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Comparative Evaluation of Deep Learning Models in Alzheimer’s Disease Diagnosis

Procedia computer science 2025年 258卷 2352-2361页

作者： Leena Arya Yogesh Kumar Sharma Smitha Nayak Janaki Sivakumar Assistant Professor Computing Department Muscat College Oman Associate Professor Programme leader Computer security and forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer’s Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients’ ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer’s Sickness from medical imaging data. Each model’s arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer’s Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction.

关键词： Artificial Intelligence (AI) Cognitive Impairment Data Augmentation Diagnosis Accuracy Medical Imaging

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models

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Procedia computer science 2024年 235卷 506-519页

作者： Ravikumar S Arockia Raj Y R. Babu Vijay K R. Ramani Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062 Assistant Professor Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Assistant Professor Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India 603203 Assistant Professor Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamilnadu India 602117 Associate Professor Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamilnadu India 626140

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications.

关键词： Hyperparameter optimization Quantum computing Natural language processing (NLP) Quantum Neural Networks (QNN) Real-time processing Adaptive learning

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Selective Text Encryption Using RSA for E-governance Applications for Pdf Document

Selective Text Encryption Using RSA for E-governance Applic...

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International Conference on Network Security and Blockchain Technology, ICNSBT 2023

作者： Adhikari, Subhajit Karforma, Sunil Assistant Professor BSH Department Institute of Engineering and Management University of Engineering and Management Kolkata India Research Scholar Department of Computer Science University of Burdwan Burdwan India Faculty Department of Computer Science The University of Burdwan Burdwan India

ISBN: (纸本)9789819944323

When data travels through an insecure medium, security must be enforced. The confidentiality of the exchanged data must be guaranteed with the help of encryption techniques. Selective encryption is a very powerful tool for encrypting textual data in a resource constrained environment. In the context of e-governance, textual data is very important and must be protected from any kind of security threat using selective text encryption. In this paper, a fast and efficient selective encryption technique based on an RSA asymmetric key encoding approach is presented. After fetching the whole textual information in the encryption phase, the user will search for a particular word or phrase using regular expression. After that, the selected data will be encrypted using the RSA-1024-bit algorithm and written to a new encoded document with the remaining text data. In the phase of decoding the data, only the encoded text of the document is considered. The experimental result confirms that the encrypting method is secure in terms of statistical security tests and fast in terms of computation time. Our encoding method can be implemented to encrypt any multimedia data like images, audio, and videos. This proposed technique can be used in IOT devices where resources are limited. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Artificial intelligence in the detection and classification of dental caries

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Journal of Prosthetic Dentistry 2025年第5期133卷 1326-1332页

作者： Ahmed, Walaa Magdy Azhari, Amr Ahmed Fawaz, Khaled Ahmed Ahmed, Hani M. Alsadah, Zainab M. Majumdar, Aritra Carvalho, Ricardo Marins Assistant Professor Department of Restorative Dentistry Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Associate Professor Department of Orthopedic Surgery Faculty of Medicine Cairo University Cairo Egypt Assistant Professor Department of Civil Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Consultant in Restorative Dentistry Dental Department East Jeddah General Hospital Ministry of Health Jeddah Saudi Arabia Graduate student Department of Computer Science Computer Science and Applications Virginia Polytechnic Institute and State University Blacksburg VA United States Professor Department of Oral Biological and Medical Sciences Faculty of Dentistry University of British Columbia Vancouver BC Canada

Statement of problem: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. Purpose: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. Material and methods: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. Results: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. Conclusions: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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Plant Disease Detection Using Convolution Neural Network 2

Plant Disease Detection Using Convolution Neural Network

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Rani, K. Pushpa Lavanya, G. Madhuri, K. Sai Vamsinath, J. Sabitha, Ch. Rao, K. Srinivas Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad 500043 Assistant Professor Department of CSE VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302

ISBN: (纸本)9798350370188

Today the persistence of plant and plant diseases is a substantial problem for cultivators, disease diagnosis is important in the farm. To preserve the production of crops, adequate consideration is a must. To isolate and detect infections in conventional situation, a lot of workforce, time, and frequent surveillance of the farm is involved. If no proper safety measures are not taken in protecting the crops, then it may damage the production often diseases make the plants weak by interrupting the photosynthesis. This paper shows in-depth study methods used in disease diagnosing. © 2024 IEEE.

关键词： Convolutional neural networks

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Precaution Chatbot for Epilepsy Patients using Natural Language Processing and Deep Learning Sequential Model

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Procedia computer science 2024年 233卷 537-546页

作者： E Kasthuri S. Subbulakshmi Rajasree Sreedharan Research Scholar Department of Computer Science and Engineering Assistant Professor Department of Computer Science and Applications Amrita School of Computing Amritapuri India Associate Professor Division of Nephrology Department of Pediatrics Medical College of Wisconsin Milwaukee Wisconsin USA

Epilepsy is a brain disorder that can affect people of all ages which is one of the most common neurological disorders affecting around 50 million individuals. Healthcare Chatbots schedule appointments, respond to patient inquiries, and offer relevant information, freeing medical staff to handle life crises. This paper implements a chatbot for epilepsy patients to increase awareness about epilepsy. Patients may easily access this AI-based healthcare chatbot whenever they have queries regarding precautions to be taken by epilepsy patients. Chatbot uses a rich set of epilepsy-related datasets collected from trusted websites which are preprocessed with NLP techniques to prepare the data for model ***, LSTM, and RNN models are created and evaluated to produce answers to user queries related to possible precautions to avoid seizures, safety measures to be taken by patients with seizure history, etc. Evaluation of models shows that the RNN model generates more accurate answers with 94% accuracy. Finally, the epilepsy question answering Chatbot for precaution is concluded with the RNN model.

