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共 11565 条记录，以下是541-550 订阅

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Machine learning-Driven Anomaly Detection: A Comprehensive Model for Digital Security 4

Machine Learning-Driven Anomaly Detection: A Comprehensive M...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Samarth, S. Sumukha Raghavan, M. Santhosh, T. Kowligi, Sahana J. Pooja, M.R. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

ISBN: (纸本)9798331529635

this research study proposes a novel approach for behavioral tracking and anomaly detection in digital systems by using AI-driven models, particularly for applications in signal processing and digital computer environments. the proposed AI model combines techniques of behavioral tracking, aiming at identifying deviations in the operations of the system, while enhanced monitoring and anomaly identification are the focus. this methodology captures subtle deviations in user behavior and system signals to enhance real-time security and system integrity, thereby reducing the likelihood of unauthorized access and system misuse. © 2024 IEEE.

关键词： AI Models Anomaly Detection Behavioral Tracking Digital Systems

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Knowledge Distillation for Single Image Super-Resolution via Contrastive learning 24

Knowledge Distillation for Single Image Super-Resolution via...

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4th Annual International conference on Multimedia Retrieval (ICMR)

作者： Liu, Cencen Zhang, Dongyang Qin, Ke Univ Elect Sci & Technol China Lab Intelligent Collaborat Comp Chengdu Peoples R China

ISBN: (纸本)9798400706028

In recent years, thanks to the vigorous development of deep learning, single image super-resolution has advanced greatly. Most super-resolution (SR) methods use convolution layers to construct the network, which achieves superior results over the traditional methods based on manual features. However, most methods based on convolutional neural networks (CNN) blindly deepen the depth of the network leading to a large number of model parameters, which inevitably brings huge computing overhead and memory consumption, and greatly limits the application in resource-limited devices. In order to alleviate this problem, a knowledge distillation framework based on contrastive learning is proposed to compress and accelerate the SR model with enormous parameters. the student network is directly constructed by reducing the number of layers of the teacher network. In particular, the proposed method distills the statistical information of the intermediate feature maps from the teacher network to train the lightweight student network. In addition, through explicit knowledge transfer, a novel contrastive loss is introduced to improve the reconstruction performance of the student network. Experiments show that the proposed contrastive distillation framework can effectively compress the model scale with an acceptable loss of performance.

关键词： Single image super-resolution knowledge distillation contrastive learning

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Innovative Transfer learning Strategies for Alzheimer's Detection Using Deep learning 4

Innovative Transfer Learning Strategies for Alzheimer's Dete...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Chauhan, Shanvi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331529635

the progressive neurodegenerative disorder known as Alzheimer's disease reduces cognitive abilities, therefore influencing the interpersonal quality of living for people impacted. Early identification is absolutely vital since it enables timely treatments and management, therefore enhancing patient outcomes and enabling focused therapy approaches. this paper uses a VGG-16 transfer learning model to investigate ways to raise the diagnosis accuracy for Alzheimer's disease. the dataset used consists in three subsets: 27,187 pictures for learning, 6,497 pictures for evaluation, and 2,039 pictures for testing, each labelled with four classes: MildDemented, VeryMildDemented, NonDemented, and ModerateDemented. the model was developed using the training data;the validation set served to tune the model and reduce overfitting. Efficiency of the model on unprocessed data was evaluated using the test set. Our results show that the VGG-16 transfer learning model obtained a remarkable 95% accuracy. this great degree of performance emphasizes the efficiency of the model in early Alzheimer's diagnosis and encourages developments in health innovation, thereby improving healthcare outcomes and promoting inclusivity in medical diagnostics. these advancements contribute towards Sustainable Development Goals (SDGs), particularly in the areas of Good Health and Well-being, Reduced Inequalities, Industry, Innovation, and Infrastructure, and Partnerships for the Goals, thereby fostering equitable and accessible healthcare solutions for all. © 2024 IEEE.

