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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Babu Rao, K. Singh, Pallavi Verma, Gargi Shankar Kannan, L. Mohana Kadam, Swaraj Mangalore Institute of Technology & Engineering Department of Computer Science and Engineering Karnataka Mijar India Saraswati Dental College & Hospital Department of Public Health Dentistry Uttar Pradesh Lucknow India Krishna's Vikash Institute of Technology Department of Computer Science and Engineering Chhattisgarh Raipur India Erode Sengunthar Engineering College Department of Biomedical Engineering Tamil Nadu Perundurai India Dr. D Y Patil Institute of Technology Department of Electrical Engineering Maharashtra Pune India

ISBN: (纸本)9798331529635

The diagnosis of brain tumors is admittedly difficult because of their varied and complicated nature. Therefore, one has to get the right diagnosis and categorization for it to be treated well. This is a situation where identification of multiple classes such as;meningioma, pituitary, glioma and no tumor is crucial in taking the right course of action. Therefore, a new deep learning architecture is suggested in this work to increase the specificity between different types of tumors by detecting brain tumors with high accuracy in MRI scans. The proposed methodology starts with the Cumulative Curve Fitting Approximation (CCFA) preprocessing algorithm to improve the quality of MRI images. Preprocessed images are used as inputs for feature extraction based on geometric algebra transformers. These transformers include geometric relationships within the data, ensuring more distinctive selection. The categorization is further carried out with assistance of a Similarity-Navigated Graph Neural Network (SNGNN) incorporating the Banyan Tree Growth Optimizer (BTGO) model, which is constructed for efficient detection of the subtlest patterns and their connection between classes of brain tumors. It integrates very well in terms of the accuracy shown by its performance metrics. It shows effectiveness in a multiclass scenario of detecting brain tumors, which helps in the precise and early diagnosis and even tailors proper treatment plans. © 2024 IEEE.

关键词： Graph neural networks

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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

Optimizing NOMA Transmissions to Advance Federated Learning ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Ziru Ni, Zhou Guan, Peiyuan Wang, Lu Cai, Lin X. Hashemi, Morteza Li, Zongzhi Illinois Institute of Technology Department of Electrical and Computer Engineering Chicago United States University of Kansas Department of Electrical Engineering and Computer Science Lawrence United States University of Oslo Department of Informatics Oslo Norway Illinois Institute of Technology Department of Civil Engineering Chicago United States

ISBN: (纸本)9798350351255

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms - Federated Vehicular Network, NOMA © 2024 IEEE.

关键词： Data privacy

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Detection of Asperger Syndrome at Early Stages in Children using Deep Neural Networks 8

Detection of Asperger Syndrome at Early Stages in Children u...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Battu, Vishnu Vardhan Vinay Kumar, B. Boddapati, Chaitanya Sreya Chaparala, Pujitha Chava, Sri Harsha Damisetty, Leela Hemanth Pvp Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350396638

ASD (Autism Spectrum Disorder) is behavioral and considered to be a developmental disorder found in human beings. It includes disorders in brain development, eventually affecting functional brain capabilities. It also affects how a person converses with others and causes problems in interaction and communication. The vast range of symptoms, abilities, and functioning levels in ASD patients is known as the "spectrum."It is essential to diagnose ASD at early stages so that autistic child can reach their full *** some cases, parents cannot accept that their child's mental growth is not developing along with physical development. With early diagnosis, children with autism can lead fulfilling and joyful lives through the provision of appropriate medication. Screening tests are used to diagnose this type of disorder but are time-consuming and costly. This research study has employed a convolutional neural network to detect the likelihood of ASD in young children at an early stage. Deep Neural Networks function well in many applications. This study has used Xception and Resnet50 model to predict ASD based on static facial images, especially in children. According to the results obtained, ResNet50's performance is better than Xception in Autism detection. © 2023 IEEE.

关键词： Deep neural networks

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Efficient Prediction and Analysis of Monkeypox Skin Lesion: A Comparative Study for Web based Application 2

Efficient Prediction and Analysis of Monkeypox Skin Lesion: ...

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2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Gupta, Piyush Mittal, Uday Jha, Tushar Agarwal, Mini Tiwari, Ajay Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350322064

After the coronavirus pandemic, another deadly disease, known as monkeypox, began to spread. This was so alarming that the World Health Organization declared it a global public health emergency. As of 31st January 2023, there have been 85449 cases of monkeypox reported in 110 countries worldwide. It is difficult to diagnose because its symptoms resemble those of chicken pox, small-pox, etc. The clinical diagnosis of monkeypox is performed using the Polymerase chain reaction (PCR) test, which takes a considerable amount of time to determine the result. Any non-clinical test that could aid in identifying monkeypox in suspected patients would be advantageous. Various deep learning models were found to be useful for this purpose, provided sufficient training data is available. We've used already existing datasets and extended them to have a sufficiently large dataset. This dataset is then fed to pre-trained deep-learning models, including ResNet50, EfficientNetB3, InceptionV3, and MobileNetV2. It has aided in comparing the accuracy of these models. Based on our analysis, we have employed best performing model i.e., EfficientNetB3 in a web application with a user-friendly interface to interact with it. © 2023 IEEE.

