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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Srikantha, H. Joseph, Ginu Anie Yadav, Surendra School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of Nursing Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

This article identifies a correlation between the 3D Magnetic Resonance Imaging (MR) of patients with worrying brain harm (TBI) and the patient's medical results. The research team compares 3-D MR imaging outcomes with the patient's observe-up go-to facts, including neurologic assessments, Glasgow Coma Scale rating, and other parameters. The effects confirmed that patients who had a better degree of injury discovered from three-D MR scans had worse scientific effects in comparison to sufferers who had milder harm. This correlation became great for acute and subacute TBI patients. The authors endorse that 3-D MR scans can be used to predict the clinical results of TBI patients and can have the potential to inform treatment selections. © 2024 IEEE.

关键词： Article Correlation Glasgow Identifies Magnetic Resonance

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Improving Cancer Prediction in Recommender Systems through Gradient Boosting 15

Improving Cancer Prediction in Recommender Systems through G...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sengupta, Pompi Das Kharbas, Vikas Kumar Adarsha, H. Arka Jain University Department of Management Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

The analysis of various styles of most cancers is frequently reliant on accurate prediction models. Recommender systems are powerful equipment that allows the prediction of cancer development and recurrence based totally on information accumulated from affected person information. But, standard feed-forward neural networks (FFNNs) utilized in current cancer prediction fashions are limited via their potential to capture subtle nonlinear relationships among affected person variables. Gradient boosting is a device gaining knowledge of set of rules that makes use of an ensemble of weak selection bushes to produce an effective predictive version. This have a look at examines the capability for boosting algorithms to enhance the predictive ability of the FFNN fashions currently used in recommender structures for predicting most cancers progression and recurrence. The outcomes of our test reveal that boosting algorithms are able to boom model accuracy over FFNNs whilst additionally lowering training time. Moreover, nonlinear relationships between affected person variables are captured extra successfully by means of boosting than with FFNNs. those findings keep crucial implications for the development of powerful predictive models for cancer progression and recurrence. © 2024 IEEE.

关键词： Cancer Implications Networks Neural Predictive Produce Recurrence Relationships

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Prediction of Cardiac Arrest using Generalized Soft Computing Techniques 18

Prediction of Cardiac Arrest using Generalized Soft Computin...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Hota, Radhanath Dash, Sachikant Mishra, Sujogya Das, Subhalaxmi Lenka, S. Pradhan, G. Giet University Department of Computer Science Engineering Odisha Gunupur India Gandhi Institute for Education and Technology Department of Computer Science Engineering Odisha Gunupur India Odisha University of Technology and Research Department of Mathematics Bhubaneswar India Odisha University of Technology and Research Dept. of Computer Science and Engineering Bhubaneswar India Utkal University Department of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9789380544519

Cardiac-related diseases always lead to fatal consequences around the globe. This research includes vague data analysis of the data related to cardiac anomalies. This study proposes a method to develop a dimension reduction model using vague data related to cardiac diseases using rough set theory (RST). The overall research is divided into two different sections. The first section of this study proposes analyses of the raw cardiac-related data using RST, and the second section calculates the performance accuracy using Principal Component Analysis (PCA) applied to the universal data set;the performance measures also include a combination of two Universal data sets. The above claims are verified using statistical tests © 2024 Bharati Vidyapeeth, New Delhi.

关键词： PCA Performance and accuracy Rough Set Theory Statistical method Vague data

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Exploring Spectrum Sharing Algorithms for 6G Cellular Communication Networks 15

Exploring Spectrum Sharing Algorithms for 6G Cellular Commun...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pattanaik, Ashutosh Shweta Ahmed, Zahid School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Arka Jain University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

6G cell verbal exchange networks might be the successor to 5G networks, and their success relies heavily upon optimization measures that are enabled through the exploration of spectrum-sharing algorithms (SSAs). SSAs are hard and fast techniques implemented to maximize machine assets, performance metrics, and operational performance. That is performed by way of lowering interference, permitting spectrum contextualization, and improving the sharing of underutilized and congested spectrum. For 6G verbal exchange networks, a heightened attention on advanced database storage, distributed computing, and interference avoidance techniques must be taken a good way to ensure the maximum green use of be had spectrum. This paper explores a spread of SSAs for 6G mobile communication networks, consisting of dynamic spectrum get admission to, non-orthogonal more than one get right of entry to, and direct tool-enabled opportunistic spectrum usage. We evaluate current research that has been implemented to the improvement of SSAs and offer insight into future demanding situations and programs inside the subject. Furthermore, we talk about the significance of massive records and systems getting to know in the creation of SSA algorithms, providing capability enhancements to the ever-changing 6G communique networks. Standard, the usage of SSAs in 6G verbal exchange networks can make certain the most effective and green usage of be had spectrum, at the same time as additionally imparting extraordinary stages of aid flexibility © 2024 IEEE.