关键词： Contrastive learning

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Skin Cancer Detection and Classification using Deep learning 4

Skin Cancer Detection and Classification using Deep Learning

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Jain, Sweta Dhakate, Srushti Gujar, Prathamesh Rajbhoj, Prathamesh Sathawane, Utkarsh Shri Ramdeobaba College of Engineering and Management Department of Computer Science Nagpur India

ISBN: (纸本)9798350349856

Skin cancer is among the most common types of cancer, and quick identification considerably enhances the odds of survival. the purpose of this work is to develop cutting-edge deep learning models that can classify images of skin cells and accurately detect cases of skin cancer. the strength of deep learning algorithms is utilized in this research, which uses a cloud-based architecture. these algorithms, which make up the system's core, allow for the creation of models that significantly improve skin cancer prediction accuracy. the publication provides a thorough explanation of the model construction procedure and how it is used to categorize dermal cell images. Using the ISIC2018 dataset, the convolution neural network (CNN) deep learning technique was employed in this study to identify the two main categories of cancers, malignant and benign. this dataset includes 3297 benign and malignant skin lesions. the deep learning models developed for this study are carefully assessed using validated datasets. Precision, Recall, F1-Score, Support and the area under the curve, all measures, showed remarkable performance at a rate of 96.02. this study presents a model-driven architecture that practitioners can use to quickly diagnose skin cancer using state of the art deep learning architecture. © 2024 IEEE.

关键词： Diseases

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Deep Heuristic learning for Real-Time Urban Pathfinding 4

Deep Heuristic Learning for Real-Time Urban Pathfinding

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4th International Mobile, intelligent, and Ubiquitous Computing conference

作者： El-Ela, Mohamed Hussein Abo Hamdi, Ali MSA Univ Comp Sci Cairo Egypt

ISBN: (纸本)9798350367782;9798350367775

this paper introduces a novel approach to urban pathfinding by transforming traditional heuristic-based algorithms into deep learning models that leverage real-time contextual data, such as traffic and weather conditions. We propose two methods: an enhanced A* algorithm that dynamically adjusts routes based on current environmental conditions, and a neural network model that predicts the next optimal path segment using historical and live data. An extensive benchmark was conducted to compare the performance of different deep learning models, including MLP, GRU, LSTM, Autoencoders, and Transformers. Both methods were evaluated in a simulated urban environment in Berlin, with the neural network model outperforming traditional methods, reducing travel times by up to 40%, while the enhanced A* algorithm achieved a 34% improvement. these results demonstrate the potential of deep learning to optimize urban navigation in real time, providing more adaptable and efficient routing solutions.

关键词： Heuristic A Neural Networks Pathfinding Traffic Prediction Real-Time Data Urban Navigation

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4th International conference on Communication and intelligent Systems, ICCIS 2022

4th International Conference on Communication and Intelligen...

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4th International conference on Communication and intelligent Systems, ICCIS 2022

ISBN: (纸本)9789819920990

the proceedings contain 52 papers. the special focus in this conference is on Communication and intelligent Systems. the topics include: Transfer learning-Based End-to-End Indian English Recognition System;Impact of COVID-19 on the Sectors of the Indian Economy and the World;wind Farm Layout optimization Problem Using Teaching–learning-Based optimization Algorithm;an Ensemble Multimodal Fusion Using Naive Bayes Approach for Haptic Identification of Objects;Chaotic Maps and DCT-based Image Steganography-cum-encryption Hybrid Approach;an Empirical Analysis and Challenging Era of Blockchain in Green Society;3D Modeling of Automated Robot for Seeding and Transplantation of Rice and Wheat Crops;metaheuristics based Task Offloading Framework in Fog Computing for Latency-sensitive Internet of things Applications;empirical Evaluation of Microservices Architecture;thermoelastic Energy Dissipation Trimming at High Temperatures in Cantilever Microbeam Sensors for IoT Applications;A Probabilistic Method to Identify HTTP/1.1 Slow Rate DoS Attacks;contactless Fingerprint Matching: A Pandemic Obligation;deep Reinforcement learning to Solve Stochastic Vehicle Routing Problems;applying Machine learning for American Sign Language Recognition: A Brief Survey;identification of Promising Biomarkers in Cancer Diagnosis Using a Hybrid Model Combining ReliefF and Grey Wolf optimization;prediction of Ectopic Pregnancy in Women Using Hybrid Machine learning Techniques;Redundancy Reduction and Adaptive Bit Length Encoding-Based Purely Lossless ECG Compression;antecedents, Barriers, and Challenges of Artificial Intelligence Adoption for Supply Chains: A Tactical Review;Statistical Influence of Parameters on the Performance of SDN;Investigations on Channel Characteristics and Range Prediction of 5G mmWave (39 GHz) Wireless Communication System;cervical Spine Fracture Detection;Transmission Pricing Using MW Mile Method in Deregulated Environment;unmanned Ground Vehicle for Survey of Endangered S

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4th International conference on Communication and intelligent Systems, ICCIS 2022

4th International Conference on Communication and Intelligen...

