolkata India University of Kalyani Department of Computer Science and Engineering West Bengal Kalyani India Kalyani Government Engineering College Department of Computer Science and Engineering West Bengal Kalyani India

ISBN: (纸本)9798331540821

In the capricious digital dominion, conventional online lottery systems come across consequential impediment regarding transparency, fairness, and security. This paper presents an inventive solution by merging Blockchain technology and smart contracts into online lotteries to take forward these challenges. The preferred disperse implementation capitalizes on Blockchain's decentralized category to covenant the translucency and immutability of lottery transactions, thereby undoing the requisite for trusted interceders. Smart contracts are immersed to automate the entire lottery process-from ticket provisioning to winner selection and payout-certifying agreement to preconceived rules and reinforcing contender trust. Through meticulous perusal and enactment, illustration of how Blockchain technology can transform online lottery systems, providing users with a secure, transparent, and fair platform have been performed. Furthermore, the suggested system's resistance to recurrent deceit and its prospective impression on the online gambling industry are talked about, foregrounding the welfares of a Blockchain-drove technique to online lotteries. © 2024 IEEE.

关键词： Smart contract

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