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An Extensive Analysis of Breast Cancer detection using Deep Learning Algorithms 3

An Extensive Analysis of Breast Cancer detection using Deep ...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Manoj, Milan Rajan, Akshay Santhosh, Gouri Anjali, T. Sankar, Panchami Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Symbiosis Institute of Health Sciences Symbiosis International University Pune India

ISBN: (纸本)9798331532420

Breast cancer remains a significant global health concern around the world, where early and accurate detection is essential for improving patient outcomes. This work aims to outdo the traditional diagonosis methods by employing three deep learning models: a fully connected neural network (FCNN), a convolutional neural network (CNN), and a feedforward neural network (FNN). Each model was analyzed for its ability to distinguish between benign and malignant cases using minimal feature variables. Among the models, the FNN showed higher validation accuracy compared to the CNN, which, while proficient at detecting complex patterns, was less effective in structured data. The FCNN, with added dense layers, performed similarly to the FNN, suggesting that the inclusion of dense layers did not significantly enhance the model’s accuracy. This research presents an innovative comparison to recent studies, demonstrating the flexibility and adaptability of neural networks for breast cancer diagnostics. The models’ ability to improve detection accuracy can significantly impact healthcare by providing reliable tools for early intervention. The findings contribute to the growing body of evidence supporting the integration of artificial intelligence into clinical practice, paving the way for more personalized treatment plans, reduced misdiagnosis rates, and overall improved patient care outcomes in cancer detection and treatment. © 2024 ieee.

关键词： Neural network models

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Secure and Fast Query Approach for High-Precision Multi-dimensional Satellite Remote Sensing data 1

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7th international conference on cognitive computing (ICCC) Held as Part of the Services conference Federation Meeting (SCF)

作者： Cheng, Zhengxiang Mao, Weixuan Wen, Ruwang Mei, Zhuolin Wu, Bin Shi, Jiaoli Cheng, Xiao Jiujiang Univ Sch Comp & Big Data Sci Jiujiang Jiangxi Peoples R China Jiujiang Key Lab Network & Informat Secur Jiujiang Jiangxi Peoples R China Jiujiang Univ Inst Informat Secur Jiujiang Jiangxi Peoples R China

ISBN: (数字)9783031518263

ISBN: (纸本)9783031518256;9783031518263

High-precision satellite remote sensing data provides rich and accurate Earth observation information that can be used for various remote sensing applications. In this era of edge computing with the Internet of Things at its core, network edge devices generate massive amounts of real-time data. However, network edge data, such as remote sensing data, may contain sensitive information. Therefore, ensuring the security of massive remote sensing data while providing fast and secure retrieval has become a challenge. However, many existing solutions only address problems in single-dimensional remote sensing data scenarios, while others are not efficient. Therefore, we propose a secure and fast query approach for high-precision multi-dimensional satellite remote sensing data (SFQA) to address the efficiency and security issues of querying multi-dimensional remote sensing data. In SFQA, we use more efficient encoding techniques to replace the complex and time-consuming encryption algorithms used in traditional methods. Additionally, we construct a secure index to achieve secure and efficient querying of massive high-resolution multi-dimensional satellite remote sensing data. Experimental results and analysis show that the SFQA method performs efficiently in querying high-resolution multi-dimensional satellite remote sensing data. Furthermore, our security analysis confirms that no external entity can access or obtain any additional information throughout the entire query process, ensuring the confidentiality and privacy of remote sensing data.

关键词： high-precision remote sensing data edge computing multi-dimension privacy querying

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Paid User data Mining Approach for a System to Assist with Volunteering for the College Entrance Examination 13

Paid User Data Mining Approach for a System to Assist with V...

