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Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

China Communications 2024年第8期21卷 115-126页

作者： Talha Younas Shen Jin Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir Mahrukh Liaqat School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255000China Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal Campus Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Oman Department of Electrical and Computer Engineering Addis Ababa Science and Technology UniversityAddis AbabaEthiopia Department of Electrical Engineering College of Electrical and Mechanical EngineeringNUSTIslamabadPakistan

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power *** power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low *** this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician *** start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in *** also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the *** emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining *** also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable *** emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

关键词： low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing

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CAAM- CNN With Autoencoder Attention Mechanism for Recommendation System to Improve Trust in E-commerce Industry 2

CAAM- CNN With Autoencoder Attention Mechanism for Recommend...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sethi, Vikas Kumar, Rajneesh Mehla, Stuti Department of Computer Science and Engineering Ambala Mullana India Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Panipat India

ISBN: (纸本)9798350383287

COVID-19 crisis expedited the spread of e-commerce, moving beyond luxury goods and services and towards new businesses, product categories and basic necessities that are important to many people. Customers can have access to a wide range of products from the comfort and security of their homes but still have trusting issues while purchasing online. Online feedback either comments or ratings given by customers, has the power to affect how consumers make purchasing decisions. This meteoric expansion of digitized information and number of users over internet brought the use of recommender systems as a significant solution by offering more proactive and individualized information services and enable users to quickly narrow their choices and make wise ***, the majority of existing recommendation models have poor accuracy issues as a result of data scarcity and cold start issues because recommender systems only have a limited amount of explicit data and this also lead to trust issue where users are fear of being taken advantage due to misleading information on the e-commerce websites. To solve this problem and provide quality recommendations, a new trust-based approach CNN with Autoencoder Attention Mechanism (CAAM) is proposed. CAAM uses static and dynamic trust combined with the user-item preferences and pass through the auto-encoder to reduce noise and make encoded features visible. After that convolution is used to combine encoded features with user and item features in latent space to provide nonlinear mapping and increase the reliability of features and to improve the recommendations which ultimately lead to improve the trust of people over *** Epinions dataset is used to evaluate the concept in a number of measures. © 2024 IEEE.

关键词： Recommender systems

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DRG-Net: Diabetic Retinopathy Grading Network Using Graph Learning with Extreme Gradient Boosting Classifier

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 171-184页

作者： Poranki, Venkata Kotam Raju Rao, B. Srinivasa Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Diabetic retinopathy (DR) is a leading cause of blindness that occurs in different age groups. So, the early detection of DR can save millions of people from blindness issues. Further, the manual analysis of DR requires much processing time and experienced doctors. Hence, computer-aided diagnosis (CAD)-based artificial intelligence models have been developed for an early DR prediction. However, the state-of-the-art methodologies failed to extract the deep balanced features, which resulted in poor classification performance. Therefore, this work implements the DR grading network (DRG-Net) using graph learning properties. Initially, the synthetic minority over-sampling technique (SMOTE) is applied to the EyePACS and Messidor dataset to balance the instances of each DR class into uniform levels. Then, a deep graph correlation network (DGCN) is applied to extract the class-specific features by identifying the relationship. Finally, an extreme gradient boosting (XGBoost) classifier is employed to perform the DR classification with the pre-trained balanced features obtained using SMOTE-DGCN. The obtained simulation results performed on the EyePACS dataset and the Messidor dataset disclose that the proposed DRG-Net resulted in higher performance than state-of-the-art DR grading classification approaches, with accuracy, sensitivity, and specificity of 99.01%, 99.01%, and 98.43% for the EyePACS dataset, respectively, and 99.6%, 99.08%, and 100% for the Messidor dataset. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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BrandCrafter AI, an AI-Based Brand Identity Generation Platform 15

BrandCrafter AI, an AI-Based Brand Identity Generation Platf...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Gawad, Selim Kim, Mira College of Engineering and Computer Science California State University Department of Computer Science Fullerton United States

