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Anomaly Detection in Medical Images Using Deep Reinforcement Learning 2

Anomaly Detection in Medical Images Using Deep Reinforcement...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rastogi, Divya Sharma, Aparna Yadav, R.K. Varshney, Neeraj Saraswat, Laxmi Computer Science and Application Sharda University UP Greater Noida India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

Anomaly detection in clinical pix is vital to identify and diagnose diseases. Deep reinforcement gaining knowledge is a practical gadget-mastering approach implemented for more than a few medical obligations. This technique applies a synthetic intelligence version to research by revealing an iterative procedure to discover a set of top-of-the-line parameters for ultimate selection-making. The cause of this examination is to use deep reinforcement mastering for anomaly detection in medical pix. This approach is used to identify strange areas in clinical pix, including tumors, mental diseases, and other abnormalities. Deep reinforcement mastering is mixed with Convolutional Neural Networks (CNNs) to extract capabilities from the medical pictures and then classify them consistent with their deviation from normal. The detection accuracy is evaluated using a novel assessment metric, referred to as the precision-recollect (PR) curve, which is designed to degree the version's accuracy. The results show that deep reinforcement learning can gain high-performance anomaly detection accuracy compared with conventional methods. © 2024 IEEE.

关键词： Accuracy Anomalous Evaluated Evaluation Measure

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A Comprehensive Analysis of Cloud Computing in Wireless Communications 2

A Comprehensive Analysis of Cloud Computing in Wireless Comm...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Bansal, Anurag Sharma, Surbhi Shukla, Ajay Narayan Badhoutiya, Arti Research and Technology 13D Research & Strategy India Abes Engineering College Department of Computer Applications UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Gla University Department of Electrical Engineering UP Mathura India

ISBN: (纸本)9798350371055

In the present-day digital landscape, cloud computing has grown to be a ubiquitous generation that offers numerous advantages to numerous businesses. Its software in wi-fi communications has had a mainly revolutionary effect, ultimately converting how data is transferred, considered, and saved. This looks at a thorough examination of how cloud computing has affected wireless communications, including its records, most important ideas, and programs. The primary phase of the object appears to be how cloud computing has changed over the years, moving from conventional on-premise computing to its current country, which includes a spread of service offerings and deployment methodologies. Subsequently, it explores the fundamental thoughts of cloud computing, which have virtualization, scalability, and on-demand provisioning, and talks about how these apply to wireless communications. The ability of cloud computing to grow network availability and dependability is one of its primary benefits for wi-fi communications. Using cloud-based computing and garage sources, network operators may additionally lower downtime and increase uptime for their services. They can also better manage unforeseen surges in community visitors and provide a higher user revel. The look additionally addresses how cloud computing helps the short rollout of the latest services and packages within the wireless communications industry. © 2024 IEEE.

关键词： Cloud Computing Data Storage Mobile Technology Network Security Wireless Communications

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Load Balancing in Cloud Computing using Mutative BFO

Load Balancing in Cloud Computing using Mutative BFO

引用

2022 International Conference on Edge Computing and applications, ICECAA 2022

作者： Singhal, Saurabh Mangal, Deepak Department of Computer Engineering & Applications GLA University Mathura India Department of CS & IT Jain University Bengaluru India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

On a per-user basis, cloud computing provides its end customers with increasingly scalable virtual resources over the Internet. Numerous users and businesses have already moved to the cloud for accessing the services that are offered by a variety of cloud service providers. To handle so many requests and clients, cloud providers must manage their servers. As a result, service providers must manage the load to guarantee that resources are being used effectively. The problem of load balancing is known to be NP-hard, and a number of researchers have suggested a variety of solutions using meta-heuristic algorithms. The paper provides a mutative bacterial Foraging optimization to reduce response time and improve resource utilization. In a heterogeneous cloud setup with dynamic workloads, the proposed algorithm performs better when compared to the other meta-heuristic algorithm. © 2022 IEEE.

关键词： Cloud computing

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A Unified Framework for Recommend Medicine Review Categories Through Bi-LSTM and Supervised Learning

A Unified Framework for Recommend Medicine Review Categories...

引用

Expert Clouds and applications (ICOECA), International Conference on

作者： Anuj Kumar Shashi Shekhar Department of Computer Engineering & Applications GLA University Mathura Uttar Pradesh India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

The number of users and daily tweets on Twitter are significant. The medicine review categories system is designed to leverage the vast amount of patient-generated data to create a reliable and effective medication recommendation framework. The proposed system employs Deep Learning and Machine Learning techniques, specifically utilizing a deep learning technology known as Bi-Directional Long Short-Term Memory (Bi-LSTM) and machine learning algorithms Decision Tree, Random Forest, and Naive Bayes to evaluate textual data effectively. The primary objective of our system is to identify appropriate categories of input text /review based on patient reviews and defined categories in the dataset. In the dataset, we found two columns where one is a review and another is categories like treatment and medication, prevention, symptoms causes, news, and others where we will find the exact categories of our input review. We evaluate the system's performance using various metrics, including Fl Score, precision, recall, and accuracy, ensuring its robustness and efficacy. Bi LSTM models provided an accuracy of 95%, the Decision Tree models provided an accuracy of 94 %., the Random Forest models resulted in an accuracy of 92%., and the Naive Bayes models provided an accuracy of 84%. This comprehensive approach aims to provide medical experts with a reliable tool for predicting and ultimately contributing to more informed and personalized healthcare decisions.

