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Brain computer Interface Imagination based Thought Analyser Framwork to Generate and Classify Real Time Image Retrieval System for Space Images in Chandrayaan 3 Lunar Misson 10

Brain Computer Interface Imagination based Thought Analyser ...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Sinha, Anurag Suman Alkhayyat, Ahmed Kumar, Biresh Kumari, Anita Kumar, Uttam School of Computing and Information Sciences Ignou New Delhi India Iitm Department of Computer Science India College of Technical Engineering The Islamic University Najaf Iraq Amity Institute of Information Technology Amity University Jharkhand Ranchi India Rkdf University Ranchi Department of English Literature India Amity School of Business Amity University Jharkhand India

ISBN: (纸本)9798350382471

We provide a ground-breaking architecture for the Chandrayaan 3 Lunar Mission that seamlessly combines Brain-computer Interface (BCI) technology with sophisticated real-time picture retrieval and classification algorithms, encouraging a paradigm change in lunar exploration. By giving mission personnel the ability to express their picture search criteria verbally rather than through manual search queries, this ground-breaking framework makes the most of the creative capacity of the human mind. This technology converts cognitive intent into executable commands by combining BCI with state-of-the-art machine learning methods, speeding up picture retrieval and reducing operator cognitive load. In parallel, a cutting-edge picture retrieval and classification system powered by computer vision and machine learning sorts through the massive collection of lunar images gathered by Chandrayaan 3 in order to find specific objects. This technique effectively classifies photos according to geological qualities, mineral composition, and topographical features, ensuring the quick identification of scientifically important data. Our system streamlines operations, decreases human error, and improves data quality by automating these procedures. As a result, lunar research is elevated to new levels, allowing for ground-breaking discoveries and a deeper knowledge of the Moon's mysterious geography and past. © 2023 IEEE.

关键词： Brain-computer Interface Chandrayaan 3 Lunar Mission Image Retrieval Imagination Thought Analyzer

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Leveraging XGBoost for Predictive Analytics in Healthcare: Enhancing Disease Diagnosis 7

Leveraging XGBoost for Predictive Analytics in Healthcare: E...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Kotiyal, Arnav Habelalmateen, Mohammed I. Rana, Ajay Devi, V.S Anusuya Rao, Bolleddu Devananda Bansal, Saloni Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Amity University Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Departyment of Computer Engineering and Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350350067

In this study, we look at how XGBoost (Extreme Gradient Boosting) may be used in healthcare predictive analytics to improve the speed and precision of illness detection. Thanks to its built-in regularisation algorithms that avoid overfitting and its capacity to handle big datasets, XGBoost has proved useful in a broad variety of prediction applications. It is a scalable and high-performance machine learning algorithm. In order to improve patient outcomes, healthcare providers must ensure that patients are diagnosed with chronic and life-threatening conditions including cancer, heart disease, and diabetes as soon as possible. To test how well XGBoost performs in comparison to more conventional machine learning techniques, the research makes use of healthcare datasets collected from the actual world. The effectiveness of the model in predicting illness outcomes is evaluated using critical metrics including accuracy, precision, recall, and F1-score. Based on the findings, XGBoost is the best classifier for real-time healthcare applications because to its quicker processing efficiency and greater predictive ability. Additionally, the article delves into the model's interpretability and how it may be integrated into clinical decision support systems. By demonstrating how XGBoost may improve diagnostic accuracy and bolster individualised treatment plans, this study adds to the expanding area of AI-driven healthcare. © 2024 IEEE.

关键词： Predictive analytics

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From Non-Expert to Expert: Recurrent Refined Learning for Medical Image Segmentation

From Non-Expert to Expert: Recurrent Refined Learning for Me...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Ruohui Li, Changtai Ban, Xiaojuan Yin, Shihua Yao, Chao Guo, Yu Obaidat, Mohammad S. University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Beijing China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing China Liaoning Academy of Materials Institute of Materials Intelligent Technology Shenyang China Second Affiliated Hospital Guangxi Medical University Nanning China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China University of Jordan King Abdullah Ii School of Information Technology Amman Jordan Amity University UP Noida India