关键词： Polymerase chain reaction

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Hybrid Cryptographic Algorithm to Secure Internet of Things 7

Hybrid Cryptographic Algorithm to Secure Internet of Things

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Amala Nikitha, G. Kathrine, G. Jaspher W. Duthie, Christina Rajesh Ebenezer, V. Silas, Salaja Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

Small IoT devices can only support light protocols since they lack significant processing or storage power. Lightweight encryption standards or techniques will be utilized for encryption and decryption to prevent hackers from accessing the data in order to increase the security of IoT devices' network connections and commands. The encryption has to be robust enough to safeguard and lightweight enough to enable light processing hardware. There aren't many standards for lightweight cryptography that are used to safeguard devices in an existing network. If the network is not secured properly, an intruder can get in and take control of the devices without making a visible appearance. A hybrid lightweight cryptographic methodology have been proposed to address this problem. LWC algorithms are designed to provide strong security while minimizing the computational and memory resources required for encryption and decryption. This is particularly important for IoT devices, which often have limited processing power, storage capacity, and battery life. LWC algorithms offer a range of benefits for IoT devices, including improved security, reduced power consumption, and increased battery life. They are also designed to be easy to implement and integrate with existing IoT platforms and protocols. Proposed system's analytical work implementation will combine Salsa20 and AES algorithms to provide greater security and making it well-suited for real-time cryptography. © 2023 IEEE.

关键词： Internet of things

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Detection of Stage of Cancer Using Machine Learning and Deep Learning 5th

Detection of Stage of Cancer Using Machine Learning and Deep...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Ashtagi, Rashmi Akkalkot, Afsha Imran Kotecha, Ketan Maginmani, Umme Habiba Patil, Pushpalata Patil, Abhijeet Computer Engineering Department Vishwakarma Institute of Technology Pune India Computer Engineering Department College of Engineering Pune Technological University Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India Computer Science and Engineering SECAB Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Vijayapur India Computer Science and Engineering Faculty of Engineering and Technology Exclusively for Women Sharnbasva University Kalburagi India RPD PU College of Arts and Commerce Belagavi India

ISBN: (纸本)9789819761029

Melanoma, an aggressive form of skin cancer known for its rapid spread, poses a significant threat and is responsible for a substantial number of fatalities. The accurate classification of cancer stages is crucial for effective diagnosis, particularly during surgical treatment. This paper introduces two methods for categorizing melanoma cancer stages. Initial system distinguishes between stage 1 and stage 2, while the second system extends the classification to include stage 3 melanoma. Leveraging a Convolutional Neural Network (CNN) algorithm, the proposed system incorporates the Similarity Measure for Text Processing (SMTP) as a loss function. The experimental results showcase the efficacy of different loss functions, with a particular focus on the proposed SMTP loss function, which outperforms several others designed for classification problems. This algorithm stands out as a more efficient approach for melanoma stage classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Intuitionistic fuzzy rough set model based on k-means and its application to enhance prediction of aptamer–protein interacting pairs

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3575-3586页

作者： Jain, Pankhuri Tiwari, Anoop Som, Tanmoy Department of Mathematical Sciences Indian Institute of Technology Banaras Hindu University Uttar Pradesh Varanasi221010 India Department of Computer Science and Engineering KL University Telangana Hyderabad500075 India

Aptamers are very interesting peptide molecules or oligonucleic acid. They are used to bind particular target molecules. Aptamers play vital roles in various practical applications and physiological functions. Consequently, several diseases can be treated using therapies based on aptamer proteins and designing the binding of aptamers to specific proteins is essential to advance understanding into processes of interaction between aptamer-protein. Despite the wide applications of aptamers, identification of interaction between aptamer protein is always inadequate and challenging. Therefore, it is necessary to develop a computational approach for achieving good predictions of interaction between aptamer-protein. In the present study, a novel method for enhancing the prediction of interacting aptamer-target pairs based on sequence features obtained from both aptamers and their target proteins by employing a novel k-mean based intuitionistic fuzzy rough feature selection method is proposed. Firstly, an intuitionistic fuzzy rough set model based on k nearest neighbour concept is proposed. Then, a novel feature selection technique is introduced by using this model. Furthermore, non-redundant and relevant features are selected from training as well as testing datasets by using proposed feature selection technique. Secondly, SMOTE (Synthetic Minority Oversampling Technique) is applied to obtain the optimal balanced training and testing datasets. Thirdly, we apply various machine learning algorithms on optimally balanced reduced training and testing datasets to evaluate their performances. Experimental results shows that the best prediction performance is obtained by boosted random forest learning algorithm. Using a 10 fold cross-validation test, the proposed method is a good performer, with sensitivity of 91.3, 86.4, specificity of 91.9, 84.8, overall accuracy of 91.60%, 85.60%, Mathews correlation coefficient of 0.832, 0.713, AUC (area under curve) of 0.969, 0.908, and g-me