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13th international conference of Information and Communication Technology, ICTech 2024

作者： Gao, Cen Wang, Sheng Fu, Lijun Zhang, Yonghong Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Jinan250100 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China Institute of Future Artificial Intelligence Technology and Innovative Applications Beijing100025 China

ISBN: (纸本)9798350376258

Addressing the dilemma that the current paid user mining techniques are complex and unsuitable for some specific applications, the present study suggests a rule-based paid user mining approach to streamline the implementation process. The main process consists of a buried design to produce JSON format logs detailing user usage. These logs are obtained as raw data in topics created with Kafka. Real-time user behavior data is then analyzed, using Spark Streaming computation engine, and matching calculation is executed based on predefined rules to determine a likelihood threshold for the conversion of nonpaying users to paying users. If the conversion probability threshold surpasses the preset level, the system identifies the user as a potential paying customer. Consequently, a message is delivered to the customer based on the outcome. The system might configure the coupon amount contingent upon different thresholds and distinct specified indications. Additionally, it persists in updating the customer's labeling status, ensuring adequately responsive, targeted advertising strategies are present at each customer visit. This method doesn't require intricate machine learning algorithms, making implementation more straightforward and providing crucial technical support for subsequent user operations. The implementation of this design improves the likelihood of user payments and boosts product profits. ©2024 ieee.

关键词： Sales

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Deep Reinforcement Learning Based Path Planning and Collision Avoidance for Smart Ships in complex Environments 2

Deep Reinforcement Learning Based Path Planning and Collisio...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Zhou, Li Gao, Pan Zhao, Xu Sichuan University Business School Chengdu China China Three Gorges University School of Economics and Management Yichang China

ISBN: (纸本)9798350368888

The port and shipping industry urgently needs to accelerate the transition to green and intelligent technologies in the post-pandemic era. Ship collision avoidance remains a pivotal challenge in achieving intelligent navigation. This paper proposes a deep reinforcement learning-based method for ship collision avoidance path planning in dynamic environments. Initially, a two-dimensional grid-based spatial environment is established to model ship domains, with collision risk levels assessed based on Automatic Identification System (AIS) data and the international Regulations for Preventing Collisions at Sea (COLREGs). The ship collision avoidance problem is formulated as a Markov Decision Process (MDP), wherein the observation space, action space, and reward function during collision avoidance are clearly defined. Utilizing this MDP framework, the Dueling Double Deep Q-network (D3QN) algorithm is employed to derive collision avoidance decisions. The algorithm incorporates prioritized experience replay and an adaptive decay greedy exploration strategy to enhance training efficiency. Simulation experiments are conducted across multiple encounter scenarios under collision regulations. The results substantiate the effectiveness of the proposed deep reinforcement learning approach for ship collision avoidance path planning. © 2024 ieee.

关键词： Deep reinforcement learning

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Leveraging Big data Analysis in Real-Time Automation for Improved Operational Efficiency 15

Leveraging Big Data Analysis in Real-Time Automation for Imp...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Puthillath, Beena Wasatkar, N.N. Albattah, Mahmoud M.S. Vijayalakshmi, V.J. Manjunath, C. Verma, Lokesh Department of EEE Department of computer engineering Vishwakarma Institute of Technology Pune India Department of Civil Engineering School of Engineering Jordan University Jordan Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

This idea has redefined the practices of an organization and revolutionized business decision-making by harnessing extensive data analysis. With industries becoming increasingly complex and data-dependent, the demand for real-time automation has taken an immense upsurge to enhance operational efficiency. Real-time automation uses massive big data analysis to ensure that the continuous monitoring and analyzing of your crucial data takes place in real time, allowing you to make timely decisions based on facts. It is a type of automation that helps an organization recognize and act on issues or opportunities as soon as they occur in real time, shortening downtime due to potential problems while increasing productivity. Automating data collection, processing, and analysis lets organizations better understand their operations and spot trends, patterns, or even outliers that might not be visible, given how cumbersome the manual processes are. In practice, real-time automation simplifies the process to enhance operational efficiency with cost savings and time efficiencies through automated processes that eliminate manual tasks. It also makes organizations adjust quickly to changes in markets or customer requirements, thus making them agile and more competitive. Utilizing real-time automation with the help of extensive data analysis can boost companies' competitive advantage by increasing their operational efficiency and decision-making capabilities. ©2024 ieee.

