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A Comprehensive Object Detection Model Empowered with Deep Learning

A Comprehensive Object Detection Model Empowered with Deep L...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Gaddam, Ramani Bavanthula, Anudeep Reddy Asoori, Murali Krishna Chintakunta, Durga Prasad Volki, Anirudh Bv Raju Institute of Technology Computer Science & Engineering Dept Medak Telangana Narsapur India

ISBN: (纸本)9798350302684

The innovative image query interface system leverages cutting-edge deep learning techniques to achieve accurate and efficient image categorization. By utilizing meta pseudo labels, the system enhances classification accuracy, yielding notable outcomes of 88.4% on CIFAR-10 and 76.3% on ImageNet datasets. To enhance feature extraction, it combines fine-tuned Decomposition Attention (FDTA) and Divide to Attend (DTA), resulting in 08.3% accuracy for X-ray image detection. For user-friendliness, the system introduces an intuitive interface for quick photo querying. To enhance tiny object identification, it employs combinatorial optimization via the graphical interface DC-YOLOv8. This incorporates a Dual-Context Feature Fusion module with YOLOv8, enhancing the model's capability to discern intricate details and contextual cues. This system demonstrates deep learning's potential in computer vision, offering both accuracy and user-friendly functionality, contributing to the field's advancement. © 2023 IEEE.

关键词： Combinatorial optimization

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Towards Adoption of Green Blockchain with Emphasis on Blockchain Type, Consensus Protocols, Data Sharding and Smart Contracts 2

Towards Adoption of Green Blockchain with Emphasis on Blockc...

引用

2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Phansalkar, Shraddha Mishra, Deepa Chaube, Neha Sonkamble, Rahul MIT Art Design and Technology University Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350333763

Blockchain and related Distributed Ledger Technologies (DLT) are anticipated to transform a the world of web from a centralised document-sharing platform to a comprehensive decentralised platform that facilitates the exchange of digital currency and supports autonomous management of digital assets. The central server is susceptible to attacks, distrust and collusion. If the web can assure reliable, safe, and responsible updates among independent participants without the need for a centralized server, the perception of a decentralised web can be re-instantiated. One of the essential technologies required to restore the openness of the internet while maintaining its security is distributed ledger technology (DLT). DLTs may now totally handle business and legal transactions online, creating a more trustworthy and accountable environment. Blockchain technology marks a major breakthrough by removing the need for a centralised trusted authority in a widely distributed network. Instead, a consensus must be reached among several sources of trust, based on an algorithm, that this transaction may be believed to be legitimate. The consensus algorithms in blockchain technology offer an immutable and permanent record of a transaction that is immutable, trustworthy and secured. Consensus algorithms are however energy consuming because of their computation heavy nature. This has been a biggest inhibition towards blockchain adoption. The energy needs for committing a blockchain transaction is also governed by whether it is a public/permissioned blockchain, its consensus algorithm, onchain-offchain data and the code complexity of smart contracts. The paper presents a state-of art evaluation of the current blockchain platforms and cryptos and evaluate them with the energy consumptions. Additionally, it also proposes a framework architecture of a green blockchain application. A green blockchain refers to the implementation of environmentally sustainable algorithms, tools and platforms

关键词： Smart contract

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Performance Analysis of a Scalable Federated Learning-Based Agricultural Network 4

Performance Analysis of a Scalable Federated Learning-Based ...

引用

4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakraborty, Shatanik Sarkar, Maharnab Gayen, Avijit Jana, Angshuman Chakraborty, Nilotpal Meghnad Saha Institute of Technology Dept. of Computer Science & Engg. Kolkata India Govt. College Of Engineering & Textile Technology Dept. of Computer Science & Engg. Berhampore India Indian Institute of Information Technology Dept. of Computer Science & Engg. Guwahati India Techno India University Dept. of Computer Science & Engg. West Bengal India

ISBN: (纸本)9798350387063

Although agriculture is a substantial part of the Indian economy, it encounters several challenges. Precision agri-culture is a farming management strategy that aims to overcome these challenges. It optimizes several components of the farming process using data and technology, resulting in increased crop yields while reducing waste and raising total resource use effi-ciency. Thus, in its context, we present a study on the development of a Federated Learning-based Agricultural Network consisting of a popular federated learning algorithm FedAvg to perform image classification on an open dataset, Plant Village. In this work, we specifically analyze the performance of the proposed Federated Learning-based Agricultural Network in terms of its scalable deployment. The proposed network yields an average classification accuracy of 97.52 % on test data (C ≈ 70), and provides a host of other benefits it derives from its federated nature, such as upholding the privacy of local client data as only the model updates are shared between the clients and the aggregation server. The prime objective of our work has been to study and explore the possibility of applying federated learning in the agriculture sector so that more efficient and privacy- preserving smart farming networks could be developed. © 2024 IEEE.