ISBN: (纸本)9798350386226

Precise lesion segmentation is essential in computer-aided diagnosis and treatment. Prevalent deep-learning-based approaches need high-quality annotations to achieve satisfying performance. However, blurring effects and infiltration hamper the accurate delineation of lesions. A common solution is to divide the annotation process into initial annotation by non-specialized personnel and subsequent modification by expert physicians, where the latent correction patterns and non-expert labels are seldom utilized effectively. To explore their helpfulness, we propose ReReNet, a recurrent refined network for lesion segmentation that learns from non-expert to expert. It achieves progressively refined segmentation results through multiple iterations with tailored discrepancy-aware supervision. During training, as the iterative process perceives discrepancies between refining and expert labels, the model gradually grasps the knowledge of turning the barely correct into the clinically accurate. We validate ReReNet's capability on three medical image segmentation (MIS) datasets, including magnetic resonance (MR) and computed tomography (CT) modalities. Comparison results indicate that the proposed approach achieves superior performance by introducing the designed recurrent mechanism and outperforms mainstream methods, demonstrating the effectiveness of mining hidden correction patterns by utilizing non-expert information. © 2024 IEEE.

关键词： Image segmentation

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Handwritten Text Recognition Using Deep Learning:A Comprehensive Review

SSRN

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SSRN 2023年

作者： Khan, Mohd Ayan Arif, Hamza Singh, Vishal Sharma, Dolly Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida201313 India

Deep learning techniques and methods have shown excellent development in the field of offline/online handwritten text recognition(OHTR) in recent times. There are many reviews in the realm of OHTR, but none of them provides a collective and comprehensivereview of these many diverse technologies and methods. This review paper provides a profound and comprehensive scientific reviewof various techniques, models, algorithms, and architectures in the field of OHTR while highlighting the advancements that havebeen made in this area of deep learning. In this review, native Convolutional Neural Networks (CNN), hybrid models, and machinelearning classifiers have been noticed to have high potential and have shown high recognition accuracy in many languages andscripts. This review is divided into four sections: Traditional and Hybrid models;New Approaches based on Neural Networks, NewApproaches based on Neural Networks;Image Enhancement, Feature Extraction and Noise Reduction;Innovative Approaches andNovel Techniques where each category comprises of a set of advancement with a respective advancement and problems in *** also lists the advantages and applications of some of the models. This review certainly gives the overall idea of various optionsavailable for OHTR, and it helps to understand the fundamental difference between different approaches and can help to choose thebest if one is looking to use the concept of transfer learning. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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The smart Fuzzy based Image Recognition Model for Modern Healthcare

The smart Fuzzy based Image Recognition Model for Modern Hea...

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Disruptive Technologies (ICDT), International Conference on

作者： Rama Shankar Yadav Archana Singh Satya Prakash Yadav Amity School of Engineering and Technology Amity University Greater Noida Campus Uttar Pradesh Amity school of engineering technology Amity University Noida UP Department of Computer Science and Engineering G.L. Bajaj Institute of technology and Management (GLBITM) Affiliated to AKTU Greater Noida India

It is a generation that combines superior photo reputation strategies with fuzzy good judgment algorithms to as it should be discovered and analyze scientific photos. This model can improve scientific photograph diagnosis and treatment planning efficiency and accuracy, thereby revolutionizing current health care. The version uses fuzzy common sense, a mathematical and computational technique to handle uncertainty and imprecision, to manner and examine clinical pictures. This allows for a more flexible and robust identity and type of abnormalities and illnesses in clinical snapshots. With the help of this version, healthcare experts can quickly and appropriately identify the presence of diseases or situations, leading to progressed analysis and treatment choices.

