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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Padma, R. Yendapalli, Vamsidhar Gitam University Gitam School of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350309140

In the domain of steganography, the act of concealing data within an image poses a significant challengebalancing between the precision of the carrier image and the capacity for embedding information. This article introduces an innovative spatial steganography approach employing evolutionary algorithms. Proposed method introduces novel approaches to strengthen the matching of the LSB between the carrier and the concealed image. This process leads to an up-scaled concealing efficiency while minimizing deformation. The technique involved optimized pixel traversal in both row and column wise directions, pixel relocation, pixel swapping confidential bits, and inverting confidential data. A generic steganography model rooted on evolutionary algorithms is formulated to explore optimal solutions. Experimental investigations of the RECS [11] and SDID [16] method confirm that the performance of the proposed method is comparatively better than the traditional methods in terms of higher Embedding rate by 29%, PSNR by 19%, and SSIM by 15% respectively. © 2024 IEEE.

关键词： Embeddings

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Performance Analysis of AES and DES Algorithm for Encrypting Medical Record Using Blockchain

Performance Analysis of AES and DES Algorithm for Encrypting...

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Madhurya, J.A. Meena, K. Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India

ISBN: (纸本)9789819998104

Secure storage and exchange of health information has emerged as a focus of research in the area of medical technology. The healthcare sector has a high requirement for data security and privacy because most of the healthcare systems maintain data in a centralized manner, storing it all in a single location. Such systems have a significant probability of allowing an anonymous user to access and alter the data, which frequently results in data leakage problems and lack of transparency. In the proposed work, the blockchain technology is used which is a decentralized and distributed ledger that enhances the data security by storing the record on blocks and encryption algorithm to encrypt the data. Here patient is allowed to provide the access permission for the record. An advanced encryption standard (AES) and data encryption standard (DES) algorithm is used for encryption and its encryption time is compared and represented in graph. The objective of this work is to do the performance analysis between algorithms and to represent the faster encryption algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Deep Learning-based Classification of Infant Cries for Parental Assistance: A Study using MFCC and CNN Technique 3

Deep Learning-based Classification of Infant Cries for Paren...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mala, B.M. Darandale, Smitha Sandeep Gitam School of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509675

Worldwide Approximate count of 130 million babies were born per annum. Maintaining newborn babies is a great difficulty, mainly for first-time parents. However, intimations from experienced parents, books, and videos help to a certain extent but not significantly because of difficulty understanding the meaning of Infant cries. Researchers have proposed several techniques using machine learning and artificial intelligence. In this work, we try to leverage deep learning-based techniques such as convolutional neural networks (CNN) to identify the infant crying. Infant crying is further classified into four classes (inclusive of pain, hunger, sleepiness, and wet diaper). The MFCC (Mel-Frequency Cepstral Coefficients) technique is used for feature extraction, as it efficiently captures audio characteristics from crying sounds. In this study, will compare the various existing machine learning models with our proposed model. Among the models evaluated, MFCC combined with Convolutional Neural Networks (CNN) achieves the highest accuracy of 95.5%, for classifying the infant cry and reasoning. © 2025 IEEE.

关键词： Convolutional neural networks

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User Task Priority Based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing in Cloud Environment

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SN computer science 2024年第8期5卷 970页

作者： G, Nida Kousar C, Gopala Krishnan Department of Computer Science and Engineering GITAM School of Technology Bengaluru 561203 India

An effective task scheduling method can accommodate user needs, boost resource usage, and boost cloud computing's overall efficiency. However, the unchanging task needs are generally the focus of grid computing's job scheduling, leading to low resource usage. Distributing the dynamic user tasks fairly among all cloud nodes is the goal of load balancing, a relatively new field of study. The primary difficulty with cloud computing is load balancing. By making better use of available resources, load balancing methods improve cloud performance. Load balancing primary goal is to lessen the burden on the environment by cutting down on energy use and carbon emissions. The most crucial characteristics that can both satisfy user needs and maximize resource utilization are used to determine the order of priorities. Existing systems often ignore user priority suggestions in favor of optimal scheduling to improve load balancing. Scheduling that takes into account user-guided priorities uses a data-driven strategy, which helps improve load balancing. Scheduling algorithms that take user priorities into account can optimize load distribution more effectively. The primary objective of this research is to provide a priority based randomized load balancing technique that assigns tasks to virtual machines in a random fashion based on criteria such as the number of users, the amount of time the task takes to run, the type of software being used, the cost of the software, and the amount of available resources. This method maximizes system performance by decreasing response time and resource consumption while increasing metrics like fault tolerance and scalability. This system for scheduling tasks not only accommodates user needs but also achieves excellent resource usage. This research proposes a User Task Priority based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing (UPRA-MCTSS-LB) Model for enhancing the cloud service quality. The proposed method res

