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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Sakthivel, M. Patil, Sonal Kamarunisha, M. Pokhariyal, Rajesh Pal, Subharun Tiwari, Mohit Malla Reddy College of Engineering Department of CSE-AIML Hyderabad India G H Raisoni Institute of Engineering and Business Management Jalgaon Department of CSE-IT Maharashtra India Department of Computer Applications Tamilnadu Perambalur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu & Kashmir India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350340600

H ead and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer in the world. Despite improvements in diagnosis and therapy, the five-year survival rate for patients with advanced HNSCC has risen only slowly. This is mostly due to the high number of patients who present with an advanced disease stage at diagnosis, as well as the high frequency of relapse and secondary tumors. Protein biomarkers that aid in the early diagnosis of primary HNSCC or relapse hold promise for improving clinical outcomes. Finding early changes in the mucosal linings previous to the development of invasive tumors and properly assessing the likelihood of malignant transformation, both currently tough activities, may be important. The proposed procedure makes use of preprocessing, feature extraction, segmentation, and model training. Anisotropic Diffusion Filter and Gaussian Filter are used in preprocessing. Extraction of the techniques follows preprocessing. The Grey Level Run Length matrix is used for feature extraction. Segmenting the nucleus is a highly recommended step in the segmentation process. Training is performed using an RCNN- SVM model. When compared to two other models, RCNN and SVM, the proposed method excels. © 2023 IEEE.

关键词： Support vector machines

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Reduce Low-Frequency Distributed Denial of Service Threats by Combining Deep and Active Learning 1

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9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Shukla, Aditya Kumar Sharma, Ashish Sengar, Sandeep Singh Department of Computer Engineering and Applications GLA University NH#2 Delhi Mathura Highway Post Ajhai Uttar Pradesh Mathura India Department of Computer Science Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

This study introduces substantial contributions to the field of Low-Rate Detection of Distribution Denial of Service (DDoS) Attacks, leveraging convolutional neural networks (CNNs) with an attention mechanism and incorporating active learning with semi-labelled data. These contributions collectively enhance the accuracy, efficiency, and adaptability of DDoS detection systems. Additionally, the study introduces active learning strategies into the learning process. By actively selecting instances for manual labelling, the study reduces the burden of extensive manual labelling efforts and enhances the model’s scalability. In the dynamic realm of cybersecurity, where threats evolve rapidly, active learning ensures the model’s adaptability with minimal human intervention. Furthermore, this research addresses the challenge of scarce labelled data in real-world cybersecurity contexts. By harnessing semi-labelled data efficiently and pairing it with active learning, the study streamlines the detection and defence against DDoS assaults with low attack rates. This is particularly relevant in situations where procuring an abundance of labelled data is impractical or cost-prohibitive. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Active learning Attention mechanism CNNs Cybersecurity Low-rate DDoS detection Semi-labelled data Threat mitigation

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A Systematic Review of Deep Learning Approaches for Vessel Segmentation in Retinal Fundus Images 2

A Systematic Review of Deep Learning Approaches for Vessel S...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Hegde, Govardhan Prabhu, Srikanth Gupta, Shourya Prabhu, Gautham Manuru Palorkar, Anshita Srujan, Metta Venkata Bhandary, Sulatha V Department of Computer Science and Engineering India Department of Data Science and Computer Applications India Department of Information and Communication Technology Manipal Institute of Technology Karnataka Manipal576104 India Department of Ophthalmology Kasturba Medical College Karnataka Manipal576104 India

Retinal vessel segmentation (RVS) is important to accurately differentiate retinal vasculature for diagnosing and monitoring various ocular and systemic diseases. The traditional methods for RVS have mostly involved supervised learning, although semi-supervised and unsupervised techniques are on the rise. This paper reviews the increase in complexity of developments in RVS primarily after 2020. The methods were chosen to cover both the gradual transition over time and a variety of unorthodox or combinatorial approaches. This includes convolutional neural networks, encoder-decoder models, generative models, and other multi-modal or hybrid techniques. CNN approaches discussed employ Zero Phase Component Analysis, Global Contrast Normalization, and reinforcement learning. Encoder-decoder models include approaches such as the use of skip and residual connections, spatial attention, and atrous enhancement U-Net. Generative models propose short link connections, recurrent residual blocks, and multi-scale features to refine convolutional blocks. Hybrid methods involve the use of connectivity features, the MISODATA Algorithm, cross-domain adaptation, and multiple filters (such as morphological, match, and Gabor). All the frameworks are compared based on their performance on the benchmark dataset DRIVE to provide a comprehensive understanding of the current state of RVS. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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Smart Home Automation with IoT Sensor Systems for Energy Conservation using Zigbee and Wifi 5

