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共 295156 条记录，以下是211-220 订阅

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Generative Artificial Intelligence Based Biomedical Applications for Pharmaceutical Industry

Generative Artificial Intelligence Based Biomedical Applicat...

引用

2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bhatt, Stuti Sharma, Sachin Uttaranchal University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331512965

The quick advancement in AI has led to the development of refined large language models (LLM). While LLM is a specific type of generative AI focused on generative text generative AI is a broader category encompassing various AI systems that can generate new content including texts, images, music, and more generation models. Generative AI is transforming various fields, like education, finance, marketing, fashion, gaming, medical area or healthcare. Generative AI finds its applications in improving drug discovery, biomedical instrument design, and healthcare delivery within the pharmaceutical industry. In this study, we aim to find generative artificial intelligence-based biomedical applications for the pharmaceutical industry. © 2025 ieee.

关键词： Generative adversarial networks

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Optimizing Advertising Campaigns through Advanced Deep Learning-Driven Audience Segmentation for Enhanced Targeting and Engagement 3

Optimizing Advertising Campaigns through Advanced Deep Learn...

引用

3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Nikhil, Gunda Anil, P.S. Obaid, Saif Sehgal, Archana Kuppuraj, T. Saravanan K, Aanandha Digital Connect Hyderabad India Gnanam School of Business Department of Management Mary's junction Sengipatti TamilNadu Thanjavur India College of technical engineering The Islamic University Department of computers Techniques engineering Najaf Iraq Lovely Professional University Punjab Phagwara India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331542375

The present work discusses the utilization of deep learning techniques for precise dividing the audience for advertising in order to increase efficiency and effectiveness of campaigns. Hence, the paper employs a diverse dataset of consumers' demographic data, purchase history, social media activity, and reviews and design a hybrid deep learning model using CNN, RNN-LSTM, and attention mechanisms. In all the assessment measurements of accuracy, precision, recall, and Fl-score, this model offers better results compared with the old-shoot segmentation methods. The performance analysis depicts great enhancements in CTR, conversion rate, as well as ROAS all of which highlight a superior engagement of the ads. Indeed, the outcome points to the effectiveness of deep learning in digital marketing that can provide clients with more efficient advertising tools. Nonetheless, the issues regarding computational capabilities and ethical concerns pose viable and useful innovations and solutions, thus contributing to the progression of deep learning theories in audience segmentation and advertising. © 2025 ieee.

关键词： Deep learning

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Optimizing Resource Allocation and Load Balancing for Multimedia Applications in Hybrid Clouds 3

Optimizing Resource Allocation and Load Balancing for Multim...

引用

3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Naval, Preeti Badiger, Veena S Sharma, Nittin Senapati, Biswaranjan Singh, Neelesh Malathi, H. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

However, hybrid clouds that contain both public and private cloud environments have been gaining traction as a popular option for hosting multimedia applications. The variety of public clouds means that resource management and load balancing can quickly become complex. This paper introduces an optimization process for resource allocation and load balancing for multimedia applications running on hybrid clouds. We first leverage a multi-objective optimization model to collaboratively determine how many resources are needed and where they should be deployed according to application requirements, user preferences, and cost constraints. It allows us to optimize resources between different clouds and reduce overall costs. We also employ a load-balancing algorithm that respects the heterogeneity of resources in hybrid clouds and demands from multimedia applications. The algorithm above guarantees a uniform distribution of resources and generates enough parallelism to avoid overloading any single cloud, our experiments show that we can efficiently allocate the resources and balance the load for multimedia applications in a hybrid cloud. Enhances performance and reduces costs while delivering a great user experience for multimedia applications in hybrid cloud environments. © 2025 ieee.

