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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Samantaray, Ruturaj Afrin, Karishma Behera, Bichitrananda Bag, Kirtimayee Tata Consultancy Services Limited Tcs BaNCS Bangalore560066 India C. V. Raman Global University Dept. of Computer Science and Engineering Bhubaneswar752054 India

ISBN: (纸本)9798350355093

The global prevalence of skin cancer has seen a concerning increase over recent decades, now accounting for one in every three cancer cases worldwide. The unchecked growth of aberrant cells in the skin can lead to skin cancer, a potentially fatal illness. There are different types of skin cancer, namely, DF (Dermatofibroma), AKIEC (Actinic Keratoses and Intraepithelial Carcinoma), Mel (Melanoma), BCC (Basal Cell Carcinoma), VASC (Vascular Lesions), BKL (Benign Keratosislike Lesions), and NV (Melanocytic Nevi). Skin cancer can spread to nearby tissues in our body if it is not caught early. Presently, diagnosing skin cancer is a time-consuming and financially burdensome process for both patients and healthcare systems. Moreover, diagnosing skin cancer heavily relies on human cell detection. Skin cancers can be dangerous if they are not treated before they have a chance to spread. However, existing technology lacks the ability to detect skin cancer accurately. Traditional machine learning algorithms display limitations when classifying different classes of skin cancer. In this research, we have proposed skin cancer detection with classification using CNN and ensemble learning. CNN, known as the Convolutional Neural Network, represents a specialized architecture within deep learning algorithms primarily tailored for image recognition and feature extraction. It comprises convolutional, pooling, and fully connected layers in its mathematical structure. In this work, CNN (Convolutional Neural Network) is employed for image feature extraction, followed by utilizing three distinct classifier algorithms, namely, SVM (Support Vector Machine), KNN (K-Nearest Neighbour), and DT (Decision Tree), integrated through an ensemble approach utilizing a voting system. Despite utilizing multiple classifiers, the model exhibits impressive performance, achieving a classification accuracy of 98%. Notably, our research leverages the HAM10000 dataset for this study. © 2024 IEEE.

关键词： Convolutional neural networks

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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs

Cost-balanced Adaptive Replica Server Cache Management under...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rohit Kumar Gupta Arka Prokash Mazumdar Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache.

关键词： Cloud computing Adaptation models Costs Content distribution networks Machine learning Predictive models Servers Resource management Time-varying systems

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Modeling Data Flows of a System for Analyzing Messages in Social Networks

Modeling Data Flows of a System for Analyzing Messages in So...

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2022 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2022

作者： Bekeneva, Yana A. Lopushansliy, Alexandr M. Kulikov, Alexandr I. Saint Petersburg Electrotechnical University Leti Dept. of Computer Science and Engineering St-Petersburg Russia

ISBN: (数字)9781665488709

ISBN: (纸本)9781665488709

There is a problem associated with an overabundance of information: users write hundreds of posts every day, leave thousands of comments, which makes it difficult to search, structure and analyze useful data. Especially in the case of creating a distributed network, it is also important to calculate the load on the network so that it works smoothly and as quickly as *** paper describes the creation of a system for modeling data flows of a message analysis system in social networks with the ability to change the composition and structure of a distributed network to estimate the amount of transmitted traffic with a different number of processing points for incoming data. © 2022 IEEE.

关键词： Data transfer

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Detecting Retinal Damage From OCT Images

Detecting Retinal Damage From OCT Images

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International Conference on Computing and Networking Technology (ICCNT)

作者： Kundrapu Vineetha Md Jaffer Ali Narisetty Prathima Tripty Singh Prakash Duraisamy Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Computer Science and Engineering University of Wisconsin- Green Bay Wisconsin USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Optical coherence tomography (OCT) is a crucial imaging modality in ophthalmology, offering detailed retinal images essential for diagnosing various retinal disorders. In this study, we investigate the application of convolutional neural networks (CNNs) and transfer learning to classify retinal OCT images. We compare a custom CNN model with two pretrained models, MobileNet and VGG16, to explore their effectiveness in categorizing retinal diseases. Our study focuses on improving classification accuracy through image enhancement techniques, training methodologies, and model evaluation using confusion matrices. The results highlight the VGG16-based CNN model as the most effective, demonstrating its potential for accurate classification of retinal disorders based on OCT images. Furthermore, we delve into strategies to further enhance the performance of these models, such as data augmentation, finetuning, and hyperparameter tuning. We discuss the importance of these techniques in optimizing model performance and reducing overfitting. Additionally, we emphasize the significance of evaluating models using confusion matrices to gain insights into their performance across different classes. Overall, our findings suggest that CNNs, particularly the VGG16based model, hold promise for improving the automated diagnosis of retinal diseases using OCT imaging, which could lead to better patient outcomes in ophthalmology.

