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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Rolly Kumar Sagar, Pramod Malhotra, Sonal Kulshreshtha, Kushagra Srm Institute of Science and Technology Computer Science & Engineering UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Computer Engineering & Applications Up Mathura India

ISBN: (纸本)9798350371055

This paper proposes a unique resource allocation technique for 6G networks the use of synthetic intelligence (AI). To assist the evolution of cellular networks, aid allocation mechanisms must be designed to successfully allocate network assets and assist the large wide variety of to be had offerings. Thus far, this has been implemented with the aid of traditional algorithms which possess an upper restrict on their talents. AI-based useful resource allocation offers superior adaptively and scalability to such algorithms, through gaining knowledge of from historic records and feedback from the person terminals. On this paper, an improved aid allocation algorithm primarily based on the integration of AI technology is proposed, underneath the perspective of optimizing both electricity performance and network overall performance. The proposed set of rules relies on a deep Q-learning framework, taking gain from deep neural networks to dynamically examine the most reliable resource allocation strategy. Simulation consequences display an improvement in network performance and energy performance in comparison to traditional optimization schemes. © 2024 IEEE.

关键词： dynamically mechanisms propose schemes technique

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Dragonfly Algorithm and Variants for Feature Selection: A Review

Dragonfly Algorithm and Variants for Feature Selection: A Re...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Prinson, P.T. Geetha, Angelina Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314946

Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach the analysis with an open mind. By doing so, it is possible to gain valuable insights and make informed decisions that can help solve complex real-world problems. By removing redundant, irrelevant, and noisy data, feature selection (FS), a difficult machine learning problem, attempts to lessen the number of features while maintaining a decent degree of classification accuracy. Feature selection is intended to reduce features. This immediately reduces the search space and aids machine learning algorithms in using only the most significant features. A review of Dragonfly algorithms and variants is presented in this paper, emphasizing their main characteristics. Eventually, the conclusion concentrates on the present research on Dragonfly Algorithm, emphasizing its limitations with suggested solutions that lead to future directions © 2023 IEEE.

关键词： Swarm intelligence

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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IAOMDR: Improved Aquila Optimized Hybrid deep learning Model for Classification of Diabetic Retinopathy 2

IAOMDR: Improved Aquila Optimized Hybrid deep learning Model...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Saju, Binju Barwal, Sahil Asha, V. Kumar, Shubham Tressa, Neethu Nikhil, T. Adi Shankara Institute of Engineering & Technology Department of Computer Science-AI Engineering Kalady India New Horizon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350328202

One of the scariest illnesses that causes irreversible blindness is Diabetic Retinopathy (DR). As a result, early exposure to Diabetic Retinopathy can help to preserve vision. The study proposes a hybrid model to classify Diabetic Retinopathy images. Initially, pre-processing of the photographs includes resizing them. Grey scale translation and noise removing using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is as opposed to other studies in literature. The proposed model has acquired F1 score of 98.98%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 99.1%, and precision of 99%. © 2024 IEEE.

关键词： Median filters

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Enhancing Medical Image Segmentation with Recurrent Neural Network Architectures 2

Enhancing Medical Image Segmentation with Recurrent Neural N...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Masson, Palak Sharma, Deepak Yadav, Kanchan Sethi, Tarun Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Srm Institute of Science and Technology Department of Computer Applications UP Modinagar India Gla University Department of Mechanical Engineering UP Mathura India Iamr Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

Scientific imaging is a swiftly evolving and developing vicinity of healthcare. Greater accurate segmentation of clinical images is paramount for correct diagnosis. Recent breakthroughs in profound mastering studies have allowed researchers to faucet into a powerful but unstructured information form - images. With the usage of recurrent neural network architectures (RNNs), researchers are exploring the capacity of this shape of facts to acquire better consequences from scientific photograph segmentation. RNNs are regarded for their flexibility in encoding temporal relationships and processing established statistics. In medical image segmentation, this flexibility aids in recognizing complicated styles in photographs and helps the model to localize features inside the image better. Furthermore, the networks can run on images of various sizes, ensuring that models can handle photos created using unique imaging modalities. Additionally, RNNs can examine large annotated datasets and, consequently, help supply meaningful segmentation outcomes. Recent advances in using RNNs for medical photo segmentation have shown promising effects. RNNs have been used to phase colon anatomy, mind tumors, and lymph nodes. U-internet-primarily based segmentation fashions that include RNNs and feature-achieved high segmentation accuracy for medical photograph segmentation responsibilities have also evolved. Within destiny, RNN-based total models will remain superior and could be implemented in various medical imaging segmentation tasks. As such, RNNs will make scientific imaging segmentation extra green and accurate, allowing faster and more accurate analysis and treatment. © 2024 IEEE.