ISBN: (纸本)9798331507695

In today's dynamic and highly competitive market, brand differentiation has become both essential and complex. The growth of social media and enhanced digital accessibility have transformed brand promotion into a multifaceted challenge, requiring a strategic and ongoing connection with target audiences to build loyalty and deter them from migrating to competitors. The constant evolution of social media trends and search engine optimization has added layers of complexity, creating an ongoing challenge for brands to remain visible and relevant. For new entrepreneurs, the cost of professional branding consultants is often beyond reach. This paper explores the potential of large language models (LLMs) as a cost-effective, automated solution for generating comprehensive, customized brand identity guidelines. We investigate the effectiveness of LLMs in creating cohesive branding strategies, encompassing brand tone, visual elements, and audience alignment, thus offering a scalable alternative to traditional consultancy services. We conducted a pilot study involving two participants, demonstrating positive outcomes, showing that LLM-generated brand identity guidelines were relevant and consistent and provided valuable support in the branding process. © 2025 IEEE.

关键词： AI Brand Identity LLM Personalization

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Query based biomedical document retrieval for clinical information access with the semantic similarity

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Multimedia Tools and Applications 2024年第18期83卷 55305-55317页

作者： Gupta, Supriya Sharaff, Aakanksha Nagwani, Naresh Kumar Nagpur India Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India

The amount of exploration done for the available medical literature is quite less and at the same time, there is less awareness of information mining in this specific field. The accessibility of immense quantity of biomedical literature has opened up additional opportunities to apply Information Retrieval and NLP methods for mining existing archives. Therefore, a query based retrieval application (QBR) based on hybrid similarity of string and semantic similarity can help medical professionals in their ongoing research. There are multiple benefits of utilizing various NLP applications, for example, information retrieval engine, and clinical diagnosis frameworks for decision support in medical field. These applications depend on the capacity to gauge Hybrid textual similarity (HTS) and N-Gram similarity. Hybrid similarity is the combination of weighting function and word embedding models providing similarity scores with optimum results. In this work, the main focus is on building of a new biomedical document retrieval model which can pull relevant literature for clinical decision support system based on the specific query. There is also an attempt to compare the statistical and NLP based approaches of query based biomedical document retrieval with the baseline systems. Analysis of the proposed method inclusive of semantic word embeddings shows promising results for both of the suggested similarities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Information retrieval

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Classification of the respiratory infection vaccination tweets using fuzzy logic and deep learning

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Multimedia Tools and Applications 2024年 1-25页

作者： Bhimavarapu, Usharani Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Respiratory diseases are contagious and immensely affect all aspects of people and spread through the air or direct contact. COVID-19 is one of the most dangerous respiratory infections, and it has exaggerated many countries. The battle to curb its spread was waged in every country, even with few or no infections. Vaccination is one of the most vital to fight against COVID-19, and it started in India in January 2021. Every country's government has created awareness programs about COVID-19 and its updates through messages and videos on social media to reduce misconceptions and panic that followed due to the outright misinformation about COVID-19 and its impacts. This study classifies the medical vaccination tweets related to COVID-19;we extracted the tweets regarding vaccination in India from 1 January 2021 to 31 December 2021. We classified the tweets into four categories: pro-vaccine, anti-vaccine, hesitancy and cognizant. We performed the text summarization using fuzzy logic and classification using the stacked ANN and compared the results using the different word embedding models. During the vaccination period, we identified that allergy is a general topic discussed by individuals in social media through quadratic discriminant analysis. The proposed model surpassed the results of the baseline models and achieved an accuracy of 96.7%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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A Deep Learning Approach to Classify Skin Cancer

A Deep Learning Approach to Classify Skin Cancer

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Sharma, Mansi Kumar, Praveen Department of Computer Science and Design Wardha India Department of Computer Science and Medical Engineering Wardha India

