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A Convnet Model-Based Deep Learning Approach for Pneumonia Recognition from Chest X-ray Images

A Convnet Model-Based Deep Learning Approach for Pneumonia R...

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International Conference on Advances in Information technology (ICAIT)

作者： Venkateswarulu Naik. B Bhuvan Unhelkar Prasun Chakrabarti G S Pradeep Ghantasala Pellakuri Vidyullatha Meghana Pyla Department Of Information Systems And Decision Sciences Muma College Of Business University of South Florida South Florida USA Department of Computer Science and Engineering Sir Padampat Singhania University Udaipur Rajasthan lndia Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Pneumonia diagnosis using chest X-ray (CXR) images shows a decisive part in early detection and handling of the disease. Within this research, proposes a convolutional neural network (ConvNet) model for automated pneumonia diagnosis based on CXR images. Our model leverages the deep learning capabilities of ConvNets to analyze features in CXR images and categorize them as pneumonia-positive or pneumonia-negative. We conducted experimentations on a comprehensive dataset of CXR images, achieving auspicious outcomes in terms of efficiency and accuracy. The proposed ConvNet model demonstrates prospective for an accurate and appropriate pneumonia diagnosis, contributing to improved healthcare outcomes and resource optimization in medical settings. ConvNet model is implemented using tensorflow keras with preprocessing using Image data generator. The models are deployed like sequential, Conv2D with tuning parameters of Relu, dropout, max pool 2d and adam optimizer. For training the images with 150, 150 pixels, train sample as are 1000 and input shape is 3, epochs are 50, batch size is 20. Applications of the categorical cross-entropy loss function for multi-class division with Adam optimizer employed for parameter optimization. The Model trained for a stable amount of epochs through early based on validation loss toward prevent overfitting and learning rate, batch size, and dropout rate optimized through hyperparameter tuning.

关键词： Deep learning Training Analytical models Pneumonia Accuracy Shape Medical services

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Optimizing Vehicle-to-Vehicle (V2V) Communication Efficiency with KNN-based Dynamic Time slot Allocation

Optimizing Vehicle-to-Vehicle (V2V) Communication Efficiency...

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Social and Sustainable Innovations in technology and engineering (SASI-ITE), International Conference on

作者： N Devakirubai Anitha Velu D Sumathi A Prasanth Department of Artificial Intelligence and Data Science R P Sarathy Institute of Technology Salem Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350360790

ISBN: (纸本)9798350360806

In the continually evolving field of vehicular communications, the efficient allocation of time slots for Vehicle-to-Vehicle (V2V) communication is of utmost importance. This work introduces a novel approach that employs a K-Nearest Neighbors (KNN) algorithm to allocate time slots in NR V2X sidelink communications. The allocation is specifically tailored to dynamically adjust to the current density of traffic in real-time. The efficacy of the KNN-based time slot allocation system is assessed through simulating the V2X environment, encompassing a fleet of vehicles. The efficacy of the suggested methodology is assessed in relation to a random allocation technique and it is observed that the suggested strategy notably enhances allocation efficiency, slot utilization, contention window, and communication time. The findings illustrate the capacity of a KNN-based dynamic allocation system to adapt to different levels of traffic congestion, leading to the efficient utilization of network resources and the mitigation of communication latency. Furthermore, the examination of the implementation of priority queues in the allocation process is explored, resulting in enhanced efficiency of the system in scenarios involving a substantial amount of traffic.

关键词： Technological innovation Vehicular ad hoc networks Sidelink Nearest neighbor methods Dynamic scheduling Real-time systems Resource management

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Fruit and Vegetable Detection with Calorie Estimation Built on Mobilenetv2

Fruit and Vegetable Detection with Calorie Estimation Built ...

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International Conference on Advances in Information technology (ICAIT)

作者： Sengottaiayn N Hamsanandhini S Rajesh Sharma R Akey Sungheetha Monika M Ketema Adare Computer Science & Engineering Alliance University Bengaluru India Department of Computer Technology Kongu Engineering College Erode India MSc Software Systems Kongu Engineering College Erode India Department of Computer Science and Engineering Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Fruit and Vegetable Recognition with Calorie Estimation based on Mobilenetv2 is a pioneering research endeavor aimed at leveraging deep learning techniques to enhance dietary monitoring and health management. Building upon the success of neural network models in various domains, this study explores the application of Mobilenetv2 and EfficientNet architecture for accurately identifying fruits and vegetables from images and estimating their respective caloric content. The research dataset comprises meticulously curated images of diverse fruits and vegetables, ensuring comprehensive coverage across different categories. Through rigorous experimentation and evaluation, the proposed model demonstrates remarkable accuracy in fruit and vegetable recognition, achieving an impressive accuracy rate of 97.6%. Moreover, the incorporation of calorie estimation adds a novel dimension to dietary analysis, enabling users to make informed decisions regarding their nutritional intake. The findings of this research not only contribute to the advancement of computer vision techniques but also hold significant implications for personalized nutrition tracking and health- conscious applications.