关键词： Proteins

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Predicting Evapotranspiration (ETo) by Training Regression Models 1

Predicting Evapotranspiration (ETo) by Training Regression M...

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1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022

作者： Sarkar, Suman Saurabh Dabra, Mamta Jain, Sushma Channa, Inderveer Sharda, Rakesh Thapar Institute of Enigineering and Technology Computer Science Engineering Department Patiala India Punjab Agriculture University Department of Soil Engineering Ludhiana India

ISBN: (数字)9781665443579

ISBN: (纸本)9781665443579

Evapotranspiration is a critical natural process in agriculture. Every farmer has to deal with this in every stage of crop development. Proper estimation of soil and leaf's-based Evapotranspiration (ETo) is required to be done. Traditionally it is being done based on climate forecasting only, but nowadays, many new techniques have been shown up by which this can be estimated more accurately. In this study, machine learning and deep learning models are being used to determine the ETo. Regression techniques are preferred over classification so that every possible value can be taken as an input for accuracy. Nowadays, many algorithms have proved their efficiency with the specific dataset. There is no unique algorithm that has established itself as suitable for every type of dataset. This study will help to find the best algorithm for getting accurate predictions on ETo. A few famous regression algorithms are being opted for this study, and the best resultant algorithm is determined for a given dataset. The highlighted algorithm based on the experiment should only be followed if similar climate attributes are encountered for any research and study. © 2022 IEEE.

关键词： Agriculture

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Biometric-Based Key Handling Using Variation to Scale Invariant Feature Transform amid COVID-19 Pandemic 7th

Biometric-Based Key Handling Using Variation to Scale Invar...

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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Kaur, Prabhjot Kumar, Nitin Singh, Maheep Department of Computer Science and Engineering National Institute of Technology Uttarakhand Uttarakhand Srinagar India School of Computing DIT University Uttarakhand Dehradun India Department of Computer Science and Engineering PEC Deemed-to-be University Chandigarh India Department of Computer Science and Engineering Doon University Uttarakhand Dehradun India

ISBN: (纸本)9789819950140

The outbreak of COVID-19 pandemic has hardened the biometric recognition, authentication, and cryptographic process based on facial features due to masks coverage. The Biometric cryptosystem (BCS) consisting of touch sensing biometric, such as fingerprint, palmprint, vein, is no longer in use in this scenario. In this context, other key-based biometric cryptosystems based on facial biometric may underperform. Thus, there is a need to simulate existing and implement novel biometric cryptosystems based on other biometric cues such as Ear, Face, Iris, Gait. This paper introduces a novel method for biometric key generation based on Scale Invariant Feature Transform (SIFT). The obtained key is used to encrypt/decrypt the data. At enrollment stage, the biometric key is obtained which subsequently encrypts the data and authentication phase decrypts the secret upon successful biometric match. The proposed method is compared with the state-of-art methods on three datasets namely AMI (ear), FACES (face), and UBIPr (iris). The metrics used to evaluate the performance of proposed method include: NPCR, UACI, PSNR, Correlation, MSE, MAE, SSIM, NRMSE. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics

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A Virtual Machine Protection Framework Against Compromised Hypervisor in Cloud Computing

A Virtual Machine Protection Framework Against Compromised H...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Mahipal, S. Ceronmani Sharmila, V. Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819947164

Virtualization in cloud computing enables efficient management of physical resources leading to affordable cloud services but has security vulnerabilities. The existing solutions are at a different level of privileges to protect VMs from compromised HV. To overcome this problem, in this paper, we proposed a solution that is software-based and works in the same privilege level of HV. Toward this end, we proposed a framework known as Secure and Isolated Execution Environment (SIEE) for VM protection against compromised hypervisors in cloud computing. An algorithm known as page marking for memory isolation (PMMI) algorithm ensures that each page can only be owned by one VM or hypervisor. Another algorithm known as interaction monitoring for hypervisor resilience (IMHR) is proposed to monitor different interactions structured to realize the SIEE. The empirical study reveals that the proposed framework incurs negligible overhead but improves the security level of virtualization in cloud computing environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Virtualization

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