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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Khenwar, Medha Chauhan, Dushyant Sharma, Nandini Gupta, Priyanka Gla University Department of Computer Engineering and Applications Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Millions of individuals globally experience spinal ailments. This stresses the importance of swift detection for achieving better treatment results. Traditional diagnosis techniques can be lengthy. They might lack precision. So here the aim is to design a powerful yet budget-friendly system that employs a deep neural network which has been trained on a vast X-ray image database. The system aims at carrying out X-ray scans of spinal vertebrae with improved accuracy. The method proposed utilizes a lite deep neural network to gather information. This is done from numerous levels to aid. In the detection of spinal health conditions, the autonomous system for finding spine illnesses consist crucial elements. These include photo filtering tools attribute excavation methods and efficient classification algorithms. Moreover, Clinical information and patient past records are added to the analyses. This augments the diagnostic precision and dependability of the system. © 2024 IEEE.

关键词： Budget control

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PrescienceMED: Leveraging Text Classification for Precise Disease Prediction 8

PreScienceMED: Leveraging Text Classification for Precise Di...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Prathwini Prathyakshini Nitte Department of Master of Computer Applications Karkala India Nitte Department of Information Science and Engineering Karkala India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350593

Emergent of new and modern technologies provide personalized solutions to health issues faced by the individuals. A chatbot using Natural language processing (NLP) is proposed which can detect relevant disease based on the user input. Different classification techniques such as Support vector Machine (SVM), Random Forest, Gaussian Naive Bayes and Decision Tree are used in detecting the relevant diseases with each disease containing 17 symptoms. Based on the symptoms the all the models can detect the diseases out of which Random Forest yields better accuracy score of 99.59%. Hence the proposed model can detect the diseases based on user queries. The study utilizes machine learning algorithms in order to automatically classify medical data based on the symptoms, reducing the reliance on manual analysis, and increasing efficiency. © 2024 IEEE.

关键词： Random forests

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nPGSAO: A Hybrid Particle Swarm Optimization and Genetic Algorithm with Niching Technology for Edge Server Placement

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IEEE Internet of Things Journal 2025年第12期12卷 19370-19383页

作者： Wang, Bo Zhang, Zhifeng Song, Ying Chen, Ming Liu, Dongqing Software Engineering College Zhengzhou University of Light Industry Zhengzhou China Computer School Beijing Information Science and Technology University Beijing China School of Mathematics and Computer Science Tongling University Tongling China National Engineering Laboratory for Internet Medical Systems and Applications the First Affiliated Hospital of Zhengzhou University Zhengzhou China

In this paper, we focus on the edge server placement problem (ESPP). ESPP is to decide positions (edge stations) where purchased edge servers (ESs) are placed when a service provider builds or upgrades its edge computing solution. The decision of ESPP largely determines the overall service quality and resource efficiency. We aim at providing ESPP solutions for optimizing the service quality in terms of the system response time, given a set of purchased ESs. To achieve this aim, we first deduce the optimal request dispatch for each edge station based on queue theory and Binomial theorem. Then, based on the deduced result, we formulate ESPP into a discrete nonlinear constrained optimization model. Because ESPP is NP-hard and generally multi-modality, we designed a hybrid metaheuristic algorithm to combine advantages of particle swarm optimization (PSO) and genetic algorithm (GA) as well as the niching technology, which is named nPGSAO. The basic idea of nPGSAO is grouping similar individuals into a niche and finding the niche best. Then, nPGSAO evolves each individual by integrating the swarm intelligence of PSO into the evolutionary process of GA based on its personal and niching best. To evaluate the performance of nPGSAO, we conducted extensive simulated experiments. The results show that nPGSAO achieves 5.78%–40.4% better response time with varied ESPP scale, compared to 11 up-to-date algorithms, and has a linear time complexity with the ESPP scale. These confirm the superior performance and scalability of nPGSAO. © 2014 IEEE.

