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Medical Image Classification Using DL-based Feature Extraction in IoMT

Recent Patents on engineering 2024年第9期18卷 e220223213881-e220223213881页

作者： Thimmapuram, Madhuri Salvadi, Shoba Rani Pallati, Narsimhulu Aluvalu, Rajanikanth Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Computer Science & Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India

Aim: Recent advances in Artificial Intelligence (AI) and the addition of Deep Learning (DL) have made it possible to analyse both real-time and historical data from the Internet of Things (IoT). Recently, IoT technology has been implemented in healthcare schemes as IoMT to aid in medical diagnoses. Medical image classification is useful for predicting and identifying serious diseases at an early stage, which is crucial in the diagnostic process. Background: When it comes to managing, treating, and preventing illness, medical photographs are an essential element of a patient’s health record. However, it is a difficult issue in computer-based diagnostics to classify images using efficient characteristics. Objective: The patent study aimed to develop a deep learning-based classification model for feature extraction. Methods: Levy flight optimization is employed to pick the weight for the classification model optimally. At the end of the day, the optimal weight led to a better classification result and a higher degree of precision when analyzing medical photos for disease. Results: We tested the proposed results in MATLAB and compared them with conventional methods of classification. The suggested model’s best results include 97.71% accuracy on a brain dataset and 97.2% accuracy on an Alzheimer’s disease dataset. Conclusion: The proposed algorithm’s high rate of convergence proves that it can successfully balance the exploration and exploitation phases by avoiding capturing in local optimization and classifying thresholds rapidly. In light of the need for improved accuracy, precision, and computational speed in clinical picture classification, a novel approach based on soft sets has been presented. © 2024 Bentham Science Publishers.

关键词： Extraction

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An enhanced two-level data sanitisation and elliptic curve cryptography encryption model for securing electronic healthcare data in a hybrid cloud platform

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International Journal of Cloud Computing 2025年第1期14卷 86-114页

作者： Ambica, V. Mary, A. Viji Amutha Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Vignana Bharathi Institute of Technology India Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

A secure framework for storing EHR in the hybrid cloud platform is implemented. At first, the required medical data is gathered from the database of the hospitals and split into sensitive and insensitive parts. The sensitive part is first encrypted with the data sanitisation method. The keys obtained are optimally chosen by the modified uniform number-based red fox optimisation (MUN-RFO). Then, the insensitive part is encrypted by optimal key-based data encryption scheme (OKDES), in which the same MUN-RFO algorithm is utilised to choose the optimal keys. The sensitive and insensitive data is combined and stored in the Hybrid Cloud. From the result analysis, the cost function of the MUN-RFO-OKDES is lower by 0.022% of HBA-OKDES, 0.011% of DHOA-OKDES, 0.0025% of EFO-OKDES and 0.001% of RFO-OKDES at 5th iteration for dataset 2. Numerous simulations are carried out to prove the security provided by the given data storage model in a hybrid cloud. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Cost functions

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Application of chicken swarm optimization algorithm for multi objective scheduling problems in FMS

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Materials Today: Proceedings 2023年 72卷 1457-1461页

作者： Satish Kumar, B. Department of Mechanical Engineering Vignana Bharathi Institute of Technology Hyderabad 501301 India

Scheduling plays an important role in Flexible Manufacturing System (FMS).Various Evolutionary Algorithms are used by different researchers to solve the multi objective scheduling problems. However, solutions obtained by some of these algorithms suffer from various issues such as struck in local optima, not support deadline constraint, poor convergence speed etc. In this paper, a new optimization technique called Chicken Swarm Optimization (CSO) algorithm is implemented for optimum scheduling of Multi objective Scheduling problems considered from the literature. The Combined Objective Function (COF) is formulated by considering two objectives such as Minimization of machine idle time and Penalty cost minimization with equal weight-ages. MATLAB code has developed to determine the COF values and also for implementing CSO to obtain the optimum solution. It is observed that, the results obtained by CSO algorithm are very competitive when compared with other well-known algorithms like Genetic Algorithms (GA), Cuckoos Search Algorithm (CSA), Modified Cuckoos Search Algorithm (MCSA) & Jaya Algorithm (JA) © 2023

关键词： Genetic algorithms

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Improving the Efficiency of Electric Vehicle Power Usage through a Fuzzy-based Energy Management System 9

Improving the Efficiency of Electric Vehicle Power Usage thr...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Siddula, Sundeep Rao, G. Amarender Vignana Bharathi Institute of Technology Dept. of Electrical & Electronics Engineering Hyderabad India Vignana Bharathi Institute of Technology Dept. of Mechanical Engineering Hyderabad India