关键词： Agribusiness

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A Comprehensive Survey on Learning Based Methods for Link Prediction Problem 6

A Comprehensive Survey on Learning Based Methods for Link Pr...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Balvir, Sachin U. Raghuwanshi, Mukesh M. Singh, Kavita R. Ycce Dept. of Ct Nagpur India Datta Meghe Institute of Engineering Technology and Research Wardha India Symboisis Intsitute of Technology Department of Computer Science and Engineering Nagpur India Yeshwantrao Chavan College of Engineering Department of Computer Technology Nagpur India

ISBN: (纸本)9798350346961

The development of internet technology is continuously increasing day by day. Social network is also the internet based technology, running online to deliver more services and strengthen communities. The availability of large amount of data encouraged the various field of study like data science, data mining and analysis, neural network etc. Currently, in real scenario the two giants Facebook and Google have the tremendous amount of data. The link prediction problem is a key issue with social network analysis. The Link prediction issue is to predicting new link or future link between two nodes in a network. This paper talked about the several method used to address the issue of link prediction based on learning approach mainly it focus on supervised, unsupervised and semi supervised learning based methods.. © 2023 IEEE.

关键词： Forecasting

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Predicting Mobile Prices with Machine Learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Rana, Deepak Singh Dhondiyal, Shiv Ashish Singh, Sumeshwar Kukreti, Sanjeev Dhyani, Ashish Graphic Era Hill University Dept. Of Computer Science And Engineering Dehradun India Graphic Era Deemed To Be University Dept. Of Computer Science And Engineering Dehradun India Jaypee Institute Of Information Technology Dept. Of Computer Science And Engineering India Graphic Era Deemed To Be University Department Of Hospitality Management Uttrakahnd Dehradun India

ISBN: (纸本)9798350306446

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs. © 2024 IEEE.

关键词： Decision trees

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An Extensive Survey on Audio-to-Text and Text Summarization for Video Content 3

An Extensive Survey on Audio-to-Text and Text Summarization ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Raut, Nitin B. Pranesh, A.S. Nagulan, B. Pranesh, S. Vasantharajan, R. KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India Grids and Guides Chennai India

ISBN: (纸本)9798350343632

This literature review examines evolving trends in audio-to-text and text summarization techniques applied to YouTube video content. With the rapid expansion of online video platforms, the demand for efficient transcription and summarization of multimedia data has grown significantly. This study offers a comprehensive analysis of current methodologies, algorithms, and tools for converting audio content to text, highlighting the intricacies of speech recognition technologies and their applicability in various contexts. Additionally, the survey explores text summarization techniques specifically designed for YouTube videos, including both extractive and abstractive methods. It assesses their effectiveness in condensing lengthy video transcripts into concise and coherent textual representations. Special emphasis is given to NLP (Natural Language Processing) algorithms and ML (Machine Learning) models that aid in extracting essential information while maintaining contextual relevance. © 2023 IEEE.

关键词： Overfitting Reinforcement Learning Supervised Learning Testing Data Training Data Unsupervised Learning

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Personality Prediction Using ML and NLP 7

Personality Prediction Using ML and NLP

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mahandule, Adinath Fiske, Gaurav Malusare, Vedang Bhambure, Omkar Shedge, Rajashree Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai400706 India

ISBN: (纸本)9798350304268

Estimating a person's personality is essential in understanding how they manage stress, lead, communicate, collaborate, and influence others. One of the critical factors that affect how people interact with their environment is their personality. This project aims to utilize machine learning and also natural language processing techniques to predict the personality of students based on a questionnaire. The system analyzes the user's personality in the database, which already records personality traits. The system then identifies the user's personality based on the MBTI model, a widely used personality model. This project's significance lies in the capability of the system to aid students, teachers, and businesses in identifying students' personalities to plan their actions better. The ML and NLP techniques implemented in this system can categorize or anticipate students' personalities, thus assisting businesses in their hiring process. © 2023 IEEE.