关键词： Deep learning Accuracy Reviews Social networking (online) System performance Supervised learning Bayes methods

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Intrusion Detection at the Edge Computing: A Deep Learning Approach Using the UNSW-NB15 Dataset 14

Intrusion Detection at the Edge Computing: A Deep Learning A...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Vipin Kumar, Vivek Bhadauria, Abhay Pratap Singh Dixit, Jay Kumar, Amit Haridwar India Department of Computer Engineering & Applications GLA University UP Mathura India

ISBN: (纸本)9798331531935

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. The findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. This study investigates the application of edge computing, Internet of Things (IoT), and deep learning to improve the performance of Intrusion Detection Systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. The results demonstrate better performance, showing their suitability for real-time security applications. These findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments. © 2025 IEEE.

关键词： Edge computing

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They use of NLP in Association with AI Technology for the Purpose of Optimization in Various Fields 4

They use of NLP in Association with AI Technology for the Pu...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Joshi, Ankita Bansal, Saloni Gard, Gopal Krishan Al-Farouni, Mohammed Srinivasan, S. Shetty, Surendra Uttaranchal University Uttaranchal Institute of Management Department of Management Dehradun248007 India Gla University Department of Computer Engineering and Applications Mathura India Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India The Islamic University Najaf Iraq Cmr College of Engineering & Technology Department of Eee India Department of Master of Computer Applications Nitte India

ISBN: (纸本)9798350360165

In the world of Natural Language Processing (NLP), developing structures that not simplest carry out nicely but additionally offer apparent reasoning for their selections is paramount. Traditional gadget mastering fashions, in particular the ones based on deep gaining knowledge of, have excelled in diverse NLP duties. However, the ones models frequently act as 'black containers,' providing little to no perception into their selection-making processes. This opacity can undermine the don't forget and reliability perceived with the useful resource of customers. To deal with this trouble, we advocate a unique generative rationalization framework that endeavors to bridge the distance between high overall performance and explainability in NLP systems. This frameworks designed to concurrently make kind picks and generate unique, terrific-grained causes for the ones choices, integrating an explainable difficulty and minimum chance schooling. By providing human-readable justifications for its predictions, our framework enhances the interpretability and trustworthiness of NLP fashions. Experimental validation on two newly curated datasets demonstrates the framework's capacity to outperform existing baselines, significantly advancing the sector of explainable AI inside NLP. © 2024 IEEE.

关键词： Natural language processing systems

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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Transformative Trends in Reinforcement Learning: From Deep Q-Learning to Real-World applications 2

Transformative Trends in Reinforcement Learning: From Deep Q...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Mani, Chandra Sharma, Tarun Kumar Yadav, Kanchan Gupta, Divya Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India Miet Department of Bca UP Meerut India

ISBN: (纸本)9798350371055

Reinforcement studying (RL) is a type of machine studying that maximizes an agent's cumulative praise in an environment. As such, it has been more and more used to address a ramification of sequential selection-making issues successfully. Currently, there were most important breakthroughs within the RL space that have led to expanded research activity and the development of several novel techniques and packages. Those transformative traits include deep learning, model-free exploration, off-policy gaining knowledge of hierarchical techniques, meta-gaining knowledge of multi-agent training, deep reinforcement learning (DRL), and transfer learning. These new strategies to RL allow for the development of more efficient and powerful algorithms, which in flip allow more a hit education and alertness of reinforcement mastering marketers in lots of one of a kind domain names. Similarly, those additives of RL permit faster, more flexible, and stronger schooling for RL marketers. As a result, RL is rapidly turning into a part of many one of a kind areas of studies that require powerful choice-making systems, inclusive of robotics, herbal language processing, and healthcare. © 2024 IEEE.

关键词： Environment Healthcare Hierarchical Reinforcement Strategies

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Security of Image Using Watermarking Techniques and Visual Cryptography

Security of Image Using Watermarking Techniques and Visual C...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Lahar Srivastava Hitendra Garg Department of Computer Engineering & Applications GLA University Mathura

Due to rapid growth in technology there is a need to secure multimedia data like audio, video, text etc. available on the internet. In the proposed work, visual cryptography based watermarking techniques are proposed to secure image data. The watermark is inserted followed by visual cryptography where ‘n’ numbers of shares are created. DCT and IDCT are implemented to insert the watermark. The k shares out of n are required to get the original image where watermark is authenticated. PSNR is used for comparing the original image with images obtained after decryption.

关键词： Visualization Image processing Watermarking Streaming media computer networks Cryptography Discrete cosine transforms

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Cartoon Face Detection and Recognition with Emotion Recognition

Cartoon Face Detection and Recognition with Emotion Recognit...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Subhash Chand Agrawal Rajesh Kumar Tripathi Department of Computer Engineering & Applications GLA University Mathura

In the past few years, we have witnessed a high attentiveness in cartoon media, power-driven by the strong demands of the industry. The first step to understanding this media, cartoon face recognition and detection is a very important but less explored work with fewer datasets proposed. The paper, we have proposed has two methods for face recognition and face detection for different cartoon characters that appear in comics, cartoons, games, etc. Cartoon face recognition and detection is a more difficult and challenging task than human face detection because of the many difficult scenes involved. If we aim at the characteristics of cartoon faces, huge differences within the intra-faces exist. In order to identify the emotions from cartoon images, we have employed a Deep Neural Network (DNN) approach in this paper. Two cartoon characters, “Tom” and “Jerry,” who each exhibit diverse emotions like surprise, happiness, sadness, and rage, were used to create the dataset. The DNN method that successfully distinguishes cartoon characters splits their face masks and names the ensuing emotions with an accuracy score.

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