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Intelligent Technologies (ACOIT), Asian Conference on

作者： D.S.Eunice Little Dani K. Rohini Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The rapid expansion of geospatial data analysis has led to an increasing need for systems that can respond quickly. Storing and analyzing extensive spatiotemporal datasets is crucial for extracting valuable information for location-aware applications like route planning and weather forecasts. Efficiently coordinating data sources and internet services is crucial for extracting results within time constraints, given the current surge in processing geographic searches. Privacy models that incorporate fog computing provide a significant advantage in terms of cost reduction and low latency. However, in order to address consumers' concerns about data exposure, recent efforts have been made to integrate fog computing for enhanced privacy. Through the use of simulated outcomes, it has been shown that the privacy protection offered by the Geo-Fog framework for localization services is highly effective. The system demonstrated average latency values as low as 23 ms for query loads of 200. Furthermore, the CPU usage remained below $37 \%$ even under the maximum query load of $\mathbf{5 0 0}$. Fog computing plays a crucial role in the advancement of location-based applications that prioritize efficiency and privacy. By enabling fog devices or servers to handle geospatial queries within the geographic environment, it ensures optimal management.

关键词： Privacy Data privacy Web and internet services Random access memory Weather forecasting Geospatial analysis Central Processing Unit Spatial resolution Protection Edge computing

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Navigating the Blockchain Landscape with a Comprehensive Survey of Smart Contracts, Ethereum, and Consensus Mechanisms 4

Navigating the Blockchain Landscape with a Comprehensive Sur...

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4th IEEE International Conference on Data Engineering and Communication Systems, ICDECS 2024

作者： Suresh Kumar, N. Dolly Brinthakumari, S. Partheeban, N. School of Computer Science and Engineering Ramanagara India Jagan Institute of Management Studies Department of Information Technology Delhi India New Horizon Institute of Technology and Management Department of Computer Engineering Thane India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350393354

In the dynamic landscape of blockchain technology, this research paper meticulously explores and contrasts two pivotal paradigmsSmart Contracts and Traditional Contractswithin the overarching framework of decentralized agreements. In the pursuit of this, the research diligently examines the subtle differentiations inherent in the various aspects of execution, intermediaries, trust, transparency, speed, costs, flexibility, security, and enforceability. While Smart Contracts, executed automatically through code on blockchain platforms such as Ethereum, offer unprecedented efficiency and transparency, Traditional Contracts, reliant on manual execution and potential intermediaries like lawyers, maintain a familiar yet potentially more cumbersome approach. The research extends its focus to Ethereum, a groundbreaking blockchain platform, spotlighting its instrumental role in upholding data security and integrity. The subsequent challenge under consideration is Ethereum's key aspects, such as decentralization, immutability, cryptography, smart contracts, consensus mechanisms, transparent ledger, tamper resistance, encryption, and permissioned access. A pivotal facet of this study centers on the comprehensive examination and comparison of consensus mechanisms employed in blockchain systems. This research assesses and compares key parameters of prominent blockchain consensus mechanisms, emphasizing factors like accuracy, time efficiency, scalability, and energy efficiency. The research culminates in a robust conclusion that synthesizes key findings, emphasizing Ethereum's role in ensuring data security and integrity, and the intricate landscape of consensus mechanisms. © 2024 IEEE.

关键词： Smart contract

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Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images 9

Analysis of U-Net and Modified VGG16 Technique for Mitosis I...

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Lijo, Jithy Saleema, J.S. Karnataka Bengaluru India Christ Academy Institute for Advanced Studies Karnataka Bengaluru India Christ Deemed to Be University Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350350845

One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%. © 2024 IEEE.

关键词： Diseases

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CNN-LSTM: Development of Offline Signature Authentication 1

CNN-LSTM: Development of Offline Signature Authentication

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Ganapathy, Revathy Lingeshwaran, D. Suthanthiradevi, P. Harish Kumar, M.V. Vels Insitute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350359756

Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre-trained CNN models are used to extract features from signature images, which are then combined with handcrafted features such as HOG and some other geometric features of signature. Such combined features are passed to bidirectional LSTM model in which drop out layer undergoes classification which differentiate real and forgery signature. The proposed system has potential applications in document authentication and security, subsequently combination of CNN models and additional features provides more comprehensive representation of signature images resulting in improved accuracy. Three signature datasets are utilized namely GDPS, CEDAR, and BHSig-Bengali each with varying signature styles and image quality. Our experimental outcomes reveal that Bidirectional Convolutional LSTM along with handcraft features attained maximum accuracy in offline signature verification system. © 2023 IEEE.

关键词： Long short-term memory

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Advancements in Skin Disease Diagnosis Through Machine Learning-Driven Lesion Segmentation Techniques

Advancements in Skin Disease Diagnosis Through Machine Learn...

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International Conference on Inventive Computation Technologies (ICICT)

作者： M. Megala T.R. Nisha Dayana Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced-Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Skin diseases pose a significant global health challenge, demanding precise diagnosis and effective treatment. This survey paper delves into the realm of enhancing skin disease diagnosis via a pioneering machine learning algorithm focused on lesion segmentation. By amalgamating cutting-edge techniques like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), this algorithm strives to elevate accuracy and efficiency in this pivotal task. The results unequivocally establish the algorithm's superiority in accurately delineating skin lesions. Timely identification and meticulous characterization of skin ailments substantially influence patient outcomes. This contribution augments the corpus of knowledge in machine learning-based medical image analysis. The amalgamation of sophisticated techniques within this algorithm unfurls new horizons for subsequent exploration and innovation in skin disease analysis. The continuous evolution of this field harbors the potential to revolutionize dermatological practice, thereby enhancing the global management of skin disorders. The machine learning algorithm for skin lesion segmentation unveils a propitious avenue for accurate and efficient skin disease diagnosis. The far-reaching impact on patient care and the broader dermatological sphere underscores the imperative of further investigation and validation of this approach.

关键词： Surveys Technological innovation Machine learning algorithms Accuracy Generative adversarial networks Skin Lesions Medical diagnosis Convolutional neural networks Diseases

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Energy-aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

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Software - Practice and Experience 2024年第2期54卷 109-148页

作者： Hashemi, Sayed Mohsen Sahafi, Amir Rahmani, Amir Masoud Bohlouli, Mahdi Department of Computer Engineering Qeshm Branch Islamic Azad university Qeshm Iran Department of Computer Engineering South Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences Zanjan Iran Research and Innovation Department Petanux GmbH Bonn Germany Zanjan Iran

The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location-aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study. © 2023 John Wiley & Sons Ltd.

关键词： Quality of service

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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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Survey on Sentiment Analysis using Deep Learning 11

Survey on Sentiment Analysis using Deep Learning

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Sharmila, K. Devi, N. Shyamala Devi, R. Vels Institute of Science Technology and Advanced Studies Department. of Computer Science Chennai India

ISBN: (纸本)9781665487320

The text mining is technique that extract data from the semi structured or unstructured text and finds pattern it thought as knowledge discovery from the text. Deep learning is most powerful machine learning technique that is used to extract the feature of the text and from the feature which is identified gives the prediction for the opinion from the various text. The opinion which is mined that helps to decide the social people opinion and sentiments that will be used as an input to various business decision. This article provides overview of sentiment analysis and deep learning approach. © 2022 IEEE.

关键词： Sentiment analysis

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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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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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