关键词： Cloud computing Load balancing Multi class tasks Quality of service Resource utilization Task scheduling User task priority

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Design of a Physics-Informed Learning Model for the Electromagnetic Modeling of Functional Materials

Design of a Physics-Informed Learning Model for the Electrom...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Narang, Naina Lingam, Greeshma Department of Computer Science and Engineering GITAM Deemed to be University Vishakhapatnam Vishakhapatnam India Department of Computer Science and Engineering GITAM Deemed to be University Hyderabad Hyderabad India

ISBN: (纸本)9798350379693

There is a crucial need for an intelligent system to assist material scientists in fabricating and testing functional materials, such as microwave-absorbing materials. Most researchers are searching for novel materials for reduced-size microwave absorbing materials needed in various civil and military applications. The existing models used by material scientists suffer from various challenges, such as trial and error, the requirement of large dataset, and sophisticated instrumentation. This paper proposes a physics-informed learning-based model to assist the researchers in selecting, modeling, and characterizing any material depending on the user-defined application. An example is demonstrated for dielectric material to achieve the minima of the reflection coefficient. © 2024 IEEE.

关键词： Military applications

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Resource prioritization and allocation in fog computing using hybrid optimization

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Web Intelligence 2025年第Pre-press期Pre-press卷 1-22页

作者： Karpe, Sharmila Patil SH, Brahmananda Department of Computer Science and Engineering GITAM University GITAM School of Technology Bangalore Campus Karnataka India Department of Information Technology Walchand Institute of Technology Maharashtra Solapur India

With the rapid proliferation of IoT devices, the volume of data generated has reached unprecedented levels, necessitating efficient management strategies. Fog computing, complemented by 5G technologies, offers promising solutions to reduce service latency and enhance Quality of Service (QoS). However, allocating resources effectively remains challenging due to factors such as uncertainty, mobility, heterogeneity, and limited resources in fog computing environments. Traditional resource allocation (RA) algorithms often fall short of addressing these complexities. This study proposes a novel approach to RA in fog computing, utilizing a non-linear function to optimize resource allocation. An objective function is introduced, incorporating multi constraints such as resource utilization, service response rate, makespan, migration cost, and communication cost. The methodology emphasizes efficient resource allocation in crucial scenarios, facilitating rapid resource distribution where needed. The novel Coati Integrated Beluga Whale Optimization (CI-BWO) strategy is proposed to achieve optimal resource allocation in fog computing environments. By leveraging CI-BWO, this research aims to overcome the limitations of traditional RA methods and enhance the performance and scalability of fog computing applications. Finally, the superiority of the suggested strategy is assessed by comparison with many existing methods. When the task count is 200, the developed CI-BWO attained less migration cost of around 1.287, while existing models have acquired higher migration costs. © 2024 – IOS Press. All rights reserved.

关键词： Resource allocation

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An Experimental Study on Denoising the Images with Autoencoders 1

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Venkata Abhishek, Gullipalli Sai Prathek, Kotha Durga Aryan, Kosuri Srinivas, Gorla Department of Computer Science and Engineering Gitam University Visakhapatnam India

ISBN: (数字)9789819971374

ISBN: (纸本)9789819971367

Noisy data is still one of the most common issues in modern data transmission. We can solve this by using deep learning with an autoencoder, a feature extraction method that can reduce noise. In the current study, we examine the ability of an Autoencoder to minimize noise on various structures and amounts of additive noise implemented with multiple optimizers. The experiment is run on a Convolutional Denoising Autoencoder (CDAE), primarily for image data. The experiment is run with additive noise with a noise factor of 0.03, such as Gaussian, Salt and Pepper, and Speckle. The proposed model outperformed image quality measures PSNR, MSE, SSIM, and RMSE. The reconstructed images after denoising are identical to the original image data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Additive noise