关键词： Resource allocation

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RecNet: Optimization for Dense Object Detection in Retail Scenarios Based on View Rectification

RecNet: Optimization for Dense Object Detection in Retail Sc...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xiao, Junhao Chen, Yi Feng, Xiao Wang, Ruoyu Wu, Zhiyu School of Computer Central China Normal University China School of Mathematical Sciences University of Electronic Science and Technology of China China School of Computer Science Fudan University China

ISBN: (纸本)9798350368741

High-precision dense object detection in retail is crucial for automation, inventory management, and sales optimization. Our experiments revealed that detection models perform significantly better with frontal views than with oblique views, motivating the development of RecNet. RecNet utilizes a Rectify-Detect (R-D) pipeline to transform oblique views into frontal views, mitigating perspective distortion and focusing on key regions. To optimize bounding box prediction, we propose the CeIoU loss function, which focuses on high-quality boxes using a fair elite selection strategy. We also introduce the Neighbor Scattering Algorithm to address accuracy loss caused by rounding errors during the rectification process. Additionally, we present a Transform-Aware Branch that integrates transformation information into the regression branch for direct bounding box prediction. Experiments show that RecNet achieves state-of-the-art performance on SKU110K and demonstrates strong generalization on PUCPR+. © 2025 ieee.

关键词： product detection retail view rectification

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Deep Learning-Based Melanoma Detection: A CNN and RNN Hybrid Approach 3

Deep Learning-Based Melanoma Detection: A CNN and RNN Hybrid...

引用

3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Yadav, Dhananjay Kumar Kartik, N. Kalia, Akhilesh Kumar, Rakesh Kandhari, Harsimrat Preetham, K. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

Deep learning networks have recently performed excellently in medical image analysis tasks, including skin cancer detection [5]. Many works have proposed using a hybrid deep learning approach that combines Convolutional Neural Networks and Recurrent Neural Networks for accurate classification to detect lethal forms of skin cancer melanoma [17]. Our proposed method combines the advantages of CNN in image feature extraction and RNN in dealing with sequential data. To establish a comprehensive CNN-RNN hybrid model, we train on more skin images, and the model can separate [separate] between benign or malignant lesions. CNN captures the low-level image features, and RNN helps to analyze the sequential pattern of these features, thereby enhancing detection accuracy. Experimental results demonstrate that the proposed hybrid approach can achieve better accuracy, sensitivity, and specificity than traditional CNN-only models. Furthermore, the model proposed may also use patient-specific information such as demographics and medical history for better detection performance. The deep learning strategy can allow earlier melanoma diagnosis and help save patient lives. © 2025 ieee.

关键词： Recurrent neural networks

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Network Coding Techniques for Resource Allocation in Wireless Relay Networks 3

Network Coding Techniques for Resource Allocation in Wireles...

引用

3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Khanna, Lalit Ranganathaswamy, M.K. Balamurugan, V. Maharishi, Ankita Agarwal Saini, Rajat Nanda, Sarmistha Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

Network coding is an applicable technique to enhance the performance of wireless relay networks, in which intermediate nodes are utilized to expand the coverage range and increase communication reliability. The utilization of network coding in these networks should be well addressed by resource allocation such as bandwidth and power allocation. Among them, network coding for resource allocation in wireless relay networks is the most attractive research work. In particular, dynamic network coding is a strategy that varies how we code data according to current conditions in the network so as to achieve the maximum possible throughput during relaying. Dual-joint power and resource combinations have relatively better performance gains due to the efficient channel gain with an additional level of resource utilization for enhanced resource allocation. In addition, network coding schemes, including opportunistic and homomorphic network coding, have been introduced to perform well in an environment with fading channels and enhance data transmitting reliability. These techniques take advantage of the intrinsic diversity in wireless pathways to improve overall network throughput and reduce latency. © 2025 ieee.

关键词： Bandwidth

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A Variable Frequency Switching Scheme for THD Reduction with 13-level Symmetrical Inverter under Test

A Variable Frequency Switching Scheme for THD Reduction with...

引用

2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Shukla, Tanmay Patidar, N.P. Verma, Ranu Prestige Institute of Management and Research Bhopal Department of Electrical and Electronics Engineering Bhopal India Maulana Azad National Institute of Technology Department of Electrical Engineering Bhopal India Amity University Department of Electrical and Electronics Engineering Noida India

ISBN: (纸本)9798331529833

To reduce distortion from the fundamental sinusoidal waveform, inverters were extended to more than two layers, which gave rise to the concept of a multilayer inverter. The need for extra switches, which increases system bulk and cost and decreases system reliability because of the higher component count, is a disadvantage of multiple-level inverters. The distortion in the result is lessened by the increase in inverter levels. In this study, a variety of switching approaches have been tried using a symmetrical 13 level inverter (13-LI) and their results are compared with 180 degree shifted adjacent level pulse span modulation. This is because efficient switching techniques may help minimize distortion. The model and variable switching strategy are constructed using the MATLAB software. © 2025 ieee.