关键词： Training Accuracy Optical coherence tomography Transfer learning Retina Feature extraction Ophthalmology Convolutional neural networks Tuning Diseases

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FusedMAC Framework Analysis for Enhanced Animal Condition Monitoring

FusedMAC Framework Analysis for Enhanced Animal Condition Mo...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Al Rafi Aurnob M D Rokon Islam Emon Tonmoy Mohajan Dipta Barua Rupom Razzaque Kamruddin Nur Dept. of Computer Science American International University-Bangladesh Dhaka Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Dept. of Computer Science & Engineering International Islamic University Chittagong Chittagong Bangladesh School of Electrical Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The integration of advanced technologies especially with machine learning has the potential to revolutionize animal condition monitoring by improving animal welfare and health status through comprehensive condition monitoring across the agricultural and veterinary domains as well as wildlife conservation. This study focuses on Multiple Additive Regression Trees (MART) and CatBoost as well as the fusion techniques. The proposed FusedMAC framework analyzes complex and high-dimensional data. Stacking fusion techniques aim to provide a comprehensive understanding of animal conditions by integrating diverse data modalities. Also, the use of the explainable artificial intelligence (XAI) methods, called SHAP (Shapley Additive Explanations). This technique ensures the transparency and interpretability of the model’s predictions. Our proposed approach addresses key gaps in previous research, including the need for advanced machine learning integration, robust data fusion and real-world validation. This study contributes to more effective animal management practices by enhancing predictive accuracy and interpretability. Ultimately, improving animal welfare and health outcomes.

关键词： Condition monitoring Additives Animals High dimensional data Prevention and mitigation Wildlife Stacking Data integration Predictive models Regression tree analysis

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Variational Perturbation Personalized Federated Learning via Prior-Posterior Distance

Variational Perturbation Personalized Federated Learning via...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hefeng Zhou Yuanbin Wang Jun Wang Jiong Lou Wugedele Bao Chentao Wu Jie Li Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Dept. of Electronic Engineering Shanghai Jiao Tong University Shanghai China Dept. of Computer Science University of Warwick Coventry United Kingdom School of Computer Science Hohhot Minzu College Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Personalized Federated Learning (pFL) mitigates the impact of statistical heterogeneity on FL architecture to some extent by allowing participants to use personalized models based on local data distributions. The existing pFL methods optimize from the perspective of model structure, attempting to adopt strategies that maintain model processing or quickly adapt to local data distribution capabilities. Our proposed method draws inspiration from the concept of variational inference, guiding model updates by comparing prior and posterior data distributions, and innovatively applying model variational perturbations to improve robustness. Finally, we conducted multidimensional experiments and the results show that our method outperforms the current baseline. Code: https://***/RezinChow/VPFL.

关键词： Adaptation models Codes Federated learning Perturbation methods Current measurement Signal processing Data models Robustness Speech processing Optimization

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Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather

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Procedia computer science 2024年 235卷 2258-2268页

作者： Arpita Maharatha Ratnakar Das Jibitesh Mishra Soumya Ranjan Nayak Srinivas Aluvala Dept. of Computer Science & Application Odisha University of Technology and Research Bhubaneswar 751029 India School of Computer Engineering KIIT Deemed to be University Bhubaneswar 751024 Odisha India Department of Computer science and artificial intelligence SR University Warangal Telangana -506371 India

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today’s world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature.

关键词： LSTM CNN Weather Forecasting Temperature

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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Machine Learning Approaches for Predicting Lung Cancer Risk and Early Diagnosis 2

Machine Learning Approaches for Predicting Lung Cancer Risk ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Jegan, K. Nikesh, Infant Pravin, A. Sathyabama Institute of Science and Technology Dept of Computer Science and Engineering Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331505745

Using a healthcare dataset, this study applies a variety of ML techniques to analyze and forecast the likelihood of lung cancer. This dataset will be initialized by cleaning, encoding categorical variables, and removing duplicates to ensure quality and efficiency. Correlation analysis is carried out to find important characteristics that raise the risk of lung cancer. engineering including interaction terms enhances the predictive efficiency of the dataset. The various machine learning models are introduced they are Logistic Regression, SVC, Random Forests, GBM, and XGB. Over-sampling with ADASYN addresses class imbalance to improve model training. The logistic regression machine learning model has attained 97% accuracy. It will ensure consistency and reliability. Comprehensive visualizations such as heatmaps and bar plots, explore feature correlations and class distributions. The study represents the efficiency of ensemble methods like Random Forests and XGBoost, achieving superior predictive performance compared to baseline models. This system predicts whether they might have lung cancer, helping doctors to diagnose or early intervention. © 2025 IEEE.

关键词： Visualization

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Hy-BTree: An efficient Tree based topology for FPGA based NoC implementation 7

Hy-BTree: An efficient Tree based topology for FPGA based No...

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7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021

作者： Prabhu Prasad, B M Parane, Khyamling Talawar, Basavaraj IIIT Dharwad Dept. of Computer Science and Engineering India NITK Surathkal Dept. of Computer Science and Engineering India

ISBN: (纸本)9781665428491

Due to their hierarchical structure, Binary Tree (BTree) topology can be employed in Network-on-Chip (NoC) applications. Because of its lower bisection bandwidth, the performance degradation is observed in communication intensive applications. The Fat tree topology has been proposed to overcome the disadvantages of the BTree topology. But, the complexity of the Fat Tree topology's router becomes more complicated as we move towards the root node of the tree and occupying a huge amount of hardware resources compared to the BTree variant. Instead of going for Fat Tree topology, the number of hops taken by a packet in the BTree topology can be reduced by introducing new links in the network with an increase in the bisection bandwidth. In this work, we propose a variant of BTree topology called Hy-BTree by introducing additional links at the intermediate levels of the network to reduce the number of hops taken for the communication. The proposed design is implemented on the FPGA and compared with the other topologies from state-of-the-art the FPGA based NoC architectures. A reduction in average latency and an improvement in throughput have been observed in Hy-BTree with respect to the BTree network with negligible overhead. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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