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Cyberbullying detection based on the fusion of DistilBERT and SIMHASH Technique 2

Cyberbullying detection based on the fusion of DistilBERT an...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Kumar, Chintoo Arun Kumar, K. Gupta, Suneet Sardar, Tanvir Habib GITAM Department of Computer Science and Engineering Bengaluru India ACED Alliance Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350349221

The advent of social network sites increases the bullying content in textual and visual formats. Bullying content disheartened a user or community to a great extent. Also detection of cyberbullying content is a challenging task, especially in online social networks where individuals can be harassed or harmed..Recently, using pre-trained language models has played a pivotal role in solving this problem reasonably. In this paper, we employed a fusion of a transformer-based model and the SIMHASH technique to solve this classification problem. We consider a readily available cyberbullying dataset. We conducted a comparative study among state-of-the-art models to check the proposed model's efficacy. The proposed model provides a promising result and outperforms. © 2024 IEEE.

关键词： Social networking (online)

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Botnet Detection Through Flow-Based Deep Feature Extraction and Ensemble Classification

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Journal of The Institution of Engineers (India): Series B 2025年 1-14页

作者： Geetha, K. Brahmananda, S.H. Department of Computer Science and Engineering GITAM School of Technology Karnataka Bengaluru India Ashtaksha Labs Pvt Ltd Karnataka Bengaluru India

As Internet of Things (IoT) devices are networked and thus susceptible to many forms of attacks, cyber security risk is the primary concern in the IoT field. To tackle this issue, this study employs machine learning and deep learning techniques to identify botnet attacks. The proposed method uses a random forest classifier or other machine learning techniques to organize the data after extracting detailed characteristics using Bidirectional Long Short-Term Memory (Bi-LSTM). This methodology yields superior outcomes for challenges involving binary and multiclass classification. The IoT-23 and N-BaIoT databases were used. The IoT-23 collection consists of three normal samples and twenty malware samples from different IoT devices, whereas the N-BaIoT dataset includes scenarios like Mirai and Bashlite attacks. In the context of IoT risks, the proposed technique successfully differentiates between benign and malicious traffic data. In terms of multiclass classification, the accuracy rates for N-BaIoT and the IoT-23 dataset are 100% and 99.9%, respectively, while the acquired accuracy rates for binary classification are 99.9% for N-BaIoT and 97% for the IoT-23 dataset. © The Institution of Engineers (India) 2025.

关键词： Deep learning

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A systematic review on recent methods on deep learning for automatic detection of Alzheimer's disease

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Medicine in Novel Technology and Devices 2025年第1期25卷 1-12页

作者： Radhakrishna Chamakuri Hyma Janapana Department of Computer Science Engineering GITAM School of TechnologyVisakhapatnamAndhra Pradesh530045India

Alzheimer's disease(AD)is the most frequent cause of dementia,however,and it is caused by a number of different *** regard to the elderly population all over the world,Alzheimer's disease is the seventh largest cause of mortality,disability,and ***,social isolation,inactivity,alcohol,smoking,obesity,diabetes,high blood pressure,and age are all variables that can increase the likelihood of getting *** risk factors include social isolation,depression,and smoking.A diagnosis of Alzheimer's disease at an earlier stage may improve the odds of receiving care and *** professionals often diagnose AD based on a limited number of *** the other hand,it is now possible to identify and categorize Alzheimer's disease(AD)because of technological advancements such as artificial intelligence(AI).However,to identify the current AI-enabled approaches,we must conduct an investigation into the state of the *** breakthrough in diagnosis methodologies will enable the development of the Clinical Decision Support System(CDSS),capable of automatically diagnosing Alzheimer's disease(AD)without human *** this publication,we conduct a systematic review of sixty research articles previously reviewed by other *** systematic review sheds light on the synthesis of new knowledge and *** study discusses the current approaches for machine learning,deep learning methods,ensemble models,transfer learning,and methods used for early Alzheimer's disease *** paper provides answers to a large number of research issues and synthesizes fresh information that is helpful to the reader on many elements of AI-enabled approaches for Alzheimer's disease *** addition,it has the potential to stimulate additional research into more effective methods of computer-based intelligent identification of Alzheimer's disease.

关键词： Alzheimer disease diagnosis Artificial intelligence Deep learning Ensemble learning Machine learning

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