关键词： Cellular Contextualization Machine Learning Non-Orthogonal Spectrum-Sharing Successor

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Personalized Assistance Through Artificial Intelligence: Smart Voice Appliances 8th

Personalized Assistance Through Artificial Intelligence: Sma...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Jain, Archana Rathore, Yogesh Singh Maurya, Santosh Kumar Malik, Suraj Bansal, Nidhi Pandey, Mahima Shankar Saraswat, Bal Krishna Computer Science and Engineering Department School of Engineering and Technology IIMT University Uttar Pradesh Meerut India Computer Science and Application Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Computer Science and Engineering Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Galgotia College of Engineering and Technology Uttar Pradesh Greater Noida India Nagpur Campus Pune India

ISBN: (纸本)9789819766772

In today’s world of growing technology, we can do things we never thought we could do before, but to achieve these ideas, there is a need for a platform that can do all our work easily and comfortably. So, we humans have developed applications such as the Human Voice Assistant with the ability to interact with the environment in one of the most visible forms of human interaction namely HUMANVOICE. The most popular Android mobile app "Google Assistant" and "Google Voice Search" were developed by Google. Various applications such as Microsoft Cortana and Amazon Alexa are also used as voice assistants. The Apple iPhone’s voice assistant named "SIRI" helps the users to communicate with the services and applications of iOS by voice and respond to the user’s voice commands. We have built an application where the voice assistant will be available on a specific desktop or laptop. It can change the overall course of interaction between the user and the system. The system designed here is such that all the services provided by the system are accessible through the user’s voice commands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： engineering education

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Assessing the Robustness of Machine Learning Algorithms against Emerging Privacy and Security Threats in Medical applications 15

Assessing the Robustness of Machine Learning Algorithms agai...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rao, Srikrishna Baskar Goswami, Sarbojeet Goyal, Manish Kumar School of Engineering and Technology Department of Aerospace Engineering Karnataka Bangalore India ARKA JAIN University Department of optometry Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

Using gadgets to realize algorithms in medical packages can provide new insights and talents, including new approaches to diagnose illnesses or reveal treatments. Rising privacy and security threats pose potential dangers to those structures that can restrict their efficacy and average performance. To make sure tool mastering algorithms are resilient to those threats, it is critical to assess their robustness. This summary offers an assessment of techniques to evaluate the robustness of tool-getting-to-know algorithms against emerging privacy and protection threats in medical programs. First, we check the kinds of threats and discuss how they'll affect the general performance of machine studying algorithms. Secondly, we compare a selection of strategies for measuring the robustness of gadgets and gaining knowledge of algorithms, which include static, dynamic, and adverse check strategies. Ultimately, we speak about techniques for mitigating threats in scientific packages, which provides for incorporating relaxed layout thoughts in tool studying structures and utilizing smoke assessments and strain tests. Through this assessment, we aim to provide more facts on the techniques for assessing the robustness of gadget-studying algorithms against rising privacy and security threats in scientific programs. © 2024 IEEE.

关键词： Adversarial machine learning

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Brain Tumor Radiogenomic Classification using multilevel contrastive information fusion

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Procedia computer Science 2025年 259卷 260-268页

作者： Asmita Praveen Mittal Department of Computer Engineering Application GLA University Mathura Uttar Pradesh 281406 India

Brain tumors present significant challenges in diagnosis and treatment due to their complex and diverse nature. Radiogenomic analysis, which combines genomic and radiological data, has become important for advancing brain tumor research, with deep learning methods, particularly fusion-based approaches, making notable progress. However, current approaches face challenges in capturing both sequential and spatial features, as well as selecting effective fusion techniques. The research introduces a multi-fusion framework that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers using contrastive learning to enhance feature representation. This approach processes medical images through a neural network alongside a transformer backbone, merging outputs at various stages to improve dark knowledge differentiation between classes. While CNNs extract spatial features, transformers focus on patch-specific information, collectively boosting the model’s discriminative power. The method demonstrates a 2-3% performance improvement over state-of-the-art models, with significant advancements in glioblastoma diagnosis and treatment. An ablation study further confirms that each component positively contributes to the classification task.

