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Triboost and beyond: Advanced Machine Learning Approaches for Diabetes Risk Prediction 3

TriBoost and Beyond: Advanced Machine Learning Approaches fo...

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3rd International Conference on Electronics and Renewable systems, ICEARs 2025

作者： Katlariwala, soham biren Gandhi, Vaibhav C. Patel, Nirav Parmar, Devendra Desai, Ayushi Indian Institute of Technology Madras B.S. Data Science and Applications Chennai India University Department of Computer Engineering Mbit Anand India Parul University Parul Institute of Technology Vadodara India

ISBN: (纸本)9798331509675

Diabetes is a tremendous world health issue and, in its management, and complications prevention, proper screening is vital. This paper examines the improvement of accurate diabetes risk prediction by some advanced machine learning methods. The study compares various models such as;Random Forest, Adaboost, support Vector Machine, Catboost, Decision Tree, Ls-sVM, Logistic Regression, Naïve bayes, k-Nearest Neighbors, LightGbM, XGboost, and neural networks. Most emphasis is put on Triboost that is a novel ensemble model which integrates XGboost, Catboost, and LightGbM. Lastly, the effectiveness of a model is determined using measures such as ROC AUC and accuracy. Evaluation of the results has shown that the proposed Triboost had the highest accuracy at 94% and ROC AUC of 91% thereby outcompeting the use of individual classifiers as well as other ensemble methods. The results of the study confirm adequacy and possibilities of ensemble learning to enhance predictive precision and stability and their applicability for early detection of diabetes in clinics. Further work shall seek to increase the variability of the dataset, integrate fresh clinical data, and increase computational effectiveness for practical use. © 2025 IEEE.

关键词： Adversarial machine learning

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smart Verification system for Media File Using blockchain 1

Smart Verification System for Media File Using Blockchain

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： siddique, s. shireen Fatima, N. sabiyath B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665497107

In this modern world where information is stored and shared in digital manner in the format of image, video, document privacy and authenticity of the data needs to be preserved which is not an easy task. It has become a major threat where data shared or stored can be tampered or manipulated with and it is very difficult to check for the same. The main principle behind this study is to provide a way to store data in secure manner with the ability to check its authenticity when the need arises. The proposed method integrates blockchain technology to the system which helps to add the media file to the system and store the 256-bit unique hash value generated through keccak-256 hashing algorithm into blockchain using Proof-of-Work protocol. The generated 256-bit hash is stored in blockchain is of immutable nature and it takes approximately 3.5-10-63 seconds to crack the 256-bit encryption. In cases where the need arises to check if the media file is tampered or manipulated it can be done by comparing hash of the media file against hash value that is stored in blockchain. If both hash values are exactly same then the media file is authentic and its integrity is preserved. © 2022 IEEE.

关键词： blockchain

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Digital File Rights Management system Using blockchain 4th

Digital File Rights Management System Using Blockchain

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4th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2022

作者： siddique, s. shireen Fatima, N. sabiyath Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India

Creating digital content and distributing them has become much easier and convenient nowadays, where the digital content creator can create the content and also distribute their work to others from anywhere with the help of internet. This also has the risk as the content is distributed using internet, the risk being copyright infringement and illegal distribution of the content. To avoid these types of risks the content creators are opting towards the help of digital rights management. The digital rights management team provides help and support to the content creators in cases where their work is illegally copied or distributed. To get the digital content copyrighted is also not an easy task as it consumes time and money. The proposed model overcomes these issues by integrating digital signature, structure similarity index measurement (ssIM) and blockchain technology in the digital rights management life cycle. The digital signature is used to uniquely identify the user and the related information can be traced back to the user using digital signature, and the copyright is provided using the ssIM method. The ssIM value can be in the range of -1 to 1 where 1 denotes that both the media files are identical and the system updates this information so that all the participant can view this information. If the ssIM value after comparing both the media files is -1 then the both images are unique and authentic, then the copyright is provided for the media file and this information is updated in the system as well as Ethereum blockchain. This makes the system more secure, rob.s. and transparent. © 2022 Elsevier b.V.. All rights reserved.

关键词： blockchain

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Deep Learning-b.s.d Diagnosis system for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI summit 2024

作者： Fatima, N. sabiyath singh, Ramendra swathi, Akuthota Choudhary, Amar Ahmed, Azhar sharma, Chhaya B.S. Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India Ghaziabad India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalli Rangareddy India Alliance University Department Electronics and Communication Engineering Bengaluru India Kcc Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350379716

The present research provides an analysis of the development and implementation of a deep learning-b.s.d diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76%, with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results b.s.d on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms b.s.d on clinical case studies. © 2024 IEEE.