关键词： Epilepsy Seizure Chatbot Natutal Language Processing LSTM Long short-term memory Recurrent neural network Deep neural network

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Real-time Face mask Detection Using Deep Learning and MobileNet V2

Real-time Face mask Detection Using Deep Learning and Mobile...

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International Conference on VLSI and Microwave and Wireless Technologies, ICVMWT 2021

作者： Verma, Akshay Srivastava, Manish Kumar Master of Technology Department of Computer Science and Engineering MMM University of Technology Gorakhpur India Assistant Professor Department of Computer Science and Engineering MMM University of Technology Gorakhpur India

ISBN: (纸本)9789811903113

COVID-19 has affected the whole world severely. Lockdowns and quarantines are imposed all over the world to prevent its spread. Hand sanitizers and face masks were made compulsory for individuals to apply for safety of their own and their society. This project will check the presence or the absence of masks on the face of a person. There could be more than a single person in the input provided, and the input could vary from images to GIFs to Livestreams. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Insightful Clinical Assistance for Anemia Prediction with Data Analysis and Explainable AI

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Procedia computer science 2024年 233卷 45-55页

Anemia among young people has positive correlation with susceptibility and impede cognitive development. As per WHO 2019 statistics global prevalence rate of anemia is 33 percent. This work aims to predict anemia with inferences to identify the root cause of anemia. Prediction is done with machine learning algorithms with proper pre-processing, data normalization, and class imbalance approaches. Explainable AI, a transparent model, is used to enhance the prediction which enables to draw inferences. LGM Boost learning model prediction has a 91% accuracy and explainable AI framework predictions of most contributing attribute is accurate when compared to other models. This approach helps healthcare professionals make informed decisions on the most optimal treatment for anemia.

关键词： Explainable Artificial Intelligence Machine learning Anemia Data preprocessing

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Assistive analytics for coronavirus-trends, patterns and predictions

Assistive analytics for coronavirus-trends, patterns and pre...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Julian, Anitha Selvi, S. Kumaravel, Shyam Sundhar Veerasekaran, Sabarijeyanth Sajja, Sumanth Sai Department of Computer Science and Engineering Saveetha Engineering College Chennai India Department of Computer Science and Engineering R.M.K Engineering College Data Engineering Associate Accenture Chennai India Infosys Chennai India EGG S&S SW TCS Chennai India

ISBN: (纸本)9781032669663

The recently identified coronavirus is the source of the contagious sickness known as Covid-19. Most of the Covid-19 patients develop respiratory disorders that are recoverable since the infection is mild or moderate. But as the coronavirus has been thriving over the past three years uncontrollably, it has created new varied mutants, which has increased the fatality rate drastically and made it hard to be diagnosed on time. The proposed work suggests the analyzing of patient X-Ray as a more accurate step than the traditional swab test, in detecting the presence of the coronavirus through a mobile application. The infected person can get needed help on medication, treatment, hospitalization, etc. through the application. The application also marks the location of the Covid-19 infected persons using the geo-fencing visible to the other users in the same location that helps in avoiding contact with the infected. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Coronavirus

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An Efficient Dual Response Surface-Based Convex Robust Optimization of Multi-storied RC Buildings Subjected to Surface Blast Loading Characterized by Interval Uncertainty 13th

An Efficient Dual Response Surface-Based Convex Robust Optim...

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13th Structural engineering Convention, SEC 2023

作者： Das, Sujit Bhattacharjya, Soumya Research Scholar Department of Civil Engineering Indian Institute of Engineering Science and Technology West Bengal Shibpur Howrah711103 India Associate Professor Department of Civil Engineering Indian Institute of Engineering Science and Technology West Bengal Shibpur Howrah711103 India

ISBN: (纸本)9789819996247

This work deals with robust design optimization (RDO) of structures when information about the input parameters is not adequate to treat them probabilistically. This case often arises while designing a structure under surface blast loading. Neither the mean, standard deviation, nor the probability distribution function is available for surface blast parameters, like charge weight, stand-off distance, overpressure, duration of the pulse, shock front velocity, drag coefficient, etc. In such cases, the parameters can be treated as interval types, where only the ranges of variations of the uncertain parameters are sufficient to model them in a hyper-ellipsoidal convex domain. Subsequently, a convex programming (CP) approach can be used to optimize the system. This approach works fine for simple deterministic systems where loading is equivalent to static in nature. But, the surface blast is a dynamic impulse type load with significant stochastic characteristics. The time-history of blast loading varies significantly even with the same hazard parameter setup due to the inherent uncertainty associated with blast load modeling. A new dual response surface method (RSM)-based RDO procedure is developed in the present paper. The dual RSM captures the stochastic nature of load time-history which in-turn is used to develop a new formulation of RDO. The effectiveness of the proposed approach is explained using a numerical problem of an RC 20-storied building. The RDO is solved by sequential quadratic programming. The results of RDO are compared with the conventional IS: 4991- 1968 based approach to indicate the need for the present approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Uncertainty analysis

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