关键词： Brain tumor Radiogenomic Classification Deep Learning Image Classification

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Multimodal Depression Detection Using Audio Visual Cues 1

Multimodal Depression Detection Using Audio Visual Cues

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1st International Conference on computer Science and Emerging Technologies, CSET 2023

作者： Tiwary, Gyanendra Chauhan, Shivani Goyal, Krishan Kumar Computer Science and Engineering Bhagwant University Ajmer India Raja Balwant Singh Management Technical Campus Computer Application Department Agra India

ISBN: (纸本)9798350341737

Millions of people all around the world suffer from depression, which is a common mental health issue. Effective intervention and therapy depend on early and precise identification of depression. Multimodal techniques that incorporate audio and video data have recently demonstrated promising outcomes in the detection of depression. In this article, authors suggest a convolutional neural network (CNN) model for multimodal depression identification based on audio and video. The proposed model makes use of audio and visual elements to gather comprehensive and complementary data about depression. Pitch, intensity, and spectral information, among others, are derived from voice recordings for the audio modality. The video modality uses face landmarks, optical flow, and pose estimation techniques to combine facial expressions, head movements, and body gestures. The parallel branches of the CNN architecture process the audio and visual inputs individually. To learn discriminative audio representations, the audio branch applies three convolutional layers, followed by pooling and dense layers. To extract video-specific information, the video branch uses five convolutional layers with different filter widths and depths, followed by fully connected and pooling layers. Further a late fusion technique has been adopted for multimodal fusion, concatenating learned features from the two modalities and passing them through additional thick layers for depression prediction. Authors also used regularization strategies, including dropout and batch normalization, during training to alleviate the overfitting problem. Authors evaluated the proposed multimodal CNN model on a DAIC-WOZ dataset consisting of audio and video recordings of individuals with and without depression. The proposed model achieved 77% accuracy and hence demonstrate that the proposed multimodal CNN model achieves superior performance compared to unimodal approaches. © 2023 IEEE.

关键词： Neural network models

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Remotely Controlled Electronic Noticeboard Using NodeMCU 14

Remotely Controlled Electronic Noticeboard Using NodeMCU

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kumar, Ayush Singh, Gurpreet Rawat, Vandana Negi, Deepti Joshi, Aditya Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350335095

In today's digital age, traditional notice boards are being replaced by more dynamic and interactive solutions. This research paper presents the design and implementation of a remotely controlled notice board using the NodeMCU ESP8266 microcontroller and an 8x8 matrix display. The system allows users to remotely update and display messages or notifications in real-time, enhancing communication efficiency and flexibility. By leveraging the capabilities of the NodeMCU ESP8266 and the visual output of the 8x8 matrix display, this solution offers an innovative approach to notice board management. The paper discusses the hardware and software components, the communication protocol, and explores the potential applications of this technology in various settings such as educational institutions, offices, and public spaces. The findings demonstrate the effectiveness and reliability of the proposed solution and emphasize its significance in improving communication processes. This research also highlights potential future enhancements and expansions, paving the way for further advancements in the field of remotely controlled notice boards. © 2023 IEEE.

关键词： Remote control

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Integration of Renewable Energy with Common Clud for Centre Level Control for Domestic Uses

Integration of Renewable Energy with Common Clud for Centre ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Verma, Archana Mehta, Deepak Jangid, Satish Kumar Noida Institute of Engineering & Technology Computer Application Uttar Pradesh Greater Noida India Department of Computer Science Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350357769

The growing problem of water scarcity is made worse by the unchecked use of fossil fuels for irrigation water-table pumping, which contributes to both environmental degradation and global warming. An burgeoning population and rising food demand mean that sustainable agriculture has to make effective use of its energy and water resources. In reaction, the area of 'smart agriculture' (SA) becomes more important, using information and communication technology (ICT) to maximize resource use and improve agricultural yields. In order to help small and medium farms overcome their financial barriers to adopting commercial solutions, this study proposes a complete SA solution that prioritizes cost-effectiveness. Tested in a real-world Smart Farm testbed, the deployed SA system demonstrated a significant decrease in water use (up to 71.8% compared to typical irrigation systems). The solution's open-source nature makes it easier for academics to adopt and modify it, particularly when it comes to creating specialized Cloud-based platforms that solve issues with water table consumption, especially in dry and sub-Saharan nations. © 2023 IEEE.

关键词： fuzzy logic control and information communication technology (ICT) Internet of Things (IoT) Smart agriculture (SA) wireless sensors networks (WSN)

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