关键词： Contrastive Learning

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Effect of Noise on Concurrence of Compact Photonic CNOT Gate Designed Using Universal Cloner 2nd

Effect of Noise on Concurrence of Compact Photonic CNOT Gate...

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2nd International Conference on Intelligent Computing Techniques for smart Energy systems, ICTsEs 2021

作者： sharma, Amit Kumar sharma, shishir Kumar sharma, Ritu ECE Department MNIT Jaipur India B.S. Computer Engineering Penn State University University Park PA United States

ISBN: (纸本)9789811902512

In this paper a two Qubit deterministic compact photonic CNOT gate is analyzed under noisy conditions. Concurrence of the designed compact photonic CNOT gate has been calculated with and without noise parameter. The behavior of this gate is universal. The feasibility of the scheme by observing variation in concurrence with different system parameters are assessed and reported. In strong (g/ k= 2.4 and ks/ k= 0.05 ) and weak (g/ k= 0.1 and ks/ k= 0.01 ) coupling condition the concurrence calculated is 0.01441 and 0.753, respectively. Under noisy environment the computed concurrence in strong (g/ k= 2.4 and ks/ k= 0.05 ) and weak (g/ k= 0.3 and ks/ k= 1 ) coupling condition is 0.1231 and 0.5265, respectively. © 2022, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： semiconductor quantum dots

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IoT b.s.d Optical sensor Network For Precision Agriculture

IoT Based Optical Sensor Network For Precision Agriculture

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Durai, R. s. Vishnu Vijayakumar, R. Lakshmisridevi, s. bhanu, shaik Thasleem Arunkumar, U. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamilnadu Coimbatore641022 India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600048 India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641021 India

ISBN: (纸本)9798350383348

Precision agriculture is a cutting-edge farming strategy that maximizes harvests by using cutting-edge technology and data-driven decision-making. Optical sensors and other Internet of Things (IoT) devices have great promise to revolutionize farming operations in this setting. sensor networks and Machine Learning (ML) b.s.d tracking devices are in great demand because of the precise data extraction and analysis they give. This research was undertaken with the goal of reducing agricultural hazards and promoting smart farming practices. Diseases caused by insects and other diseases may reduce crop yields if not addressed quickly. Thus, in this study, we provide a unique artificial swarm fish optimized naive bayes (AsFONb) method for keeping an eye on the health of the soil and preventing diseases from manifesting in cotton plants' leaves. In this research, numerous important indicators of crop growth and health were monitored using Internet of Things (IoT) devices equipped with optical sensors. The environmental factors like as temperature, humidity, light intensity, and chlorophyll content are recorded by these sensors. The proposed method involves sending the collected data to a central server for processing and analysis via wireless transmission. Once the disease has been detected, the information will be sent to the farmers via Android app. The Android app can show the chemical concentration in a container with soil factors like humidity, temperature, and wetness. Using an Android app, you may control the relay and hence the power supply and chemical sprinkler system. The experimental findings demonstrate that the proposed solution outperforms the status quo in disease identification. © 2024 IEEE.

关键词： Agriculture IoT Optimization Approach

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simulator for swarm Feature selection Algorithms for Classification Problem

Simulator for Swarm Feature Selection Algorithms for Classif...

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Interdisciplinary Approaches in Technology and Management for social Innovation (IATMsI), IEEE Conference on

作者： Mayuri Diwakar Kulkarni Makarand shahade shailesh Deore Nilesh Khandekar Ishwari bhavsar Jayesh Garud Kunal Ahirrao Dept. of Computer Engineering SVKM’s Institute of Technology Dhule India Dept. of Computer Engineering SSVPS’s B.S. Deore CoE Dhule India

swarm Intelligence (sI) algorithms address difficult optimization and decision-making issues using cues from collective behavior. These behaviors are inspired by social creatures like ants, bees, and birds. Using these creatures' behaviors algorithms have demonstrated amazing promise in several fields, such as distributed computing, robotics, and optimization. Assessing sI algorithms' performance under various conditions and scenarios is still a significant difficulty, though. We introduce a new swarm Intelligence Algorithm Performance Analysis simulator (sIAPAs) to tackle this issue. This proposed simulator introduces a novel approach to automating the process of selection for swarm Intelligence (sI) algorithms on datasets. The system empowers users to upload datasets into a specialized simulator, which then orchestrates a comparative analysis using a suite of sI algorithms. The simulator evaluates and selects the most suitable algorithm b.s.d on the dataset's characteristics and problem domain. This provides the optimal selected features by the respective algorithms b.s.d on measured parameters.