关键词： Efficiency

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Unlocking the Potential of Deep Neural Networks with Tuned Particle Swarm Optimization for Firewall Log data Analysis: Empowering Network Security Insights

Unlocking the Potential of Deep Neural Networks with Tuned P...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Verma, Pratibha Sahu, Sanat Kumar Bilaspur India Durg India

ISBN: (纸本)9798350373783

In today's world, it is vital and in trend to evaluate the firewall of computer networks and control internet traffic based on the research findings. A firewall is an excellent tool that ensures traffic control during the machines communicating on a network. It uses methods defining the traffic according to a set of precise standards and thus helps prevent cyber-attacks which may be costly for an organization. In this paper, we suggested an intelligent method for the classification of incoming and outgoing firewall travel packets. The log files in this research are classified using Deep Neural Network (DNN) classifiers. This paper introduces Tuned Particle Swarm Optimization (TPSO), an FST for reducing the number of variables in log file datasets. The TPSO proposed model is based on PSO, with the Naive Bayes (NB) classifier serving as the objective function. The approach involves using DNN as a classification model and Tuned PSO as a feature selection technique to enhance the classification performance. The TPSO algorithm is employed to optimize the selection of relevant features from the log-file dataset, aiming to improve the accuracy and efficiency of the classification process. By integrating DNN and TPSO, the study aims to leverage the power of deep learning for capturing complex patterns in the log-file dataset, while also utilizing the optimization capabilities of TPSO to select the most informative features for classification. The combined approach seeks to improve the accuracy and effectiveness of the classification task on the logfile dataset, potentially leading to enhanced insights and decision-making based on the classified log data. © 2024 ieee.

关键词： Particle swarm optimization (PSO)

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Enabling High-Throughput Parallel I/O in Particle-in-Cell Monte Carlo Simulations with openPMD and Darshan I/O Monitoring

Enabling High-Throughput Parallel I/O in Particle-in-Cell Mo...

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2024 international conference on Cluster computing

作者： Williams, Jeremy J. Medeiros, Daniel Costea, Stefan Tskhakaya, David Poeschel, Franz Widera, Rene Huebl, Axel Klasky, Scott Podhorszki, Norbert Kos, Leon Podolnik, Ales Hromadka, Jakub Narwal, Tapish Steiniger, Klaus Bussmann, Michael Laurel, Erwin Markidis, Stefano KTH Royal Inst Technol Stockholm Sweden Univ Ljubljana LeCAD Ljubljana Slovenia CAS Inst Plasma Phys Prague Czech Republic Helmholtz Zentrum Dresden Rossendorf Dresden Germany Lawrence Berkeley Natl Lab Berkeley CA USA Oak Ridge Natl Lab Oak Ridge TN USA Max Planck Comp & Data Facil Garching Germany

ISBN: (纸本)9798350383461;9798350383454

Large-scale HPC simulations of plasma dynamics in fusion devices require efficient parallel I/O to avoid slowing down the simulation and to enable the post-processing of critical information. Such complex simulations lacking parallel I/O capabilities may encounter performance bottlenecks, hindering their effectiveness in data-intensive computing tasks. In this work, we focus on introducing and enhancing the efficiency of parallel I/O operations in Particle-in-Cell Monte Carlo simulations. We first evaluate the scalability of BIT1, a massivelyparallel electrostatic PIC MC code, determining its initial write throughput capabilities and performance bottlenecks using an HPC I/O performance monitoring tool, Darshan. We design and develop an adaptor to the openPMD I/O interface that allows us to stream PIC particle and field information to I/O using the BP4 backend, aggressively optimized for I/O efficiency, including the highly efficient ADIOS2 interface. Next, we explore advanced optimization techniques such as data compression, aggregation, and Lustre file striping, achieving write throughput improvements while enhancing data storage efficiency. Finally, we analyze the enhanced high-throughput parallel I/O and storage capabilities achieved through the integration of openPMD with rapid metadata extraction in BP4 format. Our study demonstrates that the integration of openPMD and advanced I/O optimizations significantly enhances BIT1's I/O performance and storage capabilities, successfully introducing high throughput parallel I/O and surpassing the capabilities of traditional file I/O.

