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A deep learning-based early alzheimer’s disease detection using magnetic resonance images

Multimedia Tools and Applications 2025年第16期84卷 16561-16582页

作者： Suchitra, S. Krishnasamy, lalitha Poovaraghan, R.J. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Information Technology Nandha Engineering College Tamil Nadu Erode India Department of Information Technology Jaya Engineering College Tamil Nadu Thiruninravur Chennai India

Alzheimer’s disease (Ad) is a degenerative, chronic condition of the brain for which there is now no effective treatment. However, there are medications that can slow its development. In order to stop and control the development of Ad, earlier diagnosis of the disease is quintessential. Our proposed method’s primary objective is to establish a comprehensive model for the prior detection of Alzheimer’s disease and the categorization of distinct Ad stages. This work employs a deep learning methodology, especially CNN. The proposed approach makes use of well-known models that have already been trained to classify medical images, like the EfficentNetB7 model, by applying the transfer learning principle. In order to achieve greater accuracy, convolutional neural networks (CNNs) are frequently scaled up as new resources become available at a fixed cost throughout the construction phase. A compound coefficient is used by the CNN architecture and scaling approach, which is the foundation of the pre-trained EfficientNetB7 model, to scale the dimensions equally. This proposed EfficientNetB7 model is quicker, easier, and more effective than other pre-trained models like VGG19 and InceptionV3. The proposed model includes simple structures that have memory requirements, provide manageable time, overfitting, and low computational complexity as well as training and inference speeds. The Alzheimer’s disease Neuroimaging Initiative (AdNI) dataset was employed for a comprehensive assessment of the method proposed, utilizing well-known performance metrics including sensitivity, specificity, and accuracy. The findings revealed that the improvised results achieved the accuracy metric when compared to existing methods. The EfficientNetB7 model has been enhanced, and this model achieves a sensitivity of 98.08%, specificity-98%, accuracy-98.2%, and F-score-98.95%, for multi-class Ad stage classifications. © The Author(s), under exclusive licence to Springer Science+Business Media, llC, par

关键词： Magnetic resonance

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detecting driver Fatigue with Python and OpenCV 10

Detecting Driver Fatigue with Python and OpenCV

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Pandi, C. Anish, T.P. Selvanayaki, S. Jagadeesan, P. Vedaraj, M. Sanjay, R. Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Computer Science and Engineering Avadi India R.M.K College of Engineering and Technology Computer Science and Engineering Tamil nadu India Saveetha Engineering College Artificial Intelligence and Data Science Tamil nadu Thandalam India R.M.D Engineering College Computer Science and Engineering Tamil nadu India

ISBN: (纸本)9798350353068

Utilizing eye aspect ratio analysis and facial landmarks, the sleep detection tool is intended to detect indicators of sleepiness or drowsiness in people. By leveraging the face recognition library, the program detects and extracts facial landmarks, with a particular focus on locating the positions of the eyes. The software analyzes the eye aspect ratio (EAR), which takes into account the relative distances between particular eye landmarks, to ascertain if an individual has fully or partially closed their *** video frame capture is performed from a chosen video source, typically a webcam, allowing real-time monitoring of the user. Each frame is processed to detect and analyze the eye regions, computing the EAR values for both the left and right eyes. If the EAR falls below a predefined threshold for a consecutive number of frames, it indicates that the person's eyes are closed for an extended duration, suggesting potential drowsiness or *** enhance user safety, an alarm sound is played when the program detects drowsiness, providing an immediate alert to the individual. The alarm sound serves as an auditory cue, helping the person regain alertness and avoid potential accidents or undesired consequences. Additionally, the program displays the computed EAR on the video feed, allowing for visual feedback and monitoring of the eye aspect ratio values for debugging purposes and fine-tuning the system's *** employing this sleep detection program, various applications can benefit from its capabilities. For instance, it can be utilized in the context of drowsy driving detection systems, alerting drivers when they exhibit signs of fatigue and helping prevent accidents due to impaired driving. Moreover, the program holds potential for use in other scenarios where monitoring individuals' alertness and detecting sleep-related patterns is essential, such as workplace safety or medical *** summary, the sleep detection program employs faci

关键词： Video signal processing

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Assessment of Compacted Graphite Iron Viability for Cost-Effective Product designing

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Journal of The Institution of Engineers (India): Series d 2024年 1-12页