关键词： Deep learning computer vision Accuracy Architecture Neural networks Buildings Estimation computer architecture Information technology Monitoring

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AES Algorithm for the Next Generation of 5G Network Encryption Standards 2

AES Algorithm for the Next Generation of 5G Network Encrypti...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Manojkumar, T. Tamilarasi, K. Mahalakshmi, S. Vinayagam, P. Mahanthesha, U. Mohanraj, S. Karpagam Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Excel Engineering College Department of Electronics and Communication Engineering Tamilnadu Namakkal India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Bnm Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350315905

The Advanced Encryption Standard (AES) symmetric key encryption algorithm is the industry standard for 5G communication encryption. technology advancements nowadays are mostly aimed at improving the security, speed, and efficiency of processors. The main expansion phase is made easier by the study's pipeline structure. This method simplifies the process of creating subkeys in AES encryption for use in 5G communication networks. There is a 37% increase in throughput between the simulated and implemented versions of the original design on FPGA Virtex4 XC4VLX200, and this difference may be attributed to the reduction in propagation latency while producing subkeys. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Synergistic CNN-Random Forest Model for Signature Recognition and Classification

Synergistic CNN-Random Forest Model for Signature Recognitio...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Anuj Kumar Jain G S Pradeep Ghantasala Vikrant Sharma Gaganpreet Kaur Rajesh Sharma R Akey Sungheetha Department of CSE Chandigarh University Mohali Punjab India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Computer Science & Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The abstract performance characteristics of a signature classifying hypothesis, including precision, recall, F1-Score, encouragement, support percentage, and accuracy for two different signature classification levels. To take a Signature Classification stage one, the proposed approach achieves remarkable accuracy, recall, and F1-Score ratings of 99.87%, 99.50%, & 99.68%, accordingly. These metrics show how amazingly well the algorithm can classify situations in this category. The robustness of the model is demonstrated by the support column, which has 4020 instances classified as Signature Classification Stage 1. With a significant value of 0.47, the support percentage indicates the class's contribution to the entire dataset. In a similar vein, Signature Classification Stage 2 exhibits excellent performance characteristics, with 99.77 percent accuracy, recall, and F1-Score. There is a greater support rate in this class since 4510 instances give 0.52 to the total dataset. The model demonstrates its ability to identify signals with a stunning 99.64% Small Average accuracy, which is the average precision over all cases. Both signature levels performed in balance, as indicated by the weighted average & macro average metrics, which are predicted to be 99.73% for F1-Score, 99.82% for accuracy, & 99.64% for recall. This demonstrates how the model succeeds in maintaining a high level of accuracy throughout the entire dataset in addition to predicting specific classes. With a 99.6483% overall accuracy, it is shown that the notion of signature classification helps classify signatures at different levels.

关键词： Measurement Weight measurement Accuracy Forestry Predictive models Stability analysis Robustness Data models Information and communication technology Diseases

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A Review: Threat Modeling in Cyber Security Through XDR 12th

A Review: Threat Modeling in Cyber Security Through XDR

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Shinde, Swapnil Kisan Deshmukh, Suruchi Parikshit Sharma, Parth Pradhan, Sagar Kshirsagar, Kundlik B. Gaikwad, Aarti S. Vishwakarma Institute of Information Technology Maharashtra Pune India MIT Art Design and Technology University Maharashtra Pune India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Rajasthan Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pune Pimpri India D. Y. Patil College of Engineering Maharashtra Pune Akurdi India

ISBN: (纸本)9789819601424

Given the sophistication of today’s attacks, cyber security has taken on more importance in the digital world. Conventional security measures are no longer sufficient;instead, a more sophisticated and reactive reaction is needed. In this regard, handling this escalating threat seems to depend on the integration of machine learning (ML) and artificial intelligence (AI). But even with their great efficacy, there remains a growing issue: integrating different data sources and technologies so that the overall security of the system model is maintained. With a special emphasis on Open Extended Detection and Response (Open XDR) technology, this paper provides a thorough analysis of the integration of AI and ML in cyber security. A thorough literature review, which looks at the interactions and functionality of several cyber security components, is the methodology employed. In the context of AI and ML, the paper also examines the function of Active Directory, a directory service for Windows domain networks, and the procedure known as log forwarding, which involves sending log files to a central server for analysis. The study explores the evolution of AI and ML, highlighting their applications in cyber security for sophisticated data processing, threat detection, and pattern identification. It examines how machine learning approaches, such as supervised learning (where the model is trained on labeled data) and unsupervised learning (where the model learns from unlabeled data), support cyber security protocols. The paper emphasizes Open XDR’s importance as a breaking invention that combines information from several sources to provide thorough security analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： Distributed ledger

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Experimental Demonstration for Energy Aware Optimization with Effective Neighbor and Link Selection in Vehicular Adhoc Networks 6

Experimental Demonstration for Energy Aware Optimization wit...