Smart Home Automation with IoT Sensor Systems for Energy Con...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Jagannathan, Srinivasan Deepa, V. Sakthi Karthi Durai, B. Rajan, M. Sundar Celin, J. Jeya A. Dhamodaran, Sasikala Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India Tiruppur Kumaran College for Women Pg and Research Department of Computer Science Tamil Nadu Tiruppur India Kalasalingam Academy of Research and Education Computer Science and Engineering India Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Kalasalingam Academy of Research and Education Department of Information Technology Viruthunagar Krishnankoil626126 India Rajasthan Jaipur India

ISBN: (纸本)9798350379945

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency. © 2024 IEEE.

关键词： Smart homes

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A Novel Architecture for Developing IoT Solutions Applied to Healthcare 2

A Novel Architecture for Developing IoT Solutions Applied to...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Levina Kaliyamoorthi, P. Dattu, Vinjamuri S.N.Ch. Dineshnath, G. Jaiganesh, S. Dobhal, Dinesh Chandra Robin, C. R. Rene Kns Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle517325 India Aditya Engineering College Department of Mechanical Engineering Andhra Pradesh Surampalem533437 India Kl University Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Department of Master of Computer Applications Tamil Nadu Dindigul624622 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

The objective of this research study is to reduce the complexity and increase the flexibility to create and maintain computational solutions applied to health, where the scenarios are quite complex and dynamic. The main contributions of HealthDash are: (1) Provide dynamism in the process of creating IoT applications, both in the fog layer and in the cloud layer, through unification action of technologies adopted at both levels;(2) provide means to reduce the volume of information transmitted between the fog layer and the cloud layer, enabling the analysis and filtering of data closer to the edge of the network. This article proposes a solution for the development of IoT applications for health called HealthDash, based on the data flow-oriented programming paradigm, allowing to indicate in which layer of the architecture (fog or cloud) the data flows, which process data from the monitored patients, will be executed. © 2023 IEEE.

关键词： Internet of things

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Machine Learning Approach to Analyze Breast Cancer 10th

Machine Learning Approach to Analyze Breast Cancer

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10th International Conference on Frontiers of Intelligent Computing: Theory and applications, FICTA 2022

作者： Dash, Satya Ranjan Roy, Saurav Mohanty, Jnyana Ranjan Meedeniya, Dulani Mishra, Manoj Ranjan School of Computer Applications KIIT University Odisha India School of Biotechnology KIIT University Odisha India Department of Computer Science and Engineering University of Moratuwa Sri Lanka India

ISBN: (纸本)9789811975233

Machine learning has opened a new era of effective and easy ways of data analysis in biological research. The use of high throughput data analysis tools and libraries facilitated cancer research among many other benefits. Breast cancer is one of the main reasons for mortality among women. Apart from traditional molecular detection, it is also necessary to find an alternate approach. With the advent of modern technology, clinicians and healthcare providers can improve diagnosis accuracy and detection. In this study, we use a simple layer-based deep learning approach to investigate the malignancy of breast cancer. Our finding includes shows over 97% accuracy with the ANN-based machine learning approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

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computer Systems science & engineering 2022年第11期43卷 439-454页

作者： Ravindra Parshuram Bachate Ashok Sharma Amar Singh Ayman AAly Abdulaziz HAlghtani Dac-Nhuong Le School of Computer Science and Engineering Lovely Professional UniversityPunjab144001India Department of Computer Science and IT University of JammuJammu and Kashmir180006India School of Computer Applications Lovely Professional UniversityPunjab144001India Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia School of Computer Science Duy Tan UniversityDanang550000Vietnam Institute of Research and Development Duy Tan UniversityDanang550000Vietnam

Communication is a significant part of being human and living in the *** kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different *** application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models ***,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional *** is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and *** paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median *** preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech *** optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.