作者： Patel, Mamta dave, Komal Upadhyay, Gautam Rehani, Bharati R. Gujarat Technological University Gujarat Ahmedabad India Mechanical Engineering Department L D College of Engineering Gujarat Ahmedabad India Metallurgy Gujarat Technological University Gujarat Ahmedabad India Consultant TCR Advanced Engineering Private Limited Gujarat Vadodara India

Compacted graphite iron (CGI), an untapped yet promising material, belongs to the cast iron family alongside grey cast iron (GCI) and ductile cast iron (dI). Its microstructure comprises individual worm-shaped compacted or vermicular graphite particles that are shorter, thicker, and stubby with rounded edges. These particles are densely interconnected and demonstrates intermediate mechanical and physical properties as compared to GCI and dI. This material has gained prominence over last decade especially for the applications where a combination of static and thermal loads act, with the aim of cost-effective product development by altering use of other cast irons and non-ferrous materials. despite its potential, CGI has certain limitations, including poor machinability and the need for specialized foundries for mass-scale quality production. The author has successfully produced proprietary CGI by exploring conventional foundry practices through separate research study. This research study is aimed to assess the commercial viability of proprietary CGI material and outline potential design optimization opportunities for cost-effective centrifugal pump volute designing, as an alternative to equivalent grades of GCI and dI materials. © The Institution of Engineers (India) 2024.

关键词： Product development

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Question Answering FAQ Bot Using Natural language Processing 11th

Question Answering FAQ Bot Using Natural Language Processing

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Vijay, K. Sowmia, K.R. Ravikumar, S. Arockia Raj, Y. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9789819717231

The BOT retorts with the precise results that help the user for the query and also requests them to rate the response to provide better results. The [frequently answered questions] BOT is a program developed through machine learning and natural language processing. It classifies the queries and results as key-value pairs and maps the response to the user query. It extricates itself by providing additional capabilities such as NlU processes and reinforcement learning. NlU helps the bot to make it convenient for an array of languages to meet the diverse expectations. Reinforcement learning retrieves user rating to provide better performance and search results. The project was developed due to a concern about fetching meticulous responses for specific service-related queries. The overall satisfaction rate of users in getting responses to the questions they post is devastating. The root cause for the proposed project has aimed at providing an environment for a people to find their answers at better accuracy and also respond to their satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2024.

关键词： Reinforcement learning

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Towards Safer digital Spaces: Automated detection of Cyberbullying Through Multi-Modal learning 5

Towards Safer Digital Spaces: Automated Detection of Cyberbu...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Francis Raj, J. Relin Krishnan, R. Santhana Kumari, d. Abitha Murali, S. Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RMK Engineering College Department of Computer Science and Engineering Thiruvallur India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

Cyberbullying and online harassment present significant challenges to digital safety, demanding robust detection systems capable of identifying abusive content across multiple formats. This work proposes a multi-modal framework that leverages ClIP and RoBERTa embeddings to detect and categorize cyberbullying and harassment in both text and image-based posts. By utilizing two curated datasets, Harassdetect-TextImage and CyberGuard-MultiModal, the model is trained on a diverse range of offensive and non-offensive content. Text data is preprocessed through advanced techniques, while ClIP and RoBERTa generate embeddings capturing the context of both textual and visual inputs. For sequential analysis of threaded posts, a Transformer layer further enhances the model's contextual awareness, enabling it to recognize harassment patterns that unfold over multiple messages. A Support Vector Machine (SVM) classifier then categorizes content into Non-Cyberbullying, Minor, Moderate, and Severe classes, each associated with confidence levels guiding automated actions. High-confidence classifications trigger immediate responses, such as content restriction or user warnings, while moderate-confidence cases are flagged for manual review. This adaptive framework not only improves accuracy but also allows for a continuous feedback loop, updating the model to address evolving patterns of cyberbullying. Evaluation results demonstrate the efficacy of this approach in creating safer online environments through automated and contextually aware cyberbullying detection. © 2024 IEEE.

关键词： Support vector machines

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Supply Chain Excellence: A Comparative Study of Amazon and Flipkart 3

Supply Chain Excellence: A Comparative Study of Amazon and F...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Sherin Beevi, l. Joe Prathap, P.M. Chaithresh, Kommi Sree Sai Vinil dani, W. Ahamed, Mohammad Vajid R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engineering and Technology Department of EEE Chennai India

ISBN: (纸本)9798350391565

This study aims to do a comprehensive comparative examination of the complex supply chain management frameworks that are used by Amazon and Flipkart, two of the biggest online retailers. Supply chain excellence is emphasized heavily in the inquiry, which examines important aspects including inventory control