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Advanced Neural Network Models for Predictive Analytics and Healthcare Management in Neurodegenerative Diseases

Advanced Neural Network Models for Predictive Analytics and ...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Kumar, Ritesh Bhagwat, Renuka Kumar V, Akshay Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350306927

Our proposed method, HybridNet-NDM, leverages three critical algorithms - Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs), synergistically applied to predictive analytics and healthcare management for neurodegenerative diseases. CNNs are employed to extract biomarkers from medical imaging data, capturing intricate patterns critical for neurodegenerative disease identification. LSTMs model temporal aspects of patient data, aiding in disease progression prediction using longitudinal records. GCNs analyze brain connectivity patterns, providing insights into disease development based on brain structure and function. The hybrid neural network combines these algorithms, integrating their respective features to achieve superior predictive power. An attention mechanism further refines the feature fusion, enabling precise predictions. Our optimized model demonstrates enhanced performance across various metrics, showcasing its effectiveness in predictive analytics and healthcare management for neurodegenerative diseases. © 2023 IEEE.

关键词： Long short-term memory

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Healthcare Data Analysis using Competitive Ensemble Deep Learning Model

Healthcare Data Analysis using Competitive Ensemble Deep Lea...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Gunavathy, K. Pandeya, Megha Sekar, R. Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

By utilising novel techniques to boost classification performance, ML is utilised to obtain healthcare knowledge. These classifiers have the potential to boost model decision and hence boost patient care. Recent research has shown that, compared to mixed model classification, the accuracy of single-model classification is worse. This study delves into the value of DL methods for both organised and unorganised healthcare databases. The exponential growth of healthcare data has made it challenging to mine for meaningful trends. Clinical data volumes are manageable for DL, and the results are improved. To enhance classification efficiency for structured data, a fresh competitive ensemble deep learning model has been presented. When it comes to Twitter sentiment analysis, however, the suggested work emphasises a competitive DL model that can handle unstructured data. The report also covers the planned Capsule Network for text data along with the Gravitational Search Algorithm (GSA). In order to assess the efficacy of the proposed model, it is compared to a more conventional model. The algorithm improved upon previous methods of analysing user sentiment on Twitter. The proposed model outperformed industry standards in the vast majority of tests. Furthermore, for multi-label classification problems, the outcomes are superior in terms of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Coordinated Fog-to-Cloud System Managing

Coordinated Fog-to-Cloud System Managing

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

作者： Nair, Amrutha V. Nachappa, M.N. Singh, Vaishali Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Recent developments in cloud services information have sparked several improvements in networking, storage, and computing, among other areas of information technology. The Internet of information, the use of clouds, and the newly developed idea of fog computing have all emerged as a result of this transition. The processing, storing, and exchanging of data between edge devices, user clients, and the cloud are all included in fog computing. Although their quick development was first unanticipated, these technologies currently allow for the implementation of what are known as smart scenarios, such as smart homes, smart cities, and smart transportation. This article outlines the way toward a new IoT environment with highly dispersed and dynamic storage, computing, and networking resources. We also describe an architecture, an illustrated use case, and a comparative productivity study. A wide range of heterogeneity and commodity devices at the edge, developing fog-based resources, and traditional cloud services are all present in this ecosystem. © 2023 IEEE.

关键词： cloud computing decentralized operation disaster management edge devices fog computing Fog-to-Cloud system Internet of Things (IoT) technical challenges and cloud market opportunities urban planning

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Energy Consumption for Cognitive Radio Network Enabled Multi-Access Edge Computing 3

Energy Consumption for Cognitive Radio Network Enabled Multi...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Elnaim, Areeg Ali Babeker, Amin Barakat, Mohammed Gaid, Abdulguddoos S.A. Saeed, Mamoon M. Ahmed, Zainab. E. Saeed, Rashid A. College of Engineering and Communication Alneelain University Khartoum Sudan School of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Taiz University Dept. of Communication & Computer Engineering Taiz Yemen Faculty of Engineering Department of Communications and Electronics Engineering Yemen Gezira University Department of Electrical and Computer Engineering Khartoum Sudan College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia

ISBN: (纸本)9798350305333

This article discusses optimizing energy Consumption in CRN-enabled Multi-access Edge Computing (MEC) systems, presenting a novel approach to optimize the energy Consumption of a cognitive radio network-enabled MEC system using iterative optimization techniques. The proposed model aims to minimize the system's energy consumption while maintaining its performance and quality of service requirements. The iterative optimization algorithm is used to find the optimal allocation of resources, such as computing, communication, and power, among different nodes in the MEC system. As a result, the cognitive radio network enables efficient spectrum utilization by dynamically allocating available spectrum to different users based on their requirements. The performance evaluation approach is evaluated through simulations, and the results show that it can significantly improve the energy consumption of the MEC system while maintaining its performance and QoS requirements. This article contributes to developing sustainable and energy-efficient communication systems for future wireless networks. © 2023 IEEE.