关键词： Catboost Gradient Boosting Machine Learning Myers-Briggs Type Indicator (MBTI) Natural Language Processing TF-IDF(Term Frequency-Inverse Document Frequency)

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Design and Implementation of a Distributed Network Management System for a Multi-Branch University using GNS3 14

Design and Implementation of a Distributed Network Managemen...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Hosen, Md Apu Hoshen Moz, Shahadat Akber, Md. Ali Northern University of Business and Technology Khulna Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350335095

The rapid digitalization of communication necessitates continuous skill updates to keep pace with evolving technology. Network management involves multiple functionalities that play a crucial role in optimizing network performance and ensuring continuous availability. It is very important to ensure a proper network management system for a university that has multiple branches to effectively manage and monitor their network infrastructure. A proper network management system provides centralized control and visibility over the entire network infrastructure, allowing administrators to efficiently manage and monitor network devices, configurations, and performance from a single interface. this research has developed a network management system that facilitates real-time network monitoring and troubleshooting, enabling proactive identification and resolution of issues to ensure smooth operations across all branches. It simplifies configuration management, ensuring consistency and compliance in network settings and updates. Resource optimization is achieved through insights into network utilization and performance metrics, allowing administrators to allocate resources efficiently and plan for future expansion. Security and compliance measures can be enforced, protecting sensitive data and ensuring adherence to regulatory requirements. This study utilized GNS3 to create a network layout for a university with multiple branches, along with essential security configurations for the network design. A diverse range of protocols was implemented to safeguard and cater to the users of the secure university network. Furthermore, Switches, Data Routers, firewalls, and BGP Routers were incorporated into the network to enhance communication efficiency and streamline operations. © 2023 IEEE.

关键词： Sensitive data

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Youtube Spam Detection Scheme Using Stacked Ensemble Machine Learning Model

Youtube Spam Detection Scheme Using Stacked Ensemble Machine...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Shabadi, Laxmi Chaitra, Y.L. Srikanth, P. Vijay Kumar, L. Kashyap, Udisha Sjb Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350300826

This work introduced a method to identify English spam comments on YouTube, a platform that has experienced a significant increase in spam activity. Despite YouTube's efforts to implement its own spam filtering system, it still struggles to effectively block spam comments. To address such an issue, we analyzed existing research on YouTube spam comment detection and performed classification experiments using an ensemble model that combines various techniques. A stacked ensemble model combines the predictions of multiple individual models to improve the accuracy and robustness of spam detection in social media applications. It leverages the strengths of different models and captures a broader range of features, leading to a more accurate classification of spam and non-spam content. It has then implemented a web extension to hide the spam comments from the page by modifying the CSS (Cascading Style Sheets). The comment data utilized in experiments was sourced from popular music videos by artists such as Psy, Katy Perry, LMFAO, Eminem, and Shakira. © 2023 IEEE.

关键词： Classification (of information)

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Harnessing Machine Learning for Uterine Fibroid Detection Using Advanced Classification Techniques

Harnessing Machine Learning for Uterine Fibroid Detection Us...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Deepak Kumar, A. Suhaasini, S. Swetha Shri, B. Muthukumar, S. Sudhahar, T.N. Menaga, D. St. Joseph's Institute of Technology Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319200

In the 40 to 50 age range, uterine fibroids are prevalent in over 70% of women. The proposed system employs a dataset comprising 1985 ultrasound images of uterus for fibroid detection. Utilizing advanced classification techniques like ResNet-152 and MobileNet V2, and an ensemble of Logistic Regression and MobileNet V2, the proposed system attained remarkable precision at 87.23% and an impressive accuracy of 91.67%. This outperformed the previous HIFU-Net Segmentation method's precision of 84.48% on MRI scans, highlighting the potential of advanced techniques in medical diagnostics. © 2023 IEEE.

关键词： Classification Ensemble HIFU-Net MobileNet V2 ResNet-152 Uterine fibroids

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