ISBN: (纸本)9798331508432

A effective treatment for skin cancer depends on early discovery, which may be a major around the world health issue. This study offers a interesting strategy for dependably classifying skin injuries as benign or malignant by utilizing convolutional neural networks (CNNs). Convolutional Neural Networks (CNNs) are a promising strategy with incredible potential for skin cancer screening. CNNs are well-known for their capacity to observe complex designs from picture data and are able of accurately recognizing benign or malignant skin injuries. Analysts can make exceptionally valuable demonstrative devices by utilizing enormous datasets of skin injury photos to prepare CNN models. Methods like information augmentation, ensemble approaches, transfer learning, attention mechanisms, and generative ill-disposed systems can be utilized to make strides execution indeed encourage. Besides, making mobile and cloud-based apps and examining logical AI can progress the impact and openness of skin cancer diagnosis. CNNs are idealize for this reason because of their reputation for being able to extract complex patterns from visual information. Our approach involves employing a sizable dataset of photographs of skin injuries that have been meticulously labeled to prepare a CNN show. The demonstrate is planning to secure discriminative properties, such as asymmetry, uneven borders, color change, distance across, and creating characteristics, that distinguish benign from malignant tumors. The results of our tests appear how well our proposed CNN-based strategy works to distinguish skin cancer at high accuracy rates. The demonstrate ceaselessly beats out conventional machine learning approaches, illustrating the benefits of profound learning strategies in this field. This ponder progresses the field of dermatology's computer-aided conclusion, giving a possibly valuable instrument to assist therapeutic specialists distinguish and treat skin cancer early on. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing photo-to-anime translation with prestyled paired datasets

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Multimedia Tools and Applications 2024年第41期83卷 89393-89414页

作者： Chang, Chuan-Wang Dharmawan, Pratamagusta Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan

Animation is a widespread artistic expression that holds a special place in people's hearts. Traditionally, animation creation has relied heavily on manual techniques, demanding skilled drawing abilities and a significant amount of time. For instance, many Japanese anime films draw inspiration from real-world settings, requiring access to relevant references and artists capable of translating them into anime visuals. Consequently, the development of technology that automatically converts images into anime holds great significance. Numerous methods for style transfer have been developed using unsupervised learning and have achieved impressive results. However, unsupervised learning methods suffer when the image contains multiple styles within itself because they learn the style of the image globally. To solve this problem, we propose splitting these styles within the image into multiple classes: sky, buildings, greenery, water, and other objects. Then, we style these separated classes using existing image-to-image translation models. Finally, we train a pix2pix model to learn image style transfer in a paired manner. The experimental results show that the images are effectively styled into the resulting anime-styled image domain with comparable results to existing unsupervised learning GAN-based methods. The proposed method can effectively transfer the style from real-world photos into the anime-styled image domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Unsupervised learning

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A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

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computers, Materials & Continua 2023年第1期74卷 279-291页

作者： Iftikhar Ahmad Ambreen Shahnaz Muhammad Asfand-e-Yar Wajeeha Khalil Yasmin Bano Department of Computer Science and Information Technology University of Engineering and TechnologyPeshawar25120Pakistan Department of Computer Science Shaheed Benazir Bhutto Women UniversityPeshawar25000Pakistan Center of Excellence in Artificial Intelligence Department of Computer ScienceBahria UniversityIslamabadPakistan

The demand for cloud computing has increased manifold in the recent *** specifically,on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing *** cloud service provider fulfills different user requirements using virtualization-where a single physical machine can host multiple *** virtualmachine potentially represents a different user environment such as operating system,programming environment,and ***,these cloud services use a large amount of electrical energy and produce greenhouse *** reduce the electricity cost and greenhouse gases,energy efficient algorithms must be *** specific area where energy efficient algorithms are required is virtual machine *** virtualmachine consolidation,the objective is to utilize the minimumpossible number of hosts to accommodate the required virtual machines,keeping in mind the service level agreement *** research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized *** online algorithm is analyzed using a competitive analysis *** addition,an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark *** proposed online algorithm consumed 25%less energy and performed 43%fewer migrations than the benchmark algorithms.

关键词： Cloud computing green computing online algorithms virtual machine migration

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Learning Group Actions on Latent Representations 38

Learning Group Actions on Latent Representations

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Yinzhu Shrivastava, Aman Fletcher, P. Thomas Department of Computer Science University of Virginia United States Department of Electrical and Computer Engineering Department of Computer Science University of Virginia United States

In this work, we introduce a new approach to model group actions in autoencoders. Diverging from prior research in this domain, we propose to learn the group actions on the latent space rather than strictly on the data space. This adaptation enhances the versatility of our model, enabling it to learn a broader range of scenarios prevalent in the real world, where groups can act on latent factors. Our method allows a wide flexibility in the encoder and decoder architectures and does not require group-specific layers. In addition, we show that our model theoretically serves as a superset of methods that learn group actions on the data space. We test our approach on five image datasets with diverse groups acting on them and demonstrate superior performance to recently proposed methods for modeling group actions. © 2024 Neural information processing systems foundation. All rights reserved.

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