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Detection Of Criminal Activities/Criminal Through CCTV By Live Footage Analysis

Detection Of Criminal Activities/Criminal Through CCTV By Li...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Rahul Kumar Divyajeet Singh Rajpurohit Mushraf Hanief Shilpi Sharma Tanupriya Choudhury Ayan Sar Department of Computer Science and Engineering Amity School of Engineering and Technology NOIDA Uttar Pradesh India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The integration of deep learning techniques, particularly the You Only Look Once (YOLO) object detection model, with Closed-Circuit Television (CCTV) systems has significantly transformed the landscape of crime detection and prevention. This research paper provides a comprehensive analysis of YOLO-based CCTV crime detection, elucidating the innovative strategies, challenges, and potential implications for enhancing public safety. The YOLO framework is well-known for its real-time object detection capabilities, which enable it to swiftly and accurately identify multiple objects within a single image frame. In the context of CCTV surveillance, YOLO has proven invaluable in the identification and tracking of criminal activities. The paper explores how YOLO's object detection architecture is adapted to the unique challenges of CCTV crime detection, including variable lighting conditions, occlusions, and diverse camera angles. One of the fundamental contributions of this research is the development of a YOLO-based CCTV crime detection system. This system combines YOLO's speed and accuracy with a sophisticated backend for video analysis and real-time alerting. The system not only detects suspicious activities but also classifies them into different categories, such as theft, vandalism, or violent behavior, thus facilitating immediate responses from law enforcement agencies. Moreover, the paper delves into the application of deep learning in data pre-processing, noise reduction, and the handling of extensive video feeds. Techniques such as transfer learning and data augmentation are explored to enhance the model's generalization and adaptability to different surveillance environments. The scalability of the system is also discussed, allowing it to be deployed in urban centers, commercial spaces, and residential areas. While the advantages of YOLO-based CCTV crime detection are evident, this research also highlights potential challenges, including privacy concerns, ethical

关键词： YOLO Deep learning Privacy Ethics Surveillance Streaming media Real-time systems

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Intelligent Farming using Energy Efficient Routing Protocol with Efficient Transmission in Agriculture

Intelligent Farming using Energy Efficient Routing Protocol ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Ashutosh Kumar Rao Kapil Kumar Nagwanshi Manoj Kumar Shukla Shobha Aswal Computer Science & Engineering Amity School of Engineering & Technology Amity University Rajasthan Computer Science &Engineering Guru Ghasidas Viswavidalaya Raipur Computer Science &Engineering Amity University Uttar Pradesh School of Computing Graphic Era Hill University Dehradun Uttarakhand India

Wireless sensor networks (WSNs) were initially developed for research and development purposes, and are now widely employed in a variety of domains such as communication, weather prediction, agriculture, industries, smart physical condition monitoring, and surveillance. In the agriculture sector, in order to increase yields, the IoT-enabled WSN is extensively used to monitor the environment and climatic conditions by employing a variety of sensors to sense data. These types of sensors are fully deployed in the required agricultural environment to improve production yield through intelligent farming and decision making, to find better and necessary information about plants, such as temperature, humidity, and irrigation patterns. In India, Agriculture gives employment to almost 70% of the population and generates 27 % of the GDP (Gross domestic production). This makes agriculture an important area for research and development This research study is completely based on the surveying of different methods, which are completely based on Internet of Things (IoT) for leveraging better results.

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A Blockchain-assisted Authentication Protocol for RFID-enabled Supply Chain Management System

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IEEE Transactions on Network science and engineering 2025年

作者： Tariq, Tayyaba Obaidat, Mohammad S. Kuo, Wen-Chung Mahmood, Khalid Ayub, Muhammad Faizan Alenazi, Mohammed J.F National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Riyadh11451 Saudi Arabia

In the dynamic domain of supply chain management, integrating Radio-Frequency Identification (RFID) technology with blockchain technology represents a significant leap forward. This integration has created a blockchain-assisted RFID-enabled Supply Chain Management System (SCMS). With its ability to utilize electromagnetic fields to identify and track tags attached to objects, RFID technology has revolutionized product management and tracking within supply chains. (SCMS) transmits tracking and product management information through public communication channels. However, (SCMS)'s communication on these channels is vulnerable to various security attacks and privacy challenges. Numerous authentication protocols have recently been proposed to tackle these security and privacy challenges. Unfortunately, most protocols are susceptible to different security attacks, such as tag or reader impersonation, denial of service, physical cloning, desynchronization attacks, etc. Therefore, we have proposed an authentication protocol for an RFID-enabled (SCMS) that also leverages blockchain technology. The integration of blockchain technology ensures data integrity, immutability and transparency across each department involved in (SCMS). In the proposed protocol, we also employ a Physically Unclonable Function (PUF) to secure (SCMS) against physical cloning attacks. We validate the security of the proposed protocol through both informal and formal analysis. The informal analysis confirms that our protocol significantly enhances security and efficiency. Moreover, a performance analysis of the proposed protocol against various competing existing protocols shows its superior performance. Notably, the proposed protocol substantially reduces computational and communication costs by 24.38% and 8.03%, respectively, which underscores its enhanced performance and resource efficiency. © 2013 IEEE.