关键词： Genetic algorithms

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Analysis of Network on Chip Topologies 1

Analysis of Network on Chip Topologies

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Ramakrishnan, Nithin Sanju, V. Farhanaaz REVA University School of Computer Science and Engineering Bengaluru India REVA University School of Computer Science and Applications Bengaluru India

ISBN: (纸本)9798350323795

The incorporation of multi-core processors using current technologies is a challenging task as establishing effective communication among distant cores consumes significant time, ultimately diminishing the benefits of multi-core functionality. A new technology called Network on Chip (NoC) has been introduced to address this issue and is considered the future of the semiconductor industry. To develop new multi-core processors that outperform existing processors, it is crucial to compare traditional technologies with NoC. The traditional method faces difficulties implementing multi-core systems effectively and establishing efficient communication among different logic units, making NoC a helpful tool for developing multi-core processors. A detailed study of the NoC architecture is necessary, including the understanding of each component of its basic building blocks, such as topology, flow control mechanisms, routing logic, and router micro-architecture, and their performance comparison. This understanding is critical to select the most appropriate NoC architecture for implementing multi-core processors. © 2023 IEEE.

关键词： Network-on-chip

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NFT Marketplace: What are NFTs, and how does OpenSea succeed in acquiring the most of the NFT Space 1

NFT Marketplace: What are NFTs, and how does OpenSea succeed...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Thakur, Anil Divya, K. Verma, Sahil Kaur, Maninder Chandigarh University Dept. of Computer Science & Engineering Mohali India Uttaranchal University Dept. of Computer Science & Engineering Dehradun India Guru Gobind Singh College for Women Dept. of Computer Science & Applications Chandigarh India

ISBN: (纸本)9798350373042

Nowadays, the world is becoming smarter, so as the technology, the new era is on the boom, which is blockchain because of its different use cases and benefits. The blockchain is a growing list of records called blocks that are linked together with the use of cryptography. Because all servers on a network are corresponding and a significant concern in the types of network style, its data sources are managed through a peer-to-peer (p2p) network. The servers engage in negotiation and communication through the agreement method. The dispersed network is a network system in which information, software application, and computer system programs are spread out on greater than one node (computer systems). These nodes also depend on each other. It is only a p2p network that gets rid of a solitary factor of failing. It is a decentralized, dispersed, and unalterable journal to show electronic purchases. One of the concepts of blockchain is NFTs. These are digital assets with a unique identity and cannot be replicated. They can be used for a variety of purposes, such as in-game items, digital collectibles, or even to represent real-world assets like cars or houses. They can represent any digital asset or physical goods. NFTs can represent ownership and authenticity of any type of digital asset or physical goods. They can also represent a stake in the game, such as ownership in a casino or a share in a company. © 2024 IEEE.

关键词： Smart contract

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Brain Tumor Detection using Optimized Stochastic Gradient Descent Function

Brain Tumor Detection using Optimized Stochastic Gradient De...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Mishra, Annu Chaturvedi, Ravi Prakash Sharma, Hirdesh Kumar, Prashant Asthana, Sameer Parashar, Mayank Sharda School of Engineeringand Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineeringand Technology Department of Computer Science and Applications Greater Noida India JEMTEC Department of Computer Science and Engineering Greater Noida India ITS Engineering College Department of Computer Science and Engineering Greater Noida India GNIOT Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

The learning rate of a CNN determines the efficiency of the neural network. In brain tumor detection, the learning rate and the optimization function plays a key role in deciding the final output. The Optimized Stochastic Gradient Descent (OSGD) optimizer giver better result in terms of Accuracy, Recall and other determining factors. We have also compared the results obtained with other optimizers and it could be clearly stated that our proposed model. The OSGD optimizer controls the maxima and minima, and move the near optimum solution buy controlling the learning rate. So via this method we can control our right *** we detect our object in les error. © 2024 IEEE.

关键词： Stochastic systems

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Natural Language Processing (NLP) based Phonetic Insights for Improving Voice Recognition and Synthesis 4

Natural Language Processing (NLP) based Phonetic Insights fo...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Priyadharshini, N. Subbulakshmi, N. Kalasalingam Academy of Research and Education Department of Computer Applications Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Computer Science and Engineering Krishnankoil India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Voice analysis in Natural Language Processing (NLP) involves the processing of spoken language to develop applications such as speech recognition, synthesis, and speaker identification. This survey explores the phonetic intricacies of Tamil and other Dravidian languages, examining their impact on NLP. The extensive phonetic inventory of the Tamil Language is characterized by its complex vowel system, retroflex consonants, and the distinction between aspirated and unaspirated stops, which presents unique challenges and opportunities for computational linguistics. Phonetics helps to process the human language both accurately and naturally and it works as a bridge between the spoken and written communication forms. This paper emphasizes the necessity of developing specialized phonetic modeling techniques and robust algorithms. Additionally, it highlights the importance of creating comprehensive phonetic datasets and leveraging advancements in machine learning and deep learning to improve NLP tasks such as speech recognition and synthesis. A brief comparison on algorithms and applications associated with voice-assisted technologies. By advocating for interdisciplinary collaboration among linguists, computer scientists, and language technologists, this survey aims to inspire further research and innovation in processing Tamil and other Dravidian languages, ultimately contributing to more inclusive and efficient NLP systems for these linguistically rich languages. © 2024 IEEE.