关键词： Achieved Recognizing Relationships Segmentation

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Hyperparameter Optimization of Software Quality Model Using Improvised Genetic Algorithm and EEM 1

Hyperparameter Optimization of Software Quality Model Using ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh, Rakhi Sharma, Vishnu Kaushik, Neeraj Malhotra, Ritu Rauthan, Hemlata Delhi Institute of Higher Education Department of Computer Application Greater Noida India ITS Engineering College Department Of Computer Science and Engineering Greater Noida India Galgotias College of Engineering and Tehnology Department of Computer Applications Greater Noida India GNIOT Institute of Professional Studies Department of Computer Applications Greater Noida India Delhi Institute of Higher Education Department of Computer Applications Greater Noida India

ISBN: (纸本)9798350356816

Software quality analysis has become one of the topmost concerns in the modern world and requires the massive attention of developers and testers during software development. Software quality analysis is a multidimensional notion and one attribute may not describe the software product's overall quality. Software developer's objective is to build a software product that attains the highest quality which depends on multifarious attributes namely functional suitability, maintainability, etc. However, analysis of software product quality is challenging in both the development and maintenance phases. This research is aimed at hyperparameter optimization of the software quality model using an improvised genetic algorithm and an enhanced ensemble model (EEM) approach. In this research, there has been shown the optimal possibility of software quality model hyperparameter optimization using the modified architecture of genetic algorithm along with EEM. To validate the performance of this suggested approach, PROMISE Repository datasets have been utilized. As a result, this proposed approach achieves all performance metrics very optimal. The evaluated values of accuracy, precision, recall, and F1- score are 99.45%, 98.83%, 97.28%, and 99.37%, respectively. In the future, this approach may be integrated with modified architectures of software quality models for automated software product quality analysis. © 2024 IEEE.

关键词： Software quality

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Improving recommendations utilizing users’ demographic information

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Quality and Quantity 2024年第6期58卷 5559-5575页

作者： Dey, Avick Kumar Dutta Pramanik, Pijush Kanti Singh, Pradeep Kumar Choudhury, Prasenjit Department of Computer Applications DSMS College West Bengal Durgapur India School of Computer Science & amp Engineering Galgotias University Greater Noida India Department of Computer Applications & amp Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science & amp Engineering National Institute of Technology Durgapur West Bengal India

The exponential increase in digital data has increased the amount of available online information. This complicates the user’s decision-making. Most online merchants and service providers utilize recommendation systems to solve this problem and meet customer needs. The traditional collaborative filtering based approach faces enormous challenges in providing potential personalized recommendation results. The demographic information of users may improve personalized recommendation results. This research proposes an improved recommendation approach based on users’ demographic information. Compared with traditional collaborative filtering-based approaches, this approach provides improved results. The experimental results show the enhanced prediction accuracy of the proposed approach and significantly lower errors when experimenting with the MovieLens dataset. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Collaborative filtering Demographic information MAE Recommendation system RMSE

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Improved AO based Classification Framework for Eye Diseases 2

Improved AO based Classification Framework for Eye Diseases

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Saju, Binju Asha, V. Mathew, Jincy C Sangma, Sengcheng Ghosh, Shouvik Sivaguru, K. Adi Shankara Institute of Engineering & Technology Department of Computer Science - AI Engineering Kerala Kalady India New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350327533

