the proceedings contain 128 papers. the special focus in this conference is on datascience, machine learning and applications. the topics include: Digitization of Monuments – An Impact on the Tourist Experience with...
ISBN:
(纸本)9789819780303
the proceedings contain 128 papers. the special focus in this conference is on datascience, machine learning and applications. the topics include: Digitization of Monuments – An Impact on the Tourist Experience with Special Reference to Hampi;resume Parser Using machinelearning;IOT Based Smart Hydroponics System;comparative Study of machinelearning and Deep learning Techniques for Cancer Disease Detection;High thruput Modulation Approaches Used in Next Generation WiF’s Under Multi-impairments Environments with MATLAB Codes;skin Disease Detection;root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors;deep learning Model Development for an Automatic Healthcare Edge Computing Application;Empathetic Conversations in Mental Health: Fine-Tuning LLMs for Supportive AI Interactions;exploring Block Chain Technology withapplications, and Future Prospects;a Comprehensive Review of Soft Computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead;Performance Analysis of machinelearning Algorithms on Imbalanced datasets Using SMOTE Technique;An AI Based Nutrient Tracking and Analysis System;power Saving Mechanism for Street Lights System Using IoT;Automatic Login System Using ATTINY85 IC;forecasting Stock Prices: A Comparative Analysis of machinelearning, Deep learning, and Statistical Approaches;smart Vision Bot;robots in Logistics: Apprehension of Current Status and Future Trends in Indian Warehouses;smart Healthcare: Enhancing Patient Well-Being with IoT;Detection of B-ALL Using CNN Model and Deep learning;a Comprehensive Analysis for Advancements and Challenges in Deep learning Models for Image Processing;a Comprehensive Survey on Enhancing Patient Care through Deep learning and IoT-Enabled Healthcare Innovations;attention-Based Image Caption Generation.
the proceedings contain 128 papers. the special focus in this conference is on datascience, machine learning and applications. the topics include: Digitization of Monuments – An Impact on the Tourist Experience with...
ISBN:
(纸本)9789819780426
the proceedings contain 128 papers. the special focus in this conference is on datascience, machine learning and applications. the topics include: Digitization of Monuments – An Impact on the Tourist Experience with Special Reference to Hampi;resume Parser Using machinelearning;IOT Based Smart Hydroponics System;comparative Study of machinelearning and Deep learning Techniques for Cancer Disease Detection;High thruput Modulation Approaches Used in Next Generation WiF’s Under Multi-impairments Environments with MATLAB Codes;skin Disease Detection;root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors;deep learning Model Development for an Automatic Healthcare Edge Computing Application;Empathetic Conversations in Mental Health: Fine-Tuning LLMs for Supportive AI Interactions;exploring Block Chain Technology withapplications, and Future Prospects;a Comprehensive Review of Soft Computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead;Performance Analysis of machinelearning Algorithms on Imbalanced datasets Using SMOTE Technique;An AI Based Nutrient Tracking and Analysis System;power Saving Mechanism for Street Lights System Using IoT;Automatic Login System Using ATTINY85 IC;forecasting Stock Prices: A Comparative Analysis of machinelearning, Deep learning, and Statistical Approaches;smart Vision Bot;robots in Logistics: Apprehension of Current Status and Future Trends in Indian Warehouses;smart Healthcare: Enhancing Patient Well-Being with IoT;Detection of B-ALL Using CNN Model and Deep learning;a Comprehensive Analysis for Advancements and Challenges in Deep learning Models for Image Processing;a Comprehensive Survey on Enhancing Patient Care through Deep learning and IoT-Enabled Healthcare Innovations;attention-Based Image Caption Generation.
Cancer is one of the most dreadful illnesses that plague mankind. the illness has a high mortality rate. there are numerous kinds of this illness. It is challenging to identify these diseases in their early stages. Re...