关键词： Efficiency

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Fake News Detection and Classification Using Deep Learning Techniques for Social Media Text 6th

Fake News Detection and Classification Using Deep Learning T...

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6th International Conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Kumari Shalini, A. Saxena, Sameer Suresh Kumar, B. AMITY Institute of Information Technology Amity University Rajasthan Jaipur India School of Computer Science and Engineering Sanjay Ghodawat University Kolhapur India

ISBN: (纸本)9783031840616

The rise of social media networks has become a critical component of modern human living due to recent developments and advances in computer technology. The present context has evolved into a widely recognized medium for the distribution of information and news pertaining to a wide range of topics, including daily reports. This timeframe serves as the main period for data collecting and information transmission. This environment has a multitude of advantages;yet, it also entails a significant amount of misinformation and material that may potentially perplex both readers and users seeking accurate information. A significant concern associated with this system is to the limited availability of reliable information and real-world news pertaining to social media. An integrated system has been built that leverages several components of chain type of technology and natural language processing (NLP) to use machine learning techniques for the purpose of detecting false news and enhancing the accuracy of identifying fraudulent user accounts and postings. Various methodologies have been used to extract and choose features in order to provide effective training rules and appropriately assess the test classifier. A comprehensive empirical investigation demonstrates that the Twitter dataset is a useful resource for assessing the precision of fake news detection in classification tasks. The deep learning based RNN system demonstrates a high accuracy rate of 98.80% throughout both training and testing phases. This accuracy surpasses that attained by traditional machine learning methods such as SVM, ANN, and NB. Furthermore, the proposed methodology has the capability to detect superfluous information, a phenomenon that may be corroborated via several sources or by referencing current events. The prime purpose of this approach is to enhance the eminence of the material, identify any problematic sections within the text, and generate the original versions of those sections, all whi

关键词： Tweets

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M-VMT-PC: Metaverse-Enabled Virtual Medical Training and Patient Care Framework Underlying 5G

M-VMT-PC: Metaverse-Enabled Virtual Medical Training and Pat...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Kumari, Aparna Obaidat, Mohammad Panchal, Shikhar Raval, Nikhil Kumar, Prasun Kumar Yadav, Dileep Nirma University Department of CSE Ahmedabad India University of Science and Technology Beijing China School of Computing SRM University Department of Computational Intelligence SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Nirma University Department of Computer Science and Engineering Ahmedabad India SSET Sharda University Department of Biotechnology Greater Noida India Bennett University Department of Computer Science and Engineering SCSET Greater Noida India

ISBN: (纸本)9798350349832

As technology advances, merging virtual environments, blockchain, and 5G networks opens new possibilities, especially in medical training. This paper introduces a framework that combines these technologies to create an innovative medical training system. In this paper, we proposes Metaverse-enabled Virtual Medical Training and Patient Care framework, i.e., M-VMT-PC. In the proposed framework, metaverse comprises a dig-ital space where users can interact in 3D, offers exciting opportunities for medical training. It allows for realistic and interactive simulations, where trainees can practice medical scenarios and procedures in a virtual setting. More, the M-VMT-PC uses blockchain technology within this framework to ensure that the data is securely stored, enhancing its reliability. Next, 5G technology is used to make this framework effective. With ultra-reliable, low-latency communication (uRLLC) and high-speed data transfer (eMBB), 5G supports real-Time interactions and smoothly handles large volumes of data. This technology ensures the training environment is responsive and can handle extensive data needs, essential for ongoing updates and improvements. Our proposed framework M-VMT-PC addresses the current challenges in medical training by providing a scalable, secure, and engaging learning environment. It improves the medical professionals training and sets the stage for future advancements in medical education and patient care. © 2024 IEEE.

关键词： Virtual environments

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