关键词： openPMD Darshan ADIOS2 Parallel I/O Efficient data Processing Distributed Storage Large-Scale PIC Simulations

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A Lightweight AI Model for Anomaly Detection in Wireless Networks 2

A Lightweight AI Model for Anomaly Detection in Wireless Net...

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2nd international conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho National Laboratory Ai Data & Visualization Idaho FallsID United States

ISBN: (纸本)9798331529529

Detecting network anomalies is critical for wireless network security and reliability. Traditional AI methods often require substantial computational resources, particularly when deployed on cloud servers, leading to increased network load and latency. In contrast, deploying AI models directly on end devices enables real-time data processing at the source, facilitating faster anomaly detection and mitigation. We propose a lightweight hierarchical AI model where initial inferences are made by a simple model and outputs confidence scores. Using a modified TOPSIS-based method, we determine an optimal confidence score threshold. Inferences below this threshold are forwarded to a more complex model for accurate analysis, reducing overall computational demands while maintaining high detection performance. Our approach is tested on a 5 G network dataset, demonstrating competitive performance compared to other anomaly detection models. © 2024 ieee.

关键词： 5G mobile communication systems

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Enhancing Apple Disease Identification in Multi-Foliage Images through Model Fusion and Transfer Learning 10

Enhancing Apple Disease Identification in Multi-Foliage Imag...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Tambe, Utkarsh Yashwant Shanthini, A. Sugumaran, Vijayan Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Oakland University Department of Management Information Systems Rochester United States

ISBN: (纸本)9798350384369

In this study, a novel technique for scab, rust, and other various apple diseases detection in photos with several leaves within a single frame is presented. We employed ResNet50 and DenseNet121 models for accurate illness categorization by leveraging transfer learning. An inventive ensemble architecture combines the predictions from the two models to increase accuracy, improving classification performance. The great effectiveness of this ensemble technique is demonstrated by experimental findings on a large dataset, which outperform standalone models with accuracy of 97.81% and Cohen's Kappa of 0.97. Our method shows the effectiveness of transfer learning in complex agricultural contexts while also highlighting the potential of ensemble techniques to improve disease identification. Our study makes a contribution to digital agriculture by tackling the problem of detecting many diseases across several apple leaves in a single image, enabling improved crop disease management techniques with improved accuracy as compared to previous works in this domain. A potential path towards sustainable and effective agricultural practices is provided by the combination of deep learning and ensemble approaches, which is especially important in a world where the demand for food is rising. © 2024 ieee.

关键词： Adversarial machine learning

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Natural Language Processing and Collocations for Generating Enhanced Health data Insights 4

Natural Language Processing and Collocations for Generating ...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Nooji, Pavitha Savithramma, R.M. Ashwini, B.P. Pinto, Merwin Vishwakarma University Department of Artificial Intelligence Maharashtra Pune India Siddaganga Institute of Technology Vtu Department Of Cse Karnataka India

ISBN: (纸本)9798331529635

This research study explains the key factors of how Natural Language Processing and Collocations are used to improve the Health industrial sector by detecting medical terms and words to enhance healthcare industrial applications. Collocations are types of words that are seen very commonly appear together in a text corpus, each of them contributes to relationships between various medical terms for example a disease name or even a symptom occurrence, natural language processing techniques involve creating tokens, removing stopwords, tagging various types of speeches, lemmatizing words, or even stemming for an instance. The model employs a statistical method such as Pointwise Mutual Information or Chi-square to identify meaningful pairs of words in the medical context. Various metrics such as precision, recall, and F1 scores gave a good insight of the model performance to predict the terms in the medical text. The model would capture all expected complex medical terms and help gain proper insights from the medical context by especially picking out the collocations. The model is known for its relevance making it a powerful tool for healthcare units and advancing healthcare research. © 2024 ieee.

关键词： Natural language processing systems

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