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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Chaithra, I.V. Sunitha, M.R. Department of Computer Science Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India Department of Artificial Intelligence and Machine Learning Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present. Mental ill health is highly risky, stirring more than 300 million population. Because of this reason more focus of research is on this area. A statement by American Psychiatric Association "6.715 adults (6.7%) is get affected in any given year. One-sixth of people(16.6%) will experience depression in their life." Studies said that Women are in greater proportion to likely experience depression when compared to men. One among three women will share a significant depressive symptoms in their lifetime. Written text reflects its impact on language usage. The count of afflicted individuals can be lessened, using an advanced machine learning techniques and the availability of test data related to depression. This paper proposes different preprocessing techniques. Feature extraction techniques such as BoW(Bag of Words), Word2Vec, Glove, N-Gram, LIWC and LDA. Classification techniques such as Naive Bayes, SVM, LR, Adaboost and RF. © 2024 IEEE.

关键词： Deep learning

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Classification of Brain Disorder Diseases using Machine Learning based Data Mining Techniques

Classification of Brain Disorder Diseases using Machine Lear...

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2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Shalini, Alajangi Balarengadurai, C. Prasad, Boddu Ravi Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of CSE Marri Laxman Reddy Institute of Technology and Management Hyderabad India

ISBN: (纸本)9798350376913

Brain disorders include a wide range of illnesses, including mental illnesses and neurological disorders, for which an early and precise diagnosis is essential to successful treatment. This review aims to give an in-depth analysis of current developments and problems in this important field by examining the evolution of machine learning (ML) with respect to data mining usage in the categorization of brain illnesses. The study starts by clarifying the need of prompt and accurate diagnosis in brain illnesses, highlighting the possibility of utilizing machine learning and data mining tools to supplement conventional diagnostic approaches. A thorough analysis of the literature demonstrates the wide range of approaches used for classifying brain disorders, from sophisticated deep computational systems to traditional machine learning methods. The exploration clarifies the complexities of model construction by covering important topics including feature selection, dataset qualities, and assessment measures. Numerous research show how well machine learning approaches may be used to differentiate between various brain illnesses, showing encouraging outcomes in regard to specificity, sensitivity, and accuracy. The report also outlines the difficulties those working in this subject encounter, such as the requirement for vast and diverse datasets, accessibility, and variety of data. Potential alternatives are also covered, including the creation of model-based interpretation for improved practicality and the synthesis of multimodal information. This study is an invaluable tool for scholars, employees, and health care providers working at the nexus of data mining, machine learning, and brain illness categorization. Through a comprehensive analysis of the present status of the sector and the identification of potential areas for advancement, this study adds to the continuing efforts to use technologies in the identification and therapy of brain illnesses. © 2024 IEEE.

关键词： Data mining

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AI in Space 15

AI in Space

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavyashree, N. Zareen, Afshan Amrutha, J. Sharath, M.N. Department of Computer Science and Engineering BGS institute of technology Karnataka B G Nagar India Department of CSE Rajeev Institute of Technology Hassan India

ISBN: (纸本)9798331300579

Artificial intelligence (AI) and its subset, machine learning (ML), are growing in acceptance within the space *** days, autonomous navigation, spacecraft health monitoring, and operational management of satellite constellations all make extensive use of ML ***, a lot of research on AI applications in space missions can be categorized into two main *** disadvantage of the first category is that it is out of date and ignores a number of noteworthy and recent advances in the field, such as the contributions of deep learning (DL) and bioinspired AI *** issue with the second category is that it describes specific AI techniques and algorithms in excessive depth, which might be *** to these limitations, a concise. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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MACPABE: Multi-Authority-based CP-ABE with efficient attribute revocation for IoT-enabled healthcare infrastructure

MACPABE: Multi-Authority-based CP-ABE with efficient attribu...