关键词： Pulse width modulation

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Innovations in Battery Design for Improved Cycle Durability and Flexibility in Automotive Applications 3

Innovations in Battery Design for Improved Cycle Durability ...

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3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Garg, Avni Eti, Muktha Srilakshmi K, H. Yadav, Dhananjay Kumar Kaushik, Vaibhav Sahu, Prabhat Ku. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre for Interdisciplinary Research for Business and Technology Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

The increasing call for electric-powered and hybrid cars has led to the want for greater efficient and durable battery designs. In response, improvements in the battery era have targeted improving cycle sturdiness and versatility in automotive programs. One essential innovation is the use of lithium-ion batteries, which offer higher power density and longer cycle life compared to traditional lead-acid batteries. Inside the realm of lithium-ion batteries, improvements have been made in electrode materials and cell designs. For instance, using silicon-based, total anodes has substantially increased the strength density of lithium-ion batteries, allowing for longer use tiers and improved cycle existence. Additionally, advancements in battery control structures have progressed the potential to reveal and manipulate the charging and discharging of batteries, growing their usual durability and lifespan. This has caused the development of fast-changing technologies that could rate a battery to eighty in only half an hour, making it an extra handy and realistic alternative for electric car proprietors. © 2025 ieee.

关键词： Lithium-ion batteries

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Malignant Tumor Classification Using Hybrid CNN-SVM Models in Medical Imaging 3

Malignant Tumor Classification Using Hybrid CNN-SVM Models i...

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3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Kalra, Simran Das, Sudeepa Seth, Kanika Sambargi, Seema Singh, B.P. Dev, Arghya Das Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India

ISBN: (纸本)9798331542375

Automatic classification of malignant tumors has become particularly important in medical imaging because early detection and accurate diagnosis can dramatically improve patient outcomes. These days, deep learning techniques have demonstrated an out-sized success in numerous fields of medical imaging, such as tumor classification. In this paper, we present a hybrid CNN-SVM model used for classifying malignant tumors in medical imaging. In this paper, we take the fugitive approach by using CNN to segment tumor regions, and SVM is used to classify cancer as Benign or Malignant. For categorization, CNN is employed to eliminate the high-level features from the medical pictures sustained by that SVM. Our study is implemented as a hybrid model that learns from an expansive collection of medical images with benign and malignant tumors. Finally, to evaluate the performance of our model, we compared it with referenced models (traditional CNN and SVM) on the same dataset. The results indicated that the hybrid model CNN-SVM is superior in terms of higher accuracy and fewer error rates. Additional experiments on a new dataset demonstrate the model's generalization capability, which can be beneficial in practical applications. © 2025 ieee.

关键词： Mammography

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Opportunistic Spectrum Access Techniques for Cognitive Radio Networks 3

Opportunistic Spectrum Access Techniques for Cognitive Radio...

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3rd ieee international conference on automation and Computation, AUTOCOM 2025

作者： Maharishi, Ankita Agarwal Jesupriya, J. Sriram, Siddharth Tripathy, Siba Prasada Singla, Abhishek Yashoda, L. School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

Opportunistic spectrum access (OSA) strategies have emerged as a promising option to cope with the increasing demand for spectrum resources in cognitive radio networks. Those networks are designed to allow secondary users (SUs) to opportunistically utilize underutilized licensed spectrum bands without causing harmful interference to number-one customers (PUs). OSA strategies utilize spectrum sensing, spectrum selection, and spectrum sharing techniques to efficiently and correctly allocate spectrum resources to SUs. The main goal of these techniques is to maximize spectrum usage while ensuring the safety of licensed customers. Spectrum sensing strategies allow SUs to discover spectrum holes, and secondary customers can opportunistically transmit them in those idle spectrum bands. Spectrum selection techniques help SUs determine the first-rate spectrum bands to utilize based totally on diverse metrics along with channel satisfaction, interference, and sign strength. Spectrum-sharing techniques enable SUs to allocate spectrum resources among multiple users dynamically. These techniques can noticeably improve spectrum usage and alleviate the issue of spectrum shortage in cognitive radio networks, which is mainly due to advanced device performance and enhanced user revel. © 2025 ieee.

关键词： Multiple access interference

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