A sensory organ that reacts to visible light, the human eye is a component of the sensory nervous system. A new approach is needed since, although physical detection of the eye condition is still a possibility, it is challenging and time-consuming. The study recommends a hybrid paradigm to classify various eye illnesses. Prior to further processing, pictures are first reduced in size, made grayscale, and noise-free using a combination of the Weiner and Median filters. Histogram equalization based on a Gaussian Mixture is also used to enhance pictures. Picture features are recovered with the aid of a thick convolutional neural network. An improved Aquila optimized ResNet101 model is used for classification. The effectiveness of the models is contrasted with that of related studies in the literature. A F1 score of 98.18%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 98.91%, and precision of 98% were attained by the suggested model. © 2024 IEEE.

关键词： DCNN Diabetic retinopathy Gaussian Mixture IAO ResNet

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Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehensive Study using XGBoost Classifier and Feature Selection Techniques 2

Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehe...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nair, Rekha R. Babu, Tina Kishore, S. Vilashini, V. Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte University Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Karnataka Bengaluru India

Healthcare systems around the world have faced challenges due to the Coronavirus disease (COVID-19) epidemic, which has taken resources and attention away from long-term diseases like liver cancer. To identify the effect of COVID-19 on liver cancer, proposed an effective research work with Extreme Gradient Boosting (XGBoost) classifier. The identification of the most suitable feature is executed through the utilization of SelectKBest, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) techniques. A comparative analysis is carried out to ascertain the more precise feature selection, employing Decision Tree, K-Nearest Neighbors (KNN), CatBoost, LightGBM, and XGBoost algorithms. Further, the proposed work undertakes an analysis of the impact of COVID-19 on liver cancer, encompassing both the pandemic and pre-pandemic periods. In comparison to other feature selection techniques and models, SelectKBest feature selection with Xgboost classifier provided an accuracy of 0.92. The proposed research work provides valuable insights to healthcare professionals and policy makers, providing a better understanding of the challenges faced by liver cancer patients during the pandemic. By meticulously exploring feature selection techniques and employing advanced machine learning models, this study quantifiably demonstrates the effectiveness of the proposed methodology in addressing the impact of COVID-19 on liver cancer, contributing to a more comprehensive understanding of this critical issue. © 2024 Elsevier B.V.. All rights reserved.

关键词： Principal component analysis

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Empowering Early Detection: CervicalCareXG Revolutionizes Cervical type cancer Diagnostics 4

Empowering Early Detection: CervicalCareXG Revolutionizes Ce...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Verma, Shanti Suthar, Hiral Rami, Khyati L J University Department of Computer Applications Gujarat Ahmedabad India Silver Oak University Department of Computer Applications Gujarat India Sal Institute of Technology & Engineering Research Department of Computer Science Gujarat India

ISBN: (纸本)9798350354218

A XGBoost-Based System in Cervical type cancer Diagnostics are proposed in the current research work. Generally Cervical type of cancer will be a significant health concern worldwide, particularly in the regions with limited access on advanced healthcare facilities. On Early detection will plays a pivotal role on improving patient outcomes and by reducing mortality rates. In this study, the propose system termed CervicalCareXG, a novelistic system for cervical type cancer diagnostics depends on the XGBoost methodology. CervicalCareXG will utilizes a machine Learning-Driven approach to analyze patient data, also including demographic information, medical history, and cervical screening results, to Prediction the likelihood of cervical type cancer development. The XGBoost algorithm, known for its prediction accuracy and efficacy in handling complex health related datasets, is employed to train a predictive model with a capable of identifying high-risk individuals who may requires further diagnostic evaluation or preventive interventions. Through comprehensively evaluation using reality patient data, CervicalCareXG may demonstrates a promising performance in terms of sensitivity, specificity, and area under the receivers operating characteristic curve (AUC-ROC). Thus, it showcases its potential as a valuable tool for healthcare professionals on cervical type cancer screening programs. By harnessing the power of machine Learning-Driven and XGBoost, CervicalCareXG with an aim to contribute to the early detection and management of cervical type cancer, ultimately by improving patient outcomes and reducing the burden of this devastating disease. © 2024 IEEE.

关键词： Diseases

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