关键词： Deep learning

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Utilizing Artificial Intelligence for Advance Data science and Analysis 2

Utilizing Artificial Intelligence for Advance Data Science a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Deepak Chaudhary, Gaurav Rajput, Kapil Singhal, Amit Sharma, Tarun Kumar Srm Institute of Science and Technology Department of Computer Applications UPModinagar Delhi India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

Data technology has experienced an explosion in popularity and scope over the past few years, inflicting several companies to try to capitalize on the sizeable possibilities the technology has to provide. Agencies are utilizing AI to develop records science studies, allowing them to go beyond conventional strategies and discover new insights and innovations within the discipline. With AI, statistics scientists can identify styles in massive datasets, optimize algorithms to clear up complicated issues and create predictive fashions to discover developments and correlations. In addition, AI may be used to automate specific components of fact processing, making data technological know-how simpler and quicker. To make the most of this generation, organizations should have personnel knowledgeable in the basics of facts science and AI and the knowledge to recognize the complexities of the algorithms and applications they use. With the right group and the proper equipment, AI may make data technological know-how studies extra effective and more beneficial than ever. © 2024 IEEE.

关键词： Algorithms Correlations Fundamentals Technology Understand

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DNN-STACK: a stacking technique based on deep neural network for detecting copy-move forgery

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Neural Computing and applications 2025年第6期37卷 4989-5004页

作者： Krishnalal, G. Jagathy Raj, V.P. Madhu, G. Arun, K.S. School of Engineering Cochin University of Science and Technology Kerala Cochin682022 India School of Management Studies Cochin University of Science and Technology Kerala Cochin682022 India Department of Computer Applications Cochin University of Science and Technology Kerala Cochin682022 India

In recent years, detecting image forgery has become an important topic because of the availability of efficient and sophisticated image-editing software. Copy-move forgery, which involves copying a section of an image and pasting it to a different location inside the same image, is one of the most popular tampering techniques that alter the identity of a given image. This paper presents DNN-STACK, a deep neural network-based stacking scheme to identify copy-move forgery by effectively combining the predictions yielded by different base-level models. To extract hierarchical representations from the given images and generate base-level predictions, the proposed approach uses five distinct but complementary deep learning-based base-level models. After that, a consensus prediction is generated using the proposed DNN-STACK model, which can infer the nonlinear relationship between the base-level predictions. Experimental evaluation on three publicly available datasets, such as MICC-F600, MICC-F2000, and FAU, reveals the fact that the proposed DNN-STACK model outperforms the existing forgery detection techniques, significantly improving detection accuracy across varying image resolutions and attack types, including rotation, scaling, noise, and compression levels. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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Two-Stage Model for Copy-Move Forgery Detection 1st

Two-Stage Model for Copy-Move Forgery Detection

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1st International Conference on Computational Intelligence for engineering and Management applications, CIEMA 2022

作者： Kumari, Ritesh Garg, Hitendra Chawla, Sunil Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Chandigarh University Mohali India

ISBN: (纸本)9789811984921

Digital images can be tempered or modified very easily with many tools available on the internet. Nowadays some software not only enhance and retouch but also copy-move objects of the image in such a way that is hard to differentiate. Image forgery detection deals with the recognition of tempering like splicing, copy-move, and other modifications done on the image. One of the most common ways of passive forgery detection is the block-based copy-move technique. In this paper, forgery detection is achieved in two parts. In the first part, Copy-Move forgery is classified by support vector machine (SVM) and features are extracted by local binary pattern (LBP) and discrete cosine transform (DCT). However, the region of forgery is calculated in the second part with the help of the Gray-level Co-occurrence Matrix (GLCM) through overlapping blocks. The whole experiment and evaluations are performed on the CoMoFoD image data set. The performance is measured in terms of accuracy and sensitivity. The promising result indicates the effectiveness of the model with enhanced accuracy of 97.82%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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