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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Anitha, A. Nair, Anjana Kamaraj, Balakrishnan Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India Madurai Medical College Tamilnadu Madurai India

ISBN: (纸本)9798350376135

Clinical decision-making relies heavily on accurate brain tumor diagnosis from MRI imaging. Manual interpretation is time-consuming and error-prone. To enhance efficiency, automated techniques using deep neural networks (DNNs), particularly Convolution Neural Networks (CNNs), are proposed. This study incorporates explainable artificial intelligence (XAI) for interpretability. By addressing the need for automated and accurate diagnosis, this research aims to improve patient care and treatment outcomes, providing medical professionals with a dependable tool for brain tumor identification in clinical practice. © 2024 IEEE.

关键词： Deep neural networks

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A Simple Algorithm to Secure Data Dissemination in Wireless Sensor Network 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Sonai, Veeramani Bharathi, Indira Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India

ISBN: (数字)9789819981298

ISBN: (纸本)9789819981281

Wireless sensor networks are heavily utilized in numerous applications in both industry and science. As the original data is transferred from the sensor node to the sink node, unauthorized individuals attempt to access it, breaching confidentiality. The first step in enhancing the security of data transmission in a wireless sensor network (WSN) is figuring out the best method for encrypting data as it moves from the sensors to the central node. In order to move encrypted data from different sensor nodes to sink nodes efficiently in wireless sensor networks, this paper introduces a simple data encryption technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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Prediction and Analysis of Sentiments of Reddit Users towards the Climate Change Crisis 1

Prediction and Analysis of Sentiments of Reddit Users toward...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Ray, Sujana Senthil Kumar, A.M. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350336009

Climate crisis is one of the most talked about issues in the world today. In spite of a global agreement on the necessity to protect the earth from global warming, people still lack awareness on the graveness of the situation. Social media platforms such as Twitter, Facebook, Instagram, Reddit and others offer immense opportunities for people to become vocal and participate with their opinions and thoughts on these practical challenges by exchanging information and talking about them. By looking at their attitudes and the issues they discuss, it is possible to determine in this research how users of Reddit, one of the most well-known and popular social media platforms in the world, feel about climate change. Retrieved comments and posts are classified into two sentiment classes: Positive and Negative. To understand the sentiments, we find sentiment targets by comparing two neural networks CNN and RNN and using the more accurate model to predict sentiments of the comments in the test dataset and analyse the nature of climate change discussion over time. Although the computational maximal accuracy for the two models is comparable, it was discovered that the CNN model scored marginally better than the RNN in terms of average precision, average accuracy, and average loss. The examination of Reddit users' opinions demonstrates that the general attitude is negative, particularly when people acknowledge extreme weather events that have the potential to impact the public wellbeing framework. © 2023 IEEE.

关键词： Sentiment analysis

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A Comprehensive Review on Early Diagnosis of Alzheimer's Disease Detection

A Comprehensive Review on Early Diagnosis of Alzheimer's Dis...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, G.S. Nachiyappan, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

A progressive neurodegenerative Alzheimer's Disease (AD) shrinks (atrophy) the brain and kills brain cells. On a global scale, Finland and the United Kingdom have the highest prevalence of AD, with 54.7 and 42.7 cases per 100, 000 inhabitants respectively. In India, there are 14.60 AD cases for every 100, 000 people and it is ranked 137th in the world and 13th in India among the 50 causes of death. At the beginning of the disease, a person may forget things, or have trouble remembering recent conversations. As the disease progresses, a person may experience severe memory loss and difficulty performing everyday tasks. Consequently, the only way to predict AD at an early stage is to use treatments that prevent the disease from progressing. This paper provides an overview of the current approaches to AD diagnosis and detection, with a focus on the use of Biomarkers and discussed the advantages and disadvantages of Machine Learning (ML)/Deep Learning (DL) techniques in early detection of AD. It also reviews the collection of AD datasets and the pre-processing techniques used in the studies. Although DL methods gives the best outcome measures such as accuracy, precision, F-measures, recall and Area Under the Curve (AUC) still DL techniques have some limitations that can be overcome via Quantum Computing (QC). QC is currently emerging new technology based on quantum theory where qubits are used to store the data instead of bits. It accomplishes 1000 times faster and also solves the complex problem within a fraction of time than the classical computers. © 2023 IEEE.

关键词： Alzheimer's Disease Biomarkers Datasets Deep Learning Machine Learning Quantum Computing

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Stroke Disease Prediction Using Adaboost Ensemble Learning Technique 4th

Stroke Disease Prediction Using Adaboost Ensemble Learning T...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Ganachari, Sreenidhi Battula, Srinivasa Rao School of Computer Science and Engineering Vellore Institute of Technology Andhra Pradesh Amaravati India

ISBN: (纸本)9789819920990

This paper presents an approach to predict stroke utilizing a better model with Adaboost algorithm. Today the utilization of opportunely trained machine learning algorithms can be significantly utilized in fields such as medicine and data science to identify and provide solutions to quandaries that do not have a solution or if the current solutions are not efficacious. A stroke is a condition where blood arteries in the brain burst, harming the brain. The rigor of a stroke can be abated by early apperception of numerous stroke warning symptoms. Previous works have utilized sundry machine learning algorithms to determine stroke. Despite many research works in the literature indicating successful outcomes in preclinical stroke models, there has been no significant contribution to the medical field. Since one weak classifier cannot reliably predict an object’s categorization by itself, adaptive boosting, also known as AdaBoost, combines numerous weak classifiers in progressive learning to create a strong predictive classifier. It uses ensemble learning techniques to predict the accuracy which makes it better than the other algorithms discussed in the paper. The dataset used emanates from Kaggle with sundry parameters that have been taken into consideration. The machine learning algorithms considered are random forest algorithm, decision tree algorithm, SVM, logistic regression algorithm, KNN, Naïve Bayes, and AdaBoost algorithm to train different models and compare the results for the best model. Amongst all the algorithms AdaBoost algorithm gives us the best precision of 98.8%. Thus, the main aim of this paper is to propose a classifier—AdaBoost classifier that can be utilized by medical practitioners to detect the ailment in its early stages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Smartphone recommendation using popularity and collaborative filtering-based models 6