关键词： Cognitive radio

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Bridging Diverse Business Domains through Optimized Digital Platforms in Industrial Syndicates

Bridging Diverse Business Domains through Optimized Digital ...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Kannagi, A. Chaudhary, Pavan Prabhu, T. Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

In an era characterized by rapid technological advancements and volatile business environments, synergy across numerous business sectors within industrial syndicates has emerged as a major issue and opportunity. There is no way to ignore this challenge and opportunity. The optimization of digital platforms to span a variety of different fields gives a thorough analysis of the potentially disruptive possibilities. The intention is to promote cooperation among participants in industrial syndicates, improve the distribution of available resources, and spark creative thinking. The method that has been presented makes use of cutting-edge technology to create a dynamic ecosystem where many businesses can interact with one another and share knowledge invisibly. This technology includes artificial intelligence, machine learning, blockchain, and the Internet of Things. In this study, a strategic road map is constructed using evolutionary algorithms, reinforcement learning, and clustering approaches to improve resource allocation, decision-making processes, and information sharing. The route map was constructed using these methods. These tactics aim to achieve the highest levels of productivity, adaptability, and teamwork, which will ultimately usher industrial syndicates into a new era of competitiveness. This essay examines many real-world case studies in depth and focuses on how these benefits might be realized to show the practical advantages of adopting an optimal digital platform paradigm. These case studies demonstrate how distinct organizations, which were previously divided due to antiquated practices, may make advantage of the potential of multidisciplinary collaboration to confront challenging global issues. The study also makes suggestions for a culture change inside industrial syndicates, one that promotes collaboration, fosters creativity, and acknowledges the potential for new technology to upend society. If industrial syndicates promote an innovation culture t

关键词： Competition

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A Unified Framework for Classification with Camera-Guided Labeling Approach

A Unified Framework for Classification with Camera-Guided La...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Sreenivasappa, B.V. Jayak, Ansuman Pandeya, Megha Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350306927

Several factors, including the widespread availability of high-quality image sensors and the exponential expansion of image data, have contributed to a significant breakthrough in computer vision in recent years. Picture classification is a key component of computer vision that has recently gained a lot of attention due to the vast variety of possible applications. These technologies have the potential to be employed in a variety of different disciplines, including surveillance, medical diagnostics, autonomous driving, and many more. For real-world deployment, a high level of classification accuracy is necessary, which is frequently obtained by laboriously tagging big picture sets. The amount of time necessary to complete this task may be difficult. In this paper, we present a unique and comprehensive framework for image categorization. The simplicity with which camera-guided labeling systems may be integrated into this framework is one of our key contributions. To create a robust and scalable framework for exact picture categorization, we use current breakthroughs in sensor, computer vision, and machine learning technologies. This enables our method to categorize photographs more precisely. Even though manually annotating images would require significantly more effort, this strategy may be beneficial. This demonstrates how the method may be used to eliminate the requirement for time-consuming human annotation These approaches make use of camera and sensor capabilities to give temporal data, sensor inputs, and contextual clues as extra information to help in labeling. They achieve this by using the capabilities of cameras and other sensors. They perform this activity in the hopes of improving the accuracy and efficacy of the product labeling procedure. © 2023 IEEE.

关键词： Cameras

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From Data Packets to Secure Networks with Advanced Analytical Frameworks for a Comprehensive Understanding of Threat Landscapes

From Data Packets to Secure Networks with Advanced Analytica...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Srilakshmi, H. Kumar V, Akshay Singh, Vaishali Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350306927

As the current digital world becomes increasingly linked, network security becomes more important. The purpose of this research was to create a unique technique that, by leveraging more advanced analytical frameworks, has the potential to improve computer network security. The solution we provide makes an attempt to provide consumers with a complete overview of the current security condition. To do this, machine learning, behavioral analysis, and real-time threat intelligence feeds are employed in unison. We are better able to mitigate risk now that we have this information. With the ability to adapt to continually changing hazards, this technique enables accurate threat identification in real time. Because of its scalability, the system is well-suited for usage with massive networks, and the adaptive response mechanisms it provides enable optimal resource allocation and the mitigation of the impacts of disruptive occurrences. This article contrasts the proposed approach with more established approaches, indicating the superiority of the latter. Our approach has a 98% detection rate and a 1% false positive rate, making it exceptional at identifying threats while lowering the number of unnecessary alerts. It presently has a detection rate of 98%. It may be used in a variety of network configurations because of its rapid reaction time, good scalability, and agility. Furthermore, it provides a full awareness of risk situations, which encompass both known and previously unknown hazards. The need to use advanced analytical frameworks to enhance network security in the face of shifting threat environments is highlighted in this study. © 2023 IEEE.