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A Rob.s. Parkinson's Disease Recognition from MRI brain Images by Densely Multi-Dilated Densely Connected ResNet Model

A Robust Parkinson's Disease Recognition from MRI Brain Imag...

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Integrated Circuits, Communication, and Computing systems (ICIC3s), International Conference on

作者： M. Arogia Victor Paul sharmila shankar Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India

ISBN: (数字)9798350364088

ISBN: (纸本)9798350364095

Parkinson disease (PD) is a neurological ailment that can be treated that causes excruciating discomfort in the brain as well as problems with balance and dexterity. Physicians frequently employ experimental indicators, such as motorized and non-motor symptoms b.s.d on brain magnetic resonance imaging (MRI) pictures, to diagnose Parkinson's disease (PD) in a medical stage. Nevertheless, there are disparities in inter-variability because these medical evaluations heavily rely on skilled physicians. Thus, this work proposes a sophisticated deep learning (DL) method for precise brain MRI-b.s.d PD analysis. To improve the data eminence, Extended Unsharp Mask Filtering (Ex_UMF) is first used to the brain images. The suggested Multi-dilated Densely linked ResNet model (MD_DenseRNet) is then fed the preprocessed pictures to determine if the image is PD or normal. The Python platform processes and examines the generated method, and the simulation progresses using a publicly available PPMI datab.s.. A number of evaluation metrics, including computing time (CT), sensitivity, specificity, and precision, are looked at and linked to conventional research. The created MD_DenseRNet model achieved 98.87% accuracy, 97% precision, 98.1% recall, 96.9% specificity, and 0.15 ms CT in the experimental part.

关键词： Accuracy sensitivity Parkinson's disease Computed tomography Magnetic resonance imaging Computational modeling Medical services

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Prediction and Classification of Handwritten Digits using DRNN-WOA model with Radiomics Features

Prediction and Classification of Handwritten Digits using DR...

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International Conference on Power, Energy, Control and Transmission systems (ICPECTs)

作者： s. Nandhini Devi N. sabiyath Fatima Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai Tamil Nadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Words in handwriting are notoriously difficult to identify and distinguish because of their complex and irregular shapes. Word retrieval is a challenging and time-consuming process in low-resource scripts. To get the most out of deep learning and neural network models, it is necessary to employ large amount trained datasets and present randomness into the existing structure. Existing HDR methods rely primarily on hand-coded key points data. The wide variety of writing styles, as well as size fluctuations in the input sample, make accurate recognition and categorization of numerals to be a difficult task. To overcome these constraints, this research work introduces a Deep-Recurrent (DRNN) Neural Network model and provides an effective and efficient HDR system. before considering pre-processing to enhance prediction and classification accuracy, HDR is applied to the MNIsT and DIDA datasets. both the Convolution algorithms and the Gaussian filters are used to refine the digits. The DRNN model receives the extracted features called Radiomics as input. since the Whale Optimization Algorithm (WOA) is used to fine-tune the DRNN's parameters, the suggested type is called DRNN-WOA. Accuracy, recall, F1- score, specificity, AUC, precision, and false positive rate are some metrics used to compare the proposed DRNN-WOA model.

关键词： Accuracy Neural networks Training data Predictive models Prediction algorithms Feature extraction Data models Classification algorithms Testing Radiomics

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Risk Prediction in Financial Transactions Using IoT big Data Analytics

Risk Prediction in Financial Transactions Using IoT Big Data...

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Electronics and sustainable Communication systems (ICEsC), 2020 International Conference on

作者： P. Chidambaranathan V. MuthuPriya Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The Internet of Things is a network of appliances and other items that can communicate and distribute data; as a result, Internet of Things are equipped with actuators, sensors, microelectronics, software, and connections. IoT devices, to put simply, share data over wired or wireless networks. Inventions of IoT have endless possibilities that can make a huge difference in the world. IoT is having the most effect on b.s.ness since has altered not just how various corporate processes are conducted, but also how the economy is managed. big data and IoT help optimize operations, reducing costs, enhancing productivity and improving lives. Globally, financial institutions and banks are coming up with fresh, creative approaches to quickly assimilate big data analytics for every step of the process in with the intention of maximizing efficiency. Numerous previous studies mostly concentrate on risk assessment along with big data analytics together in the banking industry; nevertheless, might not particularly address the difficulties posed by cross-border transactions. The purpose of the proposed study is to close this gap by focusing on the particulars and bringing up the approach of big data analytics for risk assessment in cross-border transactions.

关键词： Industries Technological innovation Reviews Wireless networks banking big Data software sensors Internet of Things Risk management

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