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6th Iraqi International Conference on engineering technology and its Applications, IICETA 2023

作者： Al-Dulaimi, Aymen Alkhazraji, Nejood Abbas, F.H. Alkhayyat, Ahmed H. R. Al Delfi, Hassan Hassoon College of Engineering Techniques Al-Farahidi University Department of Communication Technical Engineering Baghdad Iraq College of Information Technology Imam Ja 'Afar Al-Sadiq University Department of Computer Technical Engineering Al-Muthanna Iraq Al-Mustaqbal University College Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq College of Technical Engineering Islamic University Department of Computer Technical Engineering Najaf Iraq College of Engineering Tishk International University Tiu Interior Design Department Kurdistan Erbil Iraq

ISBN: (纸本)9798350303339

Commonly Vehicular Ad-hoc Network (VANET) technology is a highly active and huge mobility based network which needs a proper route settings, neighbor settings and stable link to transmit the data. Because vehicles in VANETs consist of huge mobility which is unpredictable that's leads to affect the network performance with the increase of delay and high energy consumption. To overcome such flaws in this network Energy Aware Optimization is proposed which concentrates on certain segments like network model, energy model, neighbor selection and link duration. Through the network and energy model the input energy allocations are clearly analyzed and the essential resources are properly assigned to the vehicles. Through neighbor selection process the residual area and border area of the vehicles are calculated and according to that the neighbors are assigned to the current vehicles. Finally the link duration process is mainly employed to increase the lifetime of the vehicles. The experimentation of the proposed work is done in NS2 and the parameters which are considered for the analysis are packet delivery ratio, end-to-end delay, routing overhead, and throughput whereas the baseline methods which are used for the comparative analysis are HACCS and RAPSO. From the comparison it is shown that the proposed method achieves high delivery ratio and throughput compared with the earlier methods and that leads to increase the network efficiency and lifetime. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Application of Internet of Things and Machine learning in improving supply chain financial risk management System 2

Application of Internet of Things and Machine learning in im...

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2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Kafila Kalyan, Nalla Bala Ahmad, Kamal Rahi, Fakhruddin Shelke, Chetan Mahabub Basha, S. Sr University School of Business Telangana Warangal India Department of Management Studies A.P Tirupati517507 India Department of Commerce Uttarakhand Srinagar Garhwal India Government Post Graduate College Department of Commerce Uttarakhand Bageshwar India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Chikkahagade Cross Chandapura Anekal Main Road Karnataka Bengaluru562106 India International Institute of Business Studies Department of Commerce Bangalore India

ISBN: (纸本)9798350381979

The incorporation of the Internet of Things (IoT) and machine learning (ML) methods has attracted significant interest in a variety of sectors in previous years. This article investigates the use of IoT and ML to improve supply chain financial (SCF) threat management. The supply chain is a complicated network with many key players, and financial threat management is important to its sustainability and success. IoT and ML possess inherent advantages in SCF due to their technology properties. They also have significant opportunities to build trust to solve big challenges in SCF, which aids financial development in the Tonkin Gulf area. This paper focuses on introducing the study on using Machine learning innovation in SCF in the Tonkin Gulf area and aims to offer suggestions on how supply chain finance could evolve there using Machine learning. This paper suggests supply chain finance game applications for pertinent investigations as well as blockchain innovation, supply chain banking threats assessment on the IoT and machine learning, and supply chain finance implementation study methodologies in the Tonkin Gulf region. © 2023 IEEE.

关键词： Internet of things

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Deep Learning Techniques for Overcoming Interoperability Challenges in Smart Grid Systems 2

Deep Learning Techniques for Overcoming Interoperability Cha...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Devi, M. Kumar Rai, Anjani Reddy, Avala Raji Neelima, Gogineni Obaid, Saif Chidambaranathan, Senthilnathan New Prince Shri Bhavani College of Engineering and Technology Department of EEE Tamil Nadu Chennai600073 India GLA University Department of Computer Engineering & Applications Mathura India Department of Mechanical Engineering CMR Technical Campus Telangana Hyderabad India GRIET Bachupally Department of CSE Telangana Hyderabad India College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq Virtusa Corporation Department of Architecture and Design New Jersey United States

ISBN: (纸本)9798350368109

The application of deep learning techniques to design and develop smart grid systems present opportunities to counteract interoperability barriers ultimately improving the smart grid usability, reliability, stability and security. This paper highlights the fairly new robust Deep Learning techniques and how they can be used to solve various smart grid interoperation challenges. In this regard, following a step-by-step approach, we to detail on how data are collected and pre-processed, as well as, how deep learning models are selected and trained with corresponding performance metrics made on how the same models can be deployed in some prominent applications such as;predictive maintenance, energy consumption prediction, and anomaly detection, and lastly in cybersecurity. In addition, we continue the description of works in the field of grids standardization and protocol to achieve unification of communication between grid entities. The findings shown here prove that deep learning can enhance computational grid administration, security, and interaction ideals. There is still a need to address the issues related to the complexity of deep learning models as well as their response to dynamic gird systems in the literature;Future work should also consider the applicability of new architectures in modern grid challenges like integration of renewable energy systems and impact of EVs. In conclusion, the application of deep learning as part of the techniques offers a promising approach to improving smart grid systems and moving towards the progressive development of energy infrastructure with increased reliability and efficiency. © 2024 IEEE.

关键词： Smart power grids

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