Smartphone recommendation using popularity and collaborative...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Paul, Aniket Kumar Nath Pandey, Trilok Kumar, Aswathi Manoj Moloparambil, Savio Sajan Kartheek, Surya Singh, Suraj Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350394702

With so many options available in the smartphone market, the question of which model is to be purchased, arises. The model proposed in our article will be trained with large datasets which can then suggest the perfect model with a given set of inputs without any hassle to search for hours on the internet in order to decide which phone is best suited to the customer in accordance with his/her needs. In this paper, it should be assumed that "Users"and "Reviewers"are used interchangeably in this paper as it is assumed that phone reviewers and users are the same. Therefore, our smartphone's recommendation system is designed to create a system that can suggest the best suited smartphones based on the choices or behaviors of the consumers. © 2023 IEEE.

关键词： Smartphones

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Jaya integrated binary whale optimized algorithm for preserving medical data

Jaya integrated binary whale optimized algorithm for preserv...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sudha, I. Ramesh, P.S. Jagadeesan, S. Murugan, Guru Vimal Kumar Gokulnath, C. Poongavanam, N. Department of Computer Science and Engineering SIMATS School of Engineering SIMATS Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India School of information Technology and Engineering Vellore Institute of Technology Tamilnadu Vellore India

ISBN: (纸本)9781032669663

Cloud Computing has the source of information to various sectors that the future generation duly depends on the resources of the cloud. Users can access a variety of network, storage, and platform features through the cloud. The users exploit the cloud’s resources in accordance with their needs, and the data is centralized for cloud storage. Hence, The main aim proposes a method called Jaya Binary Whale Optimization (JBWO), which combines the Binary Whale Optimization and Jaya algorithm (JBWOA) and modifies the Fully homomorphic encryption (FHE) Organism exhausting Advanced Encryption Standard (AES), for the cloud to start securely transmitting data.. By applying the proposed JBWO algorithm to generate the Data Protection coefficient (DP), the original data is retained. For the purpose of selecting the best resolution, privacy and utility parameters are used to calculate fitness. By combining the key vector and the Key Information Product (KIP) matrix with the EX-OR operation, the sanitized data are produced. By utilizing the key that the data owner provides, users can access the unique data. The strategy produced improved results in the analysis utilizing the Cleveland datasets and is capable of managing sensitive information with clearly specified privacy. The suggested privacy protection techniques effectively safeguard data while maintaining privacy, resulting in satisfied customers. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Cloud storage

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An Application to Detect Cyberbullying Using Machine Learning and Deep Learning Techniques

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SN computer science 2022年第5期3卷 401页

作者： Raj, Mitushi Singh, Samridhi Solanki, Kanishka Selvanambi, Ramani School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore 632014 India

Nowadays, a lot of people indulge themselves in the world of social media. With the current pandemic scenario, this engagement has only increased as people often rely on social media platforms to express their emotions, find comfort, find like-minded individuals, and form communities. With this extensive use of social media comes many downsides and one of the downsides is cyberbully. Cyberbullying is a form of online harassment that is both unsettling and troubling. It can take many forms, but the most common is a textual format. Cyberbullying is common on social media, and people often end up in a mental breakdown state instead of taking action against the bully. On the majority of social networks, automated detection of these situations necessitates the use of intelligent systems. We have proposed a cyberbullying detection system to address this issue. In this work, we proposed a deep learning framework that will evaluate real-time twitter tweets or social media posts as well as correctly identify any cyberbullying content in them. Recent studies has shown that deep neural network-based approaches are more effective than conventional techniques at detecting cyberbullying texts. Additionally, our application can recognise cyberbullying posts which were written in English, Hindi, and Hinglish (Multilingual data). © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cyberbullying Deep learning model Multilingual Real-time tweets Stack word embeddings

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PlantEIRNet: Plant Leaf Disease Detection Deep Neural Networks Using Farmland Images

PlantEIRNet: Plant Leaf Disease Detection Deep Neural Networ...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Geetha, S. Pearline, S. Anubha Chakarborty, Karmabir Vellore Institute of Technology School of Computer Science and Engineering Chennai campus Chennai India

ISBN: (纸本)9798350348910

The productivity and quality of crops are greatly influenced by various diseases and pests. These are one of a kind of natural disasters which affect the natural growth of plants and can even cause the death of the plants ultimately affecting the yield of the crops. Detection of these diseases is henceforth really important. Such plant disease detected via image processing or object detection techniques. Deep Learning techniques are highly used for these kinds of tasks since they significantly outperform the conventional approaches. This research paper proposed an enhanced Inception ResNet-V2 for detecting plant diseases and is called as PlantEIRNet. Also, various state-of-the-art convolutional neural networks such as InceptionV3, ResNet-50, VGG-16, AlexNet, MobileNet-V3 are compared with the proposed method. Plant diseases are detected inspite of real-world settings such as illumination, occlusion and varied viewpoints. Three datasets namely, PlantLD-11 dataset, PlantVillage dataset and FusedPD dataset are assessed for their performance using PlantEIRNet. The proposed enhanced Inception ResNet-V2 outperformed compared to other Convolutional Neural Networks such as Inception V3, ResNet50, VGG-16 and Inception ResNet-V2. © 2023 IEEE.

关键词： Convolutional neural networks

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： computerized tomography

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