关键词： Network security

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Hybrid Approach for Accurate Vocal Anomaly Detection Using State-of-the-Art

Hybrid Approach for Accurate Vocal Anomaly Detection Using S...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Manikandan, M. Rengarajan, A. Naval, Preeti Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350306927

Accurate voice anomaly detection is achieved by the suggested method's combination of signal processing for feature extraction and improvement with deep learning. Preprocessing, feature extraction, and anomaly detection are the three pillars upon which this approach rests. The raw vocal signal is improved during preprocessing by denoising and filtering. From the preprocessed signal, the feature extraction stage derives Mel-Frequency Cepstral Coefficients (MFCCs) and their derivatives. Finally, an ensemble of three algorithms, including Support Vector Machines (SVM), Long Short-Term Memory (LSTM) Networks, and Convolutional Neural Networks (CNN), is used in the anomaly identification stage to conduct an in-depth evaluation of the detection of voice anomalies. The suggested hybrid method is meant to be more flexible and efficient in practical settings. © 2023 IEEE.

关键词： Long short-term memory

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Multifunctional Frequency Management With 5g Cognitive Networking

Multifunctional Frequency Management With 5g Cognitive Netwo...

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

作者： Neetha, S.S. Singh, Akhilendra Pratap Abrar Ahmed, Syed Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

Network traffic has significantly increased as a result of the acceptance of small-cell and Internet of Things (loT) gadgets that are growing at an exponential rate. The development of current infrastructure into 5G connectivity, which requires larger capacity, faster data rates, and very low latency, has been accelerated by this shift. Exploring spectrum assets and maximising the use of its bands are two significant areas requiring attention to satisfy these needs. However, the difficulty in coming up with a successful management plan is also increased by the restricted availability of band resource. With regard to the progress of 5G and current 5G-enabling innovations, this paper intends to provide a thorough overview of recent advances in spectrum sharing technology. A thorough analysis of SS surveys and pertinent research is offered, and spectrum exchange (SS) strategies are carefully categorised in the context of 5G networks. These studies and surveys are grouped by to how closely they adhere to one of the main SS methodologies, which considers network design, spectrum allocation behaviour, and spectrum access strategies. Additionally, a thorough examination of cognitive radio innovations in the larger picture of spectrum allocation linked to the rollout of 5G is included in this study. Discussions include the present problems and difficulties with the SS and CR applications as well as suggested solutions to speed up the development of 5G technology in order to provide a complete picture. © 2023 IEEE.

关键词： 5G technology Fixed wireless access Heterogeneous network Massive Machine Type communication Massive MIMO and Spectrum sharing Mobile broadband Ultra-low latency Ultrahigh reliability

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Advancing Early Detection of Cervical Cancer Through Multi-Model Intelligent Classifiers and Computational Technique 3

Advancing Early Detection of Cervical Cancer Through Multi-M...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Chaudhuri, Sumantra Suneetha, K. Acharjya, Kalyan Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

One of the most common illnesses in women and one with a high fatality rate is cervical cancer. Early detection using screening techniques helps to increase the lifespan of women and lower mortality rates. According to the World Health Organization (WHO), cervical cancer is now the fourth most common malignancy to affect women. According to surveys, low- and middle-income nations account for 90% of all cancer-related deaths since there is a dearth of knowledge about the disease and its preventative treatments there. When it comes to preserving the health of women, cervical cancer research is one of the most crucial fields of study. Early detection of cervical cancer is crucial because it can save the lives of many women whose lives are at risk. Foretelling cervical cancer growth prior to administering a screening test is the goal of the proposed study, which makes use of data mining and machine learning algorithms. Feature selection methods were used to isolate the characteristics useful for diagnosing cervical cancer. When applied to the dataset, an enhanced SVM model's accuracy was found to be 94.16 percent. This highlights the model's usefulness in screening for a potentially deadly disease like cervical cancer. This type of prediction algorithm for cervical cancer is gaining popularity as a result of its high accuracy. This ensures the model's high level of predictive performance and hints that it could revolutionise the way the needy are assisted if used in real time. © 2023 IEEE.

关键词： Diseases

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