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Efficient Lung Cancer Detection Based on Support Scalar Vector Feature Selection with Fuzzy Optimized-Multi Perceptron Neural Network Using Natural Language Processing

Efficient Lung Cancer Detection Based on Support Scalar Vect...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： Jabir. K S. Kamalakkannan J. Anita Smiles Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai India Department of Computer Applications SRMIST Chennai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Lung cancer is one of the most frequent types of cancer worldwide, and it has a high fatality rate. To improve survival chances and ensure a successful treatment, early identification of lung cancer is essential. The rapidly developing artificial intelligence discipline of Natural Language Processing (NLP) has great promise for transforming the understanding, identification, and management of lung cancer. Previously, NLP-based lung cancer prediction models may not always produce accurate results, as they rely on complex algorithms to analyze text data. Errors in language processing or interpretation can lead to incorrect predictions and potentially harmful decisions. This study suggests employing Fuzzy Optimized -Multi Perceptron Neural Network (FOMPNN) for lung cancer detection based on NLP and the Relative Support Scalar Vector Feature Selection (RSSCVFS) method for optimal attribute selection. To begin with, preprocessing involves finding null and missing values, and textual summaries based on stemming and tokenization. Then, the Semantic Lexicon-Based Approach (SLBA) is used to find the sentiment score. Furthermore, the sentiment score of each word in the medical summary can be added up to get the overall sentiment of the text. Based on dictionary terms, feature outcomes are added to patient records. Similar generalization patterns based on Linguistic Subset Pattern Mining (LSPM) are used to produce corresponding feature patterns. The RSSCVFS method is used to pick features based on their significance to minimize the feature dimension of the illness effect rate. To classify the risk of lung cancer, the FOMPNN is finally utilized to test the feature's weight. The proposed method proficiently produces better performance than other methods.

关键词： Fuzzy logic Accuracy Uncertainty Neural networks Lung cancer Predictive models Feature extraction

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Alzheimer's Disease Classification and Severity Level Identification in Image Processing Using Deep Learning Algorithms

Alzheimer's Disease Classification and Severity Level Identi...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： T Sangeetha Devi V. Raghavendran Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and impairs a patient's memory. It is progressive and incurable. Early identification can shield the patient from more brain cell damage and, as a result, help them avoid irreversible memory loss. The scientific community has employed a number of deep learning algorithms to automatically identify Alzheimer's patients. These comprise binary classification of patient scans into stages of AD as well as moderate cognitive impairment (MCl). Limited research has been done on the multiclass classification of Alzheimer's disease (AD) up to six distinct stages. This research proposes novel technique in Alzheimer disease detection with severity level analysis utilizing deep learning (DL) model. Input is collected as MRI brain images and processed for noise removal and smoothening. Then processed image classification and disease stage is detected using pre-trained multi-layer convolutional residual transfer Random Forest with Inception V3model. Experimental analysis is carried out in terms of training accuracy, mean average mean average precision, sensitivity, AUC for various MRI brain image dataset. Training accuracy attained by proposed technique is 96%, mean average precision of 93%, sensitivity of 95%, AUC of 90%.

关键词： Deep learning Training Sensitivity Magnetic resonance imaging Noise Brain cells Brain modeling

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Enhancing Arrhythmia Disease Classification Using MLP with Whale Optimization Approach

Enhancing Arrhythmia Disease Classification Using MLP with W...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： R. Mythili A.S. Aneetha Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Cardiac arrhythmias are a serious health danger. Thus, timely diagnosis and treatment depend on precise and effective categorization techniques. The paper addresses the implementation of Multilayer Perceptron (MLP) in conjunction with Whale Optimization-based feature selection, Particle Swarm Optimization (PSO), and Antlion Optimizer (ALO) for the categorization of Atrial Fibrillation (AFib) illnesses. Key performance indicators, including Accuracy, precision, and recall, will be examined to determine whether MLP using Whale Optimization for feature selection works better than conventional techniques. The results indicate that the suggested strategy dramatically improves the classification performance compared to established methods based on thorough experimentation and comparative analysis. The combination of MLP and Whale Optimization-based feature selection exhibits excellent recall, Accuracy, and precision and is a viable option for reliable AFib illness categorization. From the results obtained, the proposed MLP+WO gave an Accuracy of 90.74 % and precision and recall of 0.90 and 0.88, respectively. The language used is Python.

