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Disruptive Technologies (ICDT), International Conference on

作者： Rakesh Kumar Pooja Chaudhary Shilpi Gupta Manoj Singhal Department of Computer Engineering & Applications GLA University Mathura UP India Department of CSE (Data Science) Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Computer Science & Engineering Meerut Institute of Technology Meerut UP India Department of Information Technology GL Bajaj Institute of Technology & Management Greater Noida UP India

Generative adverse Networks (GANs) have become increasingly famous for a variety of tasks in picture and audio processing, especially unsupervised getting to know. GANs encompass components, a “generator” and a “discriminator,” that are skilled adversarial. The generator is tasked with producing a pattern of statistics, and the discriminator is tasked with figuring out whether the information is “actual” or “fake.” The generator “learns” through trial and mistakes as it attempts to create samples that appear practical to the discriminator. Through the antagonistic schooling manner, the generator can create more accurate “fake” records while the discriminator can emerge as better at distinguishing between “actual” and “faux” records. GANs have been applied to various duties, which include picture era, textual content to photosynthesis, photo-to-image translation, and automatic music composition. Recent tries have been made to apply GANs to extra complicated obligations in the wild, including self-reliant use and drug discovery. GANs have inspired an ever-developing quantity of industries because of their potential to generate sensible facts with minimal human input.

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Classification and Discovery of Brain Tumor Using Ensemble Deep Learning

Classification and Discovery of Brain Tumor Using Ensemble D...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Kasanagottu Srilatha Chekka Sravani Mikkilineni Vasudevarao Ajmeera Kiran D Chitra Pundru Chandra Shaker Reddy Department of Data Science Geethanjali College of Engineering and Technology Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana India Department Of Computer Applications Dayananda Sagar Business Academy Karnataka India Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad Telangana India Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor.

关键词： Accuracy Computational modeling Medical treatment Predictive models Brain modeling Feature extraction Convolutional neural networks Lesions data mining Diseases

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Federated Learning in the Era of Privacy Preservation: A Comprehensive Review and Future Directions

Federated Learning in the Era of Privacy Preservation: A Com...

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Disruptive Technologies (ICDT), International Conference on

作者： Kushagra Kulshreshtha Bhawna Goel Hoshiyar Singh Kanyal Vikas Singhal Department of Computer Engineering & Applications GLA University Mathura UP India Department of CSE (Data Science) Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Computer Science & Engineering ABES Institute of Technology Ghaziabad UP India Department of Information Technology Greater Noida Institute of Technology Greater Noida UP India

Federated studying is a disbursed system learning method that lets a set of dispensed users, or clients, collaboratively learn a shared predictive model without sharing any statistics. Through taking part in the model education method, every purchaser contributes to the development of a worldwide, predictive model without sacrificing its very own information privacy. This privateness renovation may be achieved through various techniques, which include differential privateness, cozy multi-celebration computation, and information obfuscation. In this way, model schooling isn't always a task exclusively for centralized facts creditors, but as an alternative, a large-scale collaboration amongst multiple entities with disparate hobbies and facts silos. In the technology of privacy protection, federated studying represents a powerful tool that enables the construction of predictive fashions that might be knowledgeable by means of various information resources at the same time as retaining the integrity and privateness of the individual statistics units.

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Identification of the Video Level for Video Communication in a Resource-Constrained Network

Identification of the Video Level for Video Communication in...

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Artificial Intelligence and data Engineering (AIDE), International Conference on

作者： Gururaj Bijur Ramakrishna M Karunakar A. K Department of Computer Science & Engineering Manipal Institute of Technology MAHE Manipal Udupi Karnataka India Department of Data Science & Computer Applications Manipal Institute of Technology MAHE Manipal Udupi Karnataka India

HEVC, which employs layered video coding techniques, is an extension of H.264 Advanced Video Coding. Layered video coding creates a valid bitstream by removing layers. Video streaming via wireless networks is made possible by this capability. The elements that affect the quality of video communication include node mobility, the kind of routing protocols, communication distance, etc. The video adaptation method calculates the number of video levels that will be lost and then extracts at Media Aware Network Elements in accordance with terminal capabilities and resource availability. Building the information needed to select the extraction point or adaption level is the goal of this work. The extraction points for streaming Multi-level Video over the resource constrained network have been calculated in this article.

