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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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Polycystic Ovary Syndrome (PCOS) diagnostic methods in machine learning: a systematic literature review

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Multimedia Tools and applications 2025年第16期84卷 16301-16337页

作者： Arora, Shikha Vedpal Chauhan, Naresh J.C. Bose University of Science and Technology YMCA Haryana Faridabad India Department of Computer Applications J.C. Bose University of Science and Technology YMCA Haryana Faridabad India Department of Computer Engineering J.C. Bose University of Science and Technology YMCA Haryana Faridabad India

Polycystic Ovarian Syndrome, also known as PCOS, is a major hormonal imbalance affecting women primarily in their reproductive age. Women with PCOS may have either infrequent or extended menstrual cycles or sometimes excess male hormone i.e. androgen levels. The ovaries may grow with number of slight collections of fluid, called follicles that fail to release eggs every month regularly. It is also seen that PCOS not only affects a woman’s fertility but also indirectly causes various other health issues like type 2 Diabetes, obesity, blood pressure and other metabolic disorders. Recent researches have focussed on the use of different algorithms in Machine Learning to diagnose PCOS using structured or unstructured data. Therefore, in this study, a considerable Literature Review has been carried out to provide a detailed analysis of various algorithms that have been used to detect PCOS with their comparative study. Also some of the work particularly focuses on health issues, the food and dietary patterns and the ways to manage PCOS. Further, these algorithms are critically analysed to understand the framework and limitations that should be considered while putting forward the solutions relevant to the diagnosis of PCOS in an effective way. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blood pressure

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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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Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

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computer Systems science & engineering 2023年第5期45卷 1577-1593页

作者： R.Dhanya R.Kalaiselvi Department of Computer Applications Noorul Islam Centre for Higher EducationNagercoil629180India Department of Computer Science Engineering Noorul Islam Centre for Higher EducationNagercoil629180India

A severe problem in modern information systems is Digital media tampering along with fake *** though there is an enhancement in image development,image forgery,either by the photographer or via image manipulations,is also done in *** researches have been concentrated on how to identify such manipulated media or information manually along with automatically;thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced ***,high complexity affects the developed ***,it is complicated to resolve the issue of the speed-accuracy *** tackling these challenges,this article put forward a quick and effective Copy-Move Forgery Detection(CMFD)system utilizing a novel Quad-sort Moth Flame(QMF)Light Gradient Boosting Machine(QMF-Light GBM).Utilizing Borel Transform(BT)-based Wiener Filter(BWF)and resizing,the input images are initially pre-processed by eliminating the noise in the proposed *** that,by utilizing the Orientation Preserving Simple Linear Iterative Clustering(OPSLIC),the pre-processed images,partitioned into a number of grids,are ***,as of the segmented images,the significant features are extracted along with the feature’s distance is calculated and matched with the input ***,utilizing the Union Topological Measure of Pattern Diversity(UTMOPD)method,the false positive matches that took place throughout the matching process are *** that,utilizing the QMF-Light GBM visualization,the visualization of forged in conjunction with non-forged images is *** extensive experiments revealed that concerning detection accuracy,the proposed system could be extremely precise when contrasted to some top-notch approaches.

关键词： Borel transform based wiener filter(BWF) orientation preserving simple linear iterative clustering(OPSLIC) keypoint features block features outlier detection

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Unlocking the Potential of Unlabeled Data: Semi-Supervised Learning for Stratification of Hyperspectral Images 21

Unlocking the Potential of Unlabeled Data: Semi-Supervised L...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Ranjan, Pallavi Kumar, Rajeev Girdhar, Ashish Delhi Technological University Computer Science and Engineering New Delhi India Kurukshetra University Computer Science and Applications Haryana India

ISBN: (纸本)9798350358230

Hyperspectral imaging, a critical technology for spectral data acquisition, plays a pivotal role in various fields, including agriculture, medicine, defense, remote sensing and environmental monitoring. Nonetheless, an enduring obstacle in the domain of hyperspectral image analysis revolves around the restricted accessibility of annotated data, posing an impediment to the creation of precise classification models. In response to this challenge, we present an innovative semi-supervised framework that harmoniously combines unsupervised feature learning with the employment of graph-based convolutional neural networks (GCNs). Our approach harnesses the latent knowledge hidden within vast pools of unlabeled hyperspectral data using autoencoders, which extract meaningful features. These features are then incorporated into a GCN-based architecture, leveraging spatial relationships among neighboring pixels. The fusion of unsupervised autoencoder based learning and graph-based techniques enables our model to achieve remarkable classification accuracy, even in scenarios with minimal labeled samples. Through extensive experimentation, we demonstrate the superior performance and robustness of our methodology across a spectrum of hyperspectral imaging datasets. This work is a significant step in the realm of semi-supervised hyperspectral image analysis, unlocking the potential of unlabeled data and empowering accurate classification in data-scarce environments. © 2023 IEEE.

关键词： Image classification

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Performance Analysis of Hybrid Cryptographic Algorithms in Serverless Platforms 1

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Raj, Vinay Suresh, Satharla Phridviraj, M.S.B. Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Kakatiya Institute of Technology & Science Warangal India

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

Serverless computing has become the buzzword in IT and it has been widely accepted because of its diverse cloud computing benefits. One of the major drawbacks of serverless computing is the security of the data which is handed over to the third parties. To ensure the security of the data and secure private communication between the services, hybrid cryptographic algorithms play a predominant role by combining the best security features of symmetric and asymmetric encryption techniques. In this work, two hybrid cryptographic algorithms are proposed namely 2-tier and 3-tier models and the performance of the two algorithms are analyzed in terms of encryption and decryption time along with the file size. The obtained results are also compared with an existing hybrid cryptographic model and it is observed from the results that the proposed hybrid model outperforms the existing model on both the 2-tier and 3-tier models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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