ISBN: (纸本)9798350394474

Because of the pollution they produce, traditional internal combustion engine cars running on gasoline and diesel are becoming less and less common. The need for cars that don't emit any pollution has grown throughout time. Electric cars are one kind of vehicle that emits less pollution (EV's). Studies on these cars' energy management, torque control, speed control, and other features are still under progress. Given the rising demand for energy, worries about environmental sustainability, and the requirement for effective resource usage, energy management is an essential part of modern life. Among the numerous benefits of the fuzzy-based energy management system are its dependability and ease of setup. There are two or more power sources in a hybrid electric vehicle. The Energy Management System (EMS), which is governed by fuzzy logic, provides a methodical command for controlling energy in electric vehicles. © 2024 IEEE.

关键词： Energy management systems

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A Review on Synergizing Convolutional and Swin Transformer Approaches for Superior Remote Sensing Detection 6

A Review on Synergizing Convolutional and Swin Transformer A...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Reddy, Allipurapu Meghanath Gangadhara, Sai Likhith Reddy, R. Ravinder Padmalatha, E. Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

Remote sensing object detection plays a crucial role in environmental monitoring, yet traditional methods often face difficulties with varying object scales, intricate backgrounds, and densely populated small objects. This research study elaborates on various methodologies carried out in the remote sensing detection area, with the support of the latest advances in traditional and deep learning techniques. We make a critical presentation of CNNs, Swin Transformers, and hybrid models, their adaptations, benefits, and limitations. Performance metrics along with adaptability across different datasets and environments are presented through comparative analysis of various models. The paper provides in-depth analysis into the ever-changing landscape of remote sensing technologies and the latest tangible applications regarding such technologies. © 2025 IEEE.

关键词： Convolutional neural networks

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Phish Check: Content-based Phishing Website Detection Web App 6

Phish Check: Content-based Phishing Website Detection Web Ap...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ghadiyaram, Anil Kumar Reddy, G. Srikanth Basith, Mohammed Abdul Reddy, Naini Rithvik Department of Computer Science and Engineering Vignana Bharathi Institute of Technology India

ISBN: (纸本)9798331522667

This research proposes an innovative method for categorizing phishing and legitimate websites based on content features extracted from their HTML structure. Leveraging data collection with the requests library and HTML parsing using the Beautiful Soup module, our study scrutinizes 26,584 websites sourced from "***" and "***." Employing seven diverse machine learning classifiers from the sci-kit-learn library and implementing k-fold cross-validation, this research study assesses the efficacy of different models in accurately distinguishing phishing websites. Evaluation metrics include confusion matrices, accuracy, precision, and recall scores. The proposed approach refrains from using URL-based attributes, focusing solely on the characteristics of HTML content. This study presents the results and comparisons of various classifiers, highlighting the effectiveness of our methodology. © 2025 IEEE.

关键词： Phishing

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Efficient Token Transfer Using ERC-20 Decentralized Exchange

Efficient Token Transfer Using ERC-20 Decentralized Exchange

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Venkata Viswasreddy, Mettupalle Chinnaiahgari Charan Reddy, Vootkuri Sai Rao, Gudeme Jaya Devi, N.Rama Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Hyderabad India

ISBN: (纸本)9798350375237

The implementation of blockchain-based token transfers has emerged as a transformative tool for innovation and enhanced financial inclusion. To implement Blockchain based token transfers, smart contracts play a pivotal role in automating complex processes, enhancing efficiency, and driving innovation for tokenized assets and decentralized applications (dApps) that can be run on Ethereum. With rising popularity of smart contracts in 2015, necessity for a standardized token approach within the Ethereum ecosystem is recognized. To address interoperability issues and challenges in exchanging tokens between different applications, the Ethereum Request for Commet-20 (ERC20) standard was introduced through the Ethereum Request for Comment (ERC) process. Since then, several numerous schemes have been proposed efforts to enhance token transfers and implementations while adhering to ERC-20 standards. Despite the substantial progress made in optimizing the functionality and efficiency of token transfers, achieving a truly user-friendly experience has remained an ongoing challenge. To address this challenge, several schemes have been proposed to refine the user interface and experience associated with ERC-20 token transfers. Our research work proposes a practical approach to address concerns by creating a simple user-friendly decentralized web application (Web3) using smart contracts that adhere to ERC-20 standards. By focusing on the development of a seamless user experience, the system developed aims to empower users to send tokens effortlessly by connecting to various wallets available in the market. © 2024 IEEE.