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4th International conference on Communication and intelligent Systems, ICCIS 2022

ISBN: (纸本)9789819923212

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Bibliometric Analysis of Trends and Directions in Machine learning Applied to FinTech 4

Bibliometric Analysis of Trends and Directions in Machine Le...

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4th International conference on Electronic and Electrical Engineering and intelligent System, ICE3IS 2024

作者： Arif, Erman Suherman, Suherman Widodo, Aris Puji Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350378368

Our article presents a comprehensive analysis of trends and directions in the application of machine learning in the FinTech sector. By utilizing data from academic databases such as Scopus, our objective is to showcase the advancements in this interdisciplinary research area. the study identifies key trends, influential authors, leading journals, and significant collaborative networks that shape the field. the methodology employed involves quantitative bibliometric analysis to evaluate publications from the past five years. the results indicate a substantial increase in the number of studies during this period, highlighting the importance of machine learning in addressing critical financial challenges, including fraud detection, algorithmic trading, credit scoring, and risk management. Additionally, we delve into the main themes, countries, and institutions that dominate the current research agenda. this analysis provides valuable insights into the present state and future direction of machine learning in FinTech, serving as a resource for researchers and practitioners seeking to comprehend and contribute to this dynamic and rapidly evolving field. We also discuss implications for future research, emphasizing the necessity for continuous innovation and interdisciplinary collaboration to maximize the potential of machine learning in transforming financial services. © 2024 IEEE.

关键词： Adversarial machine learning

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Expanding the Horizons of Chinese Text Classification with Federated learning Across Domains 4

Expanding the Horizons of Chinese Text Classification with F...

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4th International conference on intelligent Technologies, CONIT 2024

作者： Mehta, Shiva Aneja, Aseem Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350349900

Federated learning (FL) is a new technology that can correct privacy issues in machine learning by training models on dispersed units. through applying a hybrid algorithm comprising Federated learning (Fedlearning), Local Batch Normalization (LBN), and Parameter Factorization Weighting (PFW), the article now introduces a Chinese text classifier called the FedBN-PW-CTC Model. this model intends to ensure that accuracy and F1-value substantially improve when solving text categorization problems. Taking into account the local batch normalization, the weighting of the models, and the federated learning with group leaders is the next in line;this approach assumes the priority of a closer consideration of the different elements in Chinese news content and keeping the information secret. We determine the equivalence of the structural characteristics of Siamese networks and PPG signal databases without and with various patients. the model also offers robust grades of precision and accuracy, accompanying its successful scores. the model is superior to the literature in automating text production with a 94% F1 score, as indicated by the statistical data and the site accuracy of 95%. For Bank A and B, the confidence level is 94% and 95%, respectively, and the margin of error is 6% and 5%.Moreover, the p-values for the accuracy and F1 score comparisons were also calculated to be © 2024 IEEE.

关键词： Federated learning

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Multi-Modal ISL-Speech: A Real-Time Neural Architecture for Sign Language Translation to Natural Language 4

Multi-Modal ISL-Speech: A Real-Time Neural Architecture for ...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Chempavathy, B. Syam Dev, R.S. Karmakar, Rohit Sidhu, I.S. Nishanth, V. Sumanthraj, P. New Horizon College of Engineering Department of Cse Bengaluru India New Horizon College of Engineering Department of Aiml Bengaluru India

ISBN: (纸本)9798331529635

this research study presents a novel neural architecture for real-time Indian Sign Language (ISL) translation to regional languages, incorporating advanced optimization techniques to enhance both accuracy and computational efficiency. We introduce a hybrid attention mechanism combining spatial and temporal attention with quaternion-based neural networks (QNN) for improved gesture recognition. the architecture employs neural architecture search (NAS) with hardware-aware constraints to optimize model deployment on edge devices. Our approach integrates a novel adaptive pruning strategy that dynamically adjusts model complexity based on input complexity, significantly reducing latency while maintaining high accuracy. Experimental results demonstrate a 27% improvement in translation accuracy compared to existing methods, while achieving a 40% reduction in inference time. the system successfully translates ISL to five major Indian languages with an average accuracy of 94.2% and an end-to-end latency of 45ms on consumer-grade hardware. © 2024 IEEE.

关键词： Deep learning

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