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作者： Das, Sangjukta Namasudra, Suyel Department of Computer Science and Engineering National Institute of Technology Patna Patna India

The Internet of Things (IoT) technology along with cloud computing has gained much attention in recent years for its potential to upgrade conventional healthcare systems. Outsourcing healthcare data to a cloud environment from IoT devices is very essential as IoT devices are lightweight. To maintain confidentiality and to achieve fine-grained access control, the ciphertext policy attribute-based encryption (CP-ABE) technique is utilized very often in an IoT-based healthcare system for encrypting patients' healthcare data. However, an attribute revocation may affect the other users with the same attribute set, as well as the entire system due to its security concerns. This paper proposes a novel CP-ABE-based fine-grained access control scheme to solve the attribute revocation problem. The proposed technique includes multiple attribute authorities to reduce the work overhead of having a single authority in the traditional CP-ABE systems. In addition, the proposed scheme outsources the decryption process to a decryption assistant entity to reduce the decryption overhead of the end-users. To prove the efficiency of the proposed scheme, both formal security analysis and performance comparisons are presented in this paper. Results and discussion prove the effectiveness of the proposed scheme over some well-known schemes. © 2022 John Wiley & Sons, Ltd.

关键词： Internet of things

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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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Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model

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SN computer science 2024年第8期5卷 1-15页

作者： Chakraborty, Sanjay Pandey, Saroj Kumar Maity, Saikat Dey, Lopamudra Department of Computer and Information Science (IDA) REAL AIICS at Linköping University Linköping Sweden Department of Computer Science and Engineering Techno International New Town Kolkata India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata India Department of Biomedical and Clinical Sciences Linköping University Linköping Sweden

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other hand, is a key enabler for a wide range of IoT applications. Novel attacks can easily evade existing signature-based detection methods and are extremely difficult to detect, even going undetected for years. Existing machine learning models have also failed to detect the attack and have a high rate of false positives. In this paper, a rule-based deep neural network technique has been proposed as a framework for addressing the problem of detecting novel attacks. The designed framework significantly improves respective benchmark results, including the CICIDS 2017 dataset. The experimental results show that the proposed model keeps a good balance between attack detection, untruthful positive rates, and untruthful negative rates. For novel attacks, the model has an accuracy of more than 99%. During the automatic interaction between network devices (IoT), security and privacy are the primary obstacles. Our proposed method can handle these obstacles efficiently and finally identify, and classify the different levels of threats. A brief set of comparisons on detection accuracy and time measurements with some state-of-the-art methods are discussed in this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection Classification Deep Learning Machine Learning Network-attack

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Enhanced Multiclass Brain Tumor Detection in MRI Using Similarity-Navigated Graph Neural Networks with Banyan Tree Growth Optimization 4

Enhanced Multiclass Brain Tumor Detection in MRI Using Simil...

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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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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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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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Forecasting the Impact of Deep Learning on Data science Practices: A Comprehensive Analysis 15

Forecasting the Impact of Deep Learning on Data Science Prac...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Acharjya, Kalyan Devaraj Verma, C. Umamaheswari, B. Shelke, Priya M. Kumar, Mukesh Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350370249

It comprehensively discusses the effect of deep mastering on data technology practices. Deep studying has advanced to become a powerful tool for information mining, pattern recognition, and feature engineering. It is miles Information scientists are increasingly using the accuracy and efficiency of information science initiatives. Furthermore, the era has stepped forward hastily to one of these excessive levels. Many leading facts scientists throughout numerous domains have begun to contain deep mastering into their facts technological know-how workflows. In this paper, we examine and forecast the modern-day and destiny impact of deep gaining knowledge of data technology practices. We start by analyzing the distinct advantages of deep learning, including progressed accuracy, reduced guide attempt, more suitable automation, and advanced scalability. We then become aware of extraordinary applications in which deep learning is being applied and evaluate its impact on the respective information science pipelines. Sooner or later, we provide a comprehensive forecast of the capability applications of deep getting to know within the records technology subject inside the near and far destiny. Notably, we recall the capacity of deep studying to force essential modifications in how information mining, analytical choice-making, and large records packages. Through our analysis, we will forecast the ability of deep studying to revolutionize statistics technological know-how practices in extraordinary ways. © 2024 IEEE.

关键词： Automation Practices Processing technology Unprecedented

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