关键词： Ethereum

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Fuel oil from plastic waste

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Materials Today: Proceedings 2023年 76卷 166-169页

作者： Koorla, Prasad Babu Dasari, Narayana Mohammad, Noorullah Shareef Chemical Engineering Department Chaitanya Bharathi Institute of Technology Gandipet Hyderabad 500075 India

PLASTIC is a revolutionary invention which brought many changes in industrial & daily activities. But in more recent times it has resulted in a major problem due to the increasing usage of landfills which has led to increased pollution due to its high degradation time & and no established disposal method with minimal environmental impact. As the generation of plastic waste is increasing day by day, many engineering processes have been found to convert them to alternative fuels. Pyrolysis is the most useful and prominent method as it has a wide range of uses and helps in the reduction of the large amount of plastic waste. Pyrolysis is a process which converts the large hydrocarbon chains present to smaller hydrocarbon chains by heating the plastics at high temperature (350–400 °C), the products obtained are fuel oil and non-condensable fractions. The fuel oil can be used for heating purposes and non-condensable fractions to reduce air pollution. Polythene, Polypropylene are used as they are pure hydrocarbons and burnt cleanly. These fuels are used as industrial feedstock in many industries like petroleum refineries, alkene production etc. We are now considering plastic as a waste product but upon reprocessing and application of new technologies can yield many valuable products which can lead us to a sustainable and healthy environment in future. © 2022

关键词： Polypropylenes

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Surveying Flash Floods in Urban Indian Environment: A Review of Machine Learning Applications

Surveying Flash Floods in Urban Indian Environment: A Review...

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International Conference on Water Resources, Ocean, and Environmental engineering, ICWROEE 2024

作者： Blessy, Sardar Rechel Supraja, Balerao Rathi, Kushal Devi, Kamalini Vasanth, K. Srilatha, Pulipati Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Civil Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Electronics and Communications Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9789819776986

This survey paper conducts a thorough examination of the global challenge posed by urban flash floods, analyzing the intricate factors influencing the occurrence and severity of these events. Drawing on a diverse array of literature and case studies, the study delves into the complex dynamics of urban flash floods and their far-reaching impacts on human populations, infrastructure, and the natural environment. Highlighting the imperative for proactive flood risk management and resilient urban planning, the research puts forth actionable recommendations to bolster urban resilience in the face of mounting flood risks. In addition to evaluating machine learning algorithms like Iso clustering and ensemble classifiers for rapid flood mapping and precise flood susceptibility assessment, the study also explores the utilization of satellite imagery for enhanced flood monitoring and analysis. By integrating insights from both machine learning techniques and satellite imaging, the research aims to provide a comprehensive understanding of urban flash floods and offer practical strategies to mitigate their adverse effects. The study underscores the critical importance of implementing robust flood management measures, advanced early warning systems, and sustainable urban development practices to address the escalating threat of flash floods in urban areas and safeguard vulnerable communities, critical infrastructure, and ecological systems from the devastating consequences of these events. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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Enhanced Diabetic Retinopathy Grading with Convolutional Neural Networks and Graph Attention Networks 4

Enhanced Diabetic Retinopathy Grading with Convolutional Neu...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Pandugula, Vamshi Krishna Uyyala, Ravi Ramu, S China Choudhary, Abhishek Chaitanya Bharathi Institute of Technology Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529635

Diabetic Retinopathy (DR) remains one of the most common cause of vision impairment worldwide. The evolution of high definition Colour fundus imaging has greatly enhanced DR diagnosis but manual image analysis is still time-consuming and vulnerable to changes among different clinicians. However, Current deep-learning-based models, especially those based on Convolutional Neural Networks have demonstrated significant potential. findings in diabetic retinopathy classification often exclude the complex Interconnectivity of the deep features relevant for precision. assessment. Our current research is to fill the gap in the existing DR classification model with Graph Attention Networks with the raw convolutional neural network The proposed model is a framework. It contains two main parts: CNN for preliminary feature extraction from DR images and a GAT to capture dynamic interactions between the very deep features. The GAT layer improves the capacity of the model to emphasize Some relevant attributes and factors cause an increase in and correct DR grading. Outputs of CNN and GAT are amalgamated employing a dynamic weighting approach, thereby enabling the model to adaptively alter the effect of each one, relative to Characteristics of the dataset This integration enhances The classification performance based on the strengths of both. CNNs and GATs. © 2024 IEEE.

关键词： Convolutional neural networks

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