关键词： Wireless networks Dynamics Bandwidth Streaming media Media Routing Routing protocols

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Robust Indoor Person Re-Identification with Multimodal Training

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IEEE Internet of Things Journal 2025年

作者： Su, Can Xue, Xinlei Ma, Lei Zhang, Xiaolong Yan, Wei Bian, Kaigui Peking University School of Computer Science AI Innovation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing100871 China Peking University School of Computer Science Beijing100871 China Peking University Beijing Academy of Artificial Intelligence National Biomedical Imaging Center College of Future Technology National Key Laboratory for Multimedia Information Processing Beijing100871 China Beihang University Beijing100191 China

Existing person re-identification (ReID) methods mainly rely on images and videos to match persons across cameras, yet visual data captured by cameras are vulnerable to environmental interferences (e.g. illumination and occlusion) or personal appearance changes, leading to performance degradation under such scenes. Meanwhile, the popularization of Wi-Fi networks has allowed probe requests to be captured for mobile sensing applications such as crowd counting and trajectory estimation. However, the MAC address randomization technique adopted by modern devices breaks the association of probe requests and adversely affects the functionality of these applications. In this paper, we propose MaRPA, the first multimodal training approach that incorporates both videos and Wi-Fi probe requests to simultaneously promote tasks of probe requests association and person ReID. MaRPA first distinguishes among pairwise probe request frames through a contrastive learning model. It then matches video and probe request sequences by exploring their similarities from the position and the vision aspects. Matched videos and probe requests provide complementary information and generate more robust features for both tasks. To evaluate MaRPA, we contribute a new dataset containing synchronous videos and probe requests data for probe requests association and person ReID. Experimental results demonstrate the effectiveness of our approach. For probe requests association, it achieves > 85% discrimination accuracy and > 0.90 V-measure score;for person ReID, it achieves 75.8% mAP and 90.6% Rank-1, improving state-of-the-art video-based ReID methods by over 40% © 2014 IEEE.

关键词： Wi-Fi

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Constraint Boundary Wandering Framework: Enhancing Constrained Optimization with Deep Neural Networks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wu, Shuang Chen, Shixiang Shen, Li Zhang, Lefei Tao, Dacheng Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China University of Science and Technology of China Key Laboratory of the Ministry of Education for Mathematical Foundations and Applications of Digital Technology School of Mathematical Sciences China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Constrained optimization problems are pervasive in various fields, and while conventional techniques offer solutions, they often struggle with scalability. Leveraging the power of deep neural networks (DNNs) in optimization, we present a novel learning-based approach, the Constraint Boundary Wandering Framework (CBWF), to address these challenges. Our contributions include introducing a boundary wandering strategy inspired by the active-set method, enhancing equality constraint feasibility, and treating the Lipschitz constant as a learnable parameter. Additionally, we evaluate the regularization term, illustrating that the nonsmooth L2 norm yields superior results. Extensive testing on synthetic datasets and the ACOPT dataset demonstrates CBWF's superiority, outperforming existing deep learning-based solvers in terms of both objective and constraint loss. © 1979-2012 IEEE.

关键词： Constrained optimization

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Efficient mode decision in HEVC

Efficient mode decision in HEVC

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Artificial Intelligence and data Engineering (AIDE), International Conference on

HEVC is the new coding standard aiming to provide better coding efficiency compared to its previous standard. Meantime this increases the computational complexity. In HEVC, there are 35 intra prediction modes, where as in H.264/A VC there were only 9 intra prediction modes. In this paper, we proposed a fast rough mode decision (RMD) algorithm to decrease the computational complexity using up and left reference pixels. The three-stage search method is implemented in RMD algorithm. The results obtained when compared with HM16.18, shows that the proposed algorithm can save 12.78 % to 28.86% of the encoding time with acceptable loss in video quality.