关键词： Deep learning grasshopper optimization algorithm recurrent neural network speech recognition word error rate

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Integration of Clinical and Imaging Data for Enhancing Oral Cancer Detection Using Deep Learning

Integration of Clinical and Imaging Data for Enhancing Oral ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Shakila, M. Dhasaratham, M. Saranya, N. Naga Thotapalli, Aruna Telrandhe, S.V. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Tkr College of Engineering and Technology Department of Information Technology Hyderabad India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Cvr College of Engineering Department of Information Technology Telangana Hyderabad India Guru Nanak Institute of Engineering & Technology Department of Etc Nagpur India

ISBN: (纸本)9798350360660

This study aims at unlocking the integration of clinical and imaging data using the deep learning model for enhancing the diagnosis of oral cancer cases. Therefore, for image analysis, in this paper proposed to major on CNNs while for clinical data, our Research used RNNs and hence came up with a unique architecture. The proposed model was trained on the patients' records and Histopathological Images Database from 5000 patients. As for the overall measure of accuracy of all of the constructed classifiers applying our integrated model the result was 94 %. Specificity achieved for the system was 7% while sensitivity was 93% for the oral cancer. 2%, sensitivity of 69.1%. This is an improvement of the previous methods that required either imaging or clinical data to obtain accuracy approximated at 85-90 percent. As a unique effectiveness factor of increasing the accuracy of oral cancer diagnosis, it is proposed to use an integration of clinical and imaging data through deep learning, which can also result in inclusion of the diagnosis at the early stages of the disease, thereby enhancing the prognosis of the illness. © 2024 IEEE.

关键词： Lung cancer

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Risk Modelling and Prediction of Financial Management Using FTL-CSGRU Approach 1

Risk Modelling and Prediction of Financial Management Using ...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Mothilal Nehru, G. Kudare, Roopali P. Karnawat, Amrita Tatia Rohini, J. Singh, Saurabh Nishant, Neerav SRM UNIVERSITY Faculty of Science & Humanities Department of computer Applications Chengalpattu India PCET's S B Patil Institute of Management Department of MBA Maharashtra Nigdi Pune India Indira School of Business Studies Tathawade Maharashtra Pune India Erode Sengunthar Engineering College Computer Science and Engineering Tamil Nadu Perundurai India Prestige Institute of Management and Research Department of Finance Indore India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

The need of building information technology is becoming more evident to those working in corporate financial management as the digital era arrives. An increasing number of modern societies are using enterprise resource planning (ERP) systems for their financial management needs. That way, companies can collect financial data in real-time and analyze it later. The steps of preprocessing, feature extraction, and training the model are all sequentially laid out. When it comes to statistical approaches used for data preprocessing, z-score normalization, decimal scaling, and min-max all offer very accurate results. You get the greatest results with MCML when you compare it to NCA, PCA, and Cluster-cluster LDA, the four approaches that comprise feature extraction. The suggested method employed an FTL-CSGRU strategy for model training. This method seems antiquated in comparison to the cutting-edge FTL and GRU. The statistics showed a significant improvement with an accuracy rating of 97.36%. © 2024 IEEE.

关键词： Enterprise resource planning

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Predictive Modeling for Disease Progression in Chronic Conditions Using Machine Learning 6

Predictive Modeling for Disease Progression in Chronic Condi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Umamaheswari, T.S. Dhaygude, Amol Dattatray Dewangan, Omprakash Krishnan, T. Yerpude, Poonam Swarnkar, Suman Kumar Jayagovind Harigopal Agarwal Agarsen College Department of Computer Applications Chennai India University of Washington Department of Data Science Seattle United States Kalinga University Department of CS & IT Chhattisgarh Raipur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Indian Institute of Technology Department of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Department of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350304480

Predictive modeling of disease progression in chronic conditions is a crucial task in healthcare, as it enables early identification and personalized intervention for patients at risk of adverse outcomes. In recent times, there has been a notable advancement in the field of machine learning techniques, which has demonstrated considerable potential in accurately forecasting the evolution of diseases. This development has proven to be highly beneficial for healthcare professionals, as it equips them with vital insights that can significantly enhance the management of patients. This research paper provides a thorough examination and comparative evaluation of different machine learning algorithms in the context of forecasting the course of diseases in chronic situations. We explore the use of diverse datasets containing longitudinal patient records, clinical variables, and disease-specific markers to train and evaluate predictive models. The study also investigates the impact of feature selection and data preprocessing techniques on the model's performance. Moreover, we assess the generalizability and robustness of the models across different chronic diseases to determine their potential for broader clinical applications. The results demonstrate that machine learning models can effectively predict disease progression, with certain algorithms outperforming others in specific scenarios. The research findings presented in this study offer significant contributions to the understanding and implementation of machine learning techniques for predicting disease development. The aforementioned realizations have the potential to be of substantial assistance to medical professionals in terms of making well-informed decisions, which will ultimately lead to better outcomes for patients. © 2023 IEEE.

关键词： Chronic conditions Clinical applications Clinical variables Data preprocessing Disease progression Feature selection Generalizability Healthcare Longitudinal patient records Machine learning Patient outcomes Predictive modeling

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