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Cancer is one of the most dreadful illnesses that plague mankind. the illness has a high mortality rate. there are numerous kinds of this illness. It is challenging to identify these diseases in their early stages. Recent studies have shown the significance of machinelearning and Deep learning techniques in disease diagnosis. the most promising methods are presented in this study employing several machinelearning and deep learning algorithms and their comparative study to determine the specific type of cancer sickness that a patient has. Additionally, it offers the most effective models for each disease type currently in use analyzed using Accuracy and AUC ROC metrics.
Liver diseases are a global health concern, and early diagnosis is crucial for effective treatment. While traditional liver-function laboratory tests provide valuable information, they may not say much about any emerg...
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Liver diseases are a global health concern, and early diagnosis is crucial for effective treatment. While traditional liver-function laboratory tests provide valuable information, they may not say much about any emerging or underlying illnesses. In this study, we explore the efficacy of machinelearning algorithms in predicting the risk of liver disease using the Indian Liver Patient dataset. this could help patients concerned opt for timely and effective treatment.
Continual learning in machinelearning systems requires models to adapt and evolve based on new data and experiences. However, this dynamic nature also introduces a vulnerability to data poisoning attacks, wheremalici...
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Continual learning in machinelearning systems requires models to adapt and evolve based on new data and experiences. However, this dynamic nature also introduces a vulnerability to data poisoning attacks, wheremaliciously crafted input can lead to misleading model updates. In this research, we propose a novel approach utilizing theEdDSAencryption system to safeguard the integrity of data streams in continual learning scenarios. By leveraging EdDSA, we establish a robust defense against data poisoning attempts, maintaining the model's trustworthiness and performance over time. through extensive experimentation on diverse datasets and continual learning scenarios, we demonstrate the efficacy of our proposed approach. the results indicate a significant reduction in susceptibility to data poisoning attacks, even in the presence of sophisticated adversaries.
In the realm of data clustering, the Deep Embedded Clustering (DEC) algorithm has earned a reputation for efficiently grouping data points. Its limitation is that it only deals with numerical data. In real-world scena...
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In the realm of data clustering, the Deep Embedded Clustering (DEC) algorithm has earned a reputation for efficiently grouping data points. Its limitation is that it only deals with numerical data. In real-world scenarios, data is often a mixture of numerical and categorical attributes, posing a more intricate challenge. this project presents an enhanced version of the DEC framework, tailored to address the complexities of mixed data clustering. It incorporates embedded layers and soft-target updates to ensure seamless handling of both numerical and categorical attributes, maintaining convergence stability throughout the process. It also uses the concept of a "deep reinforcement learning" In the evaluation process, the proposed approach performed better than standard metrics.
the detection of B-cell acute lymphoblastic leukemia (B-ALL) plays a crucial role in ensuring timely and effective treatment for patients. Recent advancements in Convolutional Neural Networks (CNNs) and deep learning ...
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the detection of B-cell acute lymphoblastic leukemia (B-ALL) plays a crucial role in ensuring timely and effective treatment for patients. Recent advancements in Convolutional Neural Networks (CNNs) and deep learning techniques have shown promise in automating the detection and diagnosis of B-ALL. this project provides a concise overview of the literature surrounding the use of CNN models and deep learning approaches for B-ALL detection. the studies reviewed demonstrate the effectiveness of these techniques in achieving high accuracy and improving the speed of diagnosis. the application of CNN models and deep learning in B-ALL detection has the potential to enhance early identification and improve patient outcomes.
Malware remains a critical challenge within the domain of working frameworks and program, with Android frameworks being no special case. In spite of past endeavors utilizing Signature-based methods for malware detecti...
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Malware remains a critical challenge within the domain of working frameworks and program, with Android frameworks being no special case. In spite of past endeavors utilizing Signature-based methods for malware detection, their restrictions in distinguishing obscure malwares are apparent. the scene is characterized by a large number of location and investigation strategies, however viably tending to the precise recognizable proof of novel malware remains a basic concern. Our technique involves leveraging a comprehensive dataset of consents related with pernicious applications. Furthermore, our approach amplifies past authorizations and dives into the semantic layer by subjecting the application's comments to thorough investigation. through this comprehensive system, we point to supply an inventive and compelling arrangement that increases the exactness of malware location, especially centering on tending to the challenges postured by rising and already obscure malware strains.
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