关键词： Degradation Search methods Prediction algorithms data engineering Encoding Quality assessment Computational complexity

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Intelligent Energy Management for Electric Vehicles: Fuzzy Logic and Neural Network Integration

Intelligent Energy Management for Electric Vehicles: Fuzzy L...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： T Thirumalaikumari D Lita Princy S Uma R Sadaieswaran P Malathi N Legapriyadharshini Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Master of Computer Applications Meenakshi College of Engineering Chennai Tamil Nadu India Department of Computer Science Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mahabalipuram Tamil Nadu India Department of Computer Applications Tagore College of arts and science Chromepet Chennai India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Application Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This study suggests a novel methodology for intelligent energy management in electric vehicles (EVs) through the integration of neural networks and fuzzy logic. Achieving enhanced energy efficiency for electric vehicles is a critical concern that is tackled in this research through the integration of neural networks' decision-making and pattern recognition functionalities with fuzzy logic's adaptability and learning capabilities. The proposed method seeks to optimize energy consumption, enhance overall performance, and extend driving range. The integrated system is subjected to rigorous testing using a meticulously planned experimental setup and simulation environment, which enables it to surpass the performance of more conventional approaches to energy management. The results emphasize the potential of the novel methodology to improve environmentally sustainable transportation options and propel the development of electric vehicle technology. This study not only offers valuable insights into efficient energy management but also establishes the groundwork for subsequent developments in the domain.

关键词： Fuzzy logic Roads Neural networks Transportation Propulsion Electric vehicles Energy efficiency Pollution measurement Energy management systems Testing

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Text Classification on Tweets predicting the Mental Illness using Natural Language Processing and Machine Learning Classifiers

Text Classification on Tweets predicting the Mental Illness ...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： A. Vijayalakshmi T. N. Aruna Divya. N V J Chakravarthy M. Seenivasan Department of Data Science Dwaraka Doss Goverdhan Doss Vaishnav College Chennai India Department of Computer Applications Dr. MGR Educational and Research Institute Chennai Maduravoyal India Department of Mathematics CDOE Annamalai University Tamil Nadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

In the 21st Century, people’s are investing additional occasions on social media reflecting their feelings by means of the languages(text). The modern inquire about is looking at whether people's conventional dialect gives a great flag for anticipating the event of mental ailment. Dialect tests (Tweets) were assembled from the social media site, with tweets utilized to portray different sorts of mental condition. As hypothesized, tweets taken from the client can be utilized to separate a assortment of mental sicknesses (ADHD, uneasiness, bipolar clutter, and sadness). At last, models prepared on tweets particular in expressions showing disorder-specific side effects, while models instructed to foresee mental states particular in words, showing the sorts of choices prescient of mental ailment. This paper points to appear an NLP spaCy and the machine learning classifiers to anticipate mental sickness on tweets by categorizing the content and gets the exactness of 99.25% in NLP - spaCy and 96.71% in ML Irregular Timberland & XGBoost Demonstrate.

关键词： Social networking (online) Text categorization Machine learning Natural language processing Information and communication technology Clutter

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Improving distributed denial of service attack detection using supervised machine learning

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Measurement: Sensors 2023年 30卷

作者： Fathima, Afrah Devi, G. Shree Faizaanuddin, Mohd Dept.of Computer Applications BSAR Crescent Institute of Science and Technology Chennai India Dept. Of CS & IT Maulana Azad National Urdu University Hyderabad India Dept. of AI & Data Science M.Tech Chaitanya Bharathi Institute of Technology Hyderabad India

Distributed denial-of-service (DDoS) attacks are a big problem for cyber security because they can cause a lot of damage to both people and companies. Distributed Denial of Service (DDoS) attacks have been seen to do a lot of damage to the networks and devices they are aimed at. These hacks slow down networks and use up buffer space, which makes resources unavailable. To solve this problem, "Supervised Machine Learning Models" have been used. Several machine learning techniques, such as Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression, were used to figure out what was normal and what was an attack. This study used a sample of the CSE-CICIDS2018, CSE-CICIDS2017, and CICDoS datasets. The dataset was divided into two parts in which three fourth of the data was used for training and one fourth of the data for testing purpose. The proposed research attempt to classify the DDoS attack by using supervised machine learning classifiers. This approach employs three machine learning classifiers such as Random Forest, KNN and Logistic regression. Then we perform Feature Scaling by using Standard Scaler. Finally, the system was evaluated. Random forest classifier outperformed other classifiers with an accuracy of 97.6 % whereas KNN and Logistic regression achieved 97 % and 91.1 %. The study employed several Supervised Machine Learning techniques, including Random Forest, KNN, and Logistic Regression to identify the most effective algorithm for the test. Results demonstrate that Random Forest outperformed the other models. © The Authors

关键词： Denial-of-service attack

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