关键词： Accuracy Arrhythmia Neural networks Optimization methods Multilayer perceptrons Feature extraction Whale optimization algorithms

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Breast Cancer Detection by Improving Feature Selection and Classify using Ensemble Artificial Neural Network method

Breast Cancer Detection by Improving Feature Selection and C...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： G. Sridevi K. Sharmila Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies(VISTAS) Pallavaram Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

he most frequent cancer endured to the women is Breast Cancer (BC) and one in every eight women get affected by BC from their age of forty. According to the National Cancer institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. This study aims to detect and divide BC into two categories using the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark feature set and to select the fewest features to attain the highest accuracy. To this end, this study explores automated BC prediction using multi-model features and ensemble machine learning (EML) techniques. The sample have considered from WDBC dataset in which essential features have been selected through the BORUTA feature selection algorithm, which prepares conditional variables corresponding to features for a prediction model based on rough set theory (RST). This paper majorly focus on high accuracy in detection of BC through propose an advanced ensemble technique, which incorporates voting classification in the keras of Artificial Neural Network (ANN) techniques for the classifier to distinguish benign breast tumors from malignant cancers. In the feature extraction process, considered the most frequently repeated process to find the most important features of the WDBC that are pertinent to BC detection and classification. The combination of boruta algorithm with ensemble based ANN model has reduces the false positive and false negative errors using ensembling techniques that combine several base models to produce one optimal predictive model. The valuation of the proposed model accuracy as 97.36% is comparatively high than traditional ANN method and boruta with ANN model in detect

关键词： Accuracy Protocols Machine learning algorithms Rough sets Artificial neural networks Predictive models Feature extraction Prediction algorithms Breast cancer Classification algorithms

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Securing IoT Healthcare Data: The Power of Blockchain and Homomorphic Encryption

Securing IoT Healthcare Data: The Power of Blockchain and Ho...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： M Sathishkumar V. Raghavendran Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The rapid progress of the Internet of Things (loT) and the increasing use of medical software in this field have raised considerable concerns regarding the security and privacy of sensitive health data. In response to these difficulties, blockchain technology has arisen as a viable remedy, offering autonomous and unchangeable data storage as well as accessible records of transactions. Nevertheless, conventional blockchain systems continue to encounter constraints in maintaining data confidentiality. This study presents an innovative method for improving privacy protection in medical devices based on the Internet of Things (loT). It achieves this by combining homomorphic techniques for encryption with blockchain technology. Homomorphic encryption enables the execution of computations on data that has been encrypted without the need for decryption, ensuring the data's privacy during the entire computational procedure. Authorized parties can process and evaluate the encrypted data without disclosing the real contents, thus safeguarding patient privacy. In addition, this approach integrates smart contracts into the blockchain network to enforce access control and establish data-sharing policies. The smart contracts offer precise permission settings, guaranteeing that only authorized entities can access and exploit the protected data.

关键词： Data privacy Privacy Smart contracts Memory Medical services Software Blockchains

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Anwesha: A Tool for Semantic Search in Bangla

Anwesha: A Tool for Semantic Search in Bangla

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2022 International Conference and Workshop on Agglutanative Language Technologies as a Challenge of Natural Language Processing, ALTNLP 2022

作者： Das, Arup Kundu, Bibekananda Ghorai, Lokasis Gupta, Arjun Kumar Chakraborti, Sutanu Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai600096 India Centre for Development of Advanced Computing Kolkata700091 India

Bangla is a low-resource language that is highly agglutinative, and designing effective search and information retrieval systems over Bangla is quite challenging. This paper presents our explorations toward building (Anwesha), a prototype for a search engine in Bangla. To the best of our knowledge, this search system is the first such initiative in Bangla that facilitates retrieval of semantically related documents by use of diverse knowledge sources like WordNet, statistical co-occurrences (by way of Latent Semantic Analysis (LSA)) and external knowledge sources like Wikipedia (by way of Explicit Semantic Analysis (ESA)). We also present our efforts to overcome the limitations of existing spell-check and lemmatization approaches in Bangla and integrate them into Anwesha. In addition, we also present methods to explain search results by highlighting keywords that LSA or ESA reckons to be semantically related to the query. Since there is no Gold standard dataset available to evaluate the effectiveness of Bangla information retrieval systems, we have created a dataset containing query document relevance pairs in two distinct domains. We analyze the system’s performance on queries having different difficulty levels. Our technique could be adapted to facilitate effective semantic search in other low-resource, highly inflected languages. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Information retrieval systems

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A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems

A Novel Preprocessing Technique to Aid the Detection of Infe...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： J. Kalaivani A. S. Arunachalam R. Gobinath Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Science Christ University Bengaluru India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

An innovative preprocessing method for discerning infected areas in CT images of COVID-19 is described in this abstract. The methodology being suggested exploits the capabilities of artificial intelligence (AI) to improve disease detection communication systems. By employing sophisticated AI algorithms to preprocess CT images, the method seeks to increase the precision and effectiveness of COVID-19-associated area detection. The incorporation of artificial intelligence (AI) into communication systems facilitates enhanced image analysis, resulting in improved diagnostic capabilities and treatment strategizing. The study's findings demonstrate the potential of preprocessing techniques powered by artificial intelligence in augmenting communication systems with the aim of enhancing healthcare outcomes.

关键词： COVID-19 Quantum computing Communication systems Computed tomography Perturbation methods Noise Refining

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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A Framework for Tile Processing on Edge Servers for Roadside Traffic Surveillance 23

A Framework for Tile Processing on Edge Servers for Roadside...

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Proceedings of the 7th International Conference on Algorithms, computing and Systems

作者： Saumya Jaipuria Arani Bhattacharya Ansuman Banerjee Advanced Computing and Microelectronics Unit Indian Statistical Institute India Department of Computer Science Indraprastha Institute of Information Technology India

ISBN: (纸本)9798400709098

Roadside traffic monitoring is increasingly performed by deploying roadside high-resolution video cameras and then running computer Vision (CV) models on the video data. Since computer vision models are compute-intensive as they utilize Deep Neural Networks (DNNs), the data is usually sent to one or more edge servers located adjacent to mobile base stations. Recent techniques propose running CV models on tiles of videos separately to detect and track small objects. Several CV models exist, each with different requirements of compute and memory. Since more compute and memory-intensive CV models provide higher accuracy, a key challenge of such techniques is to determine which vision model should be used on which tile. This becomes even more challenging if multiple videos are processed by the same edge server. In this paper, we first formulate this problem of model selection on edge devices as an Integer Linear Programming (ILP) problem, and then propose a heuristic to solve it. Our experiments show that it is quite effective in practice.

关键词： computer Vision Edge computing Integer Linear Programming

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The Performance Analysis and Comparison of Azure and AWS Hypervisor using Different Workloads in Virtualization

The Performance Analysis and Comparison of Azure and AWS Hyp...

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International Conference on ent computing and Control Systems (ICICCS)

作者： J Mary Ramya Poovizhi R Devi Department of Computer Science Schools of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Department of Computer Applications Schools of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai

The abstraction of IT infrastructure enables the integration and pooling of IT resources to be shared across several applications to compensate for declining resources. Growing business needs virtualization offers a conceptual encapsulation of current computing resources. It generates computer systems unrestricted by physical design. The implementation objective of virtualization in cloud computing is to simplify the provision of products by providing a platform for maximizing complex IT resources in a scalable way, hence decreasing the cost of cloud computing. Hypervisors are one of the essential components of the virtualization of Hardware. Many operating systems can run simultaneously on a single physical server in a virtualized environment. For cloud computing, the hypervisor is the best way to deliver a variety of operational environments. This work will compare the cloud centre hypervisor Azure and AWS with different environments and workloads. The result shows not all hypervisors will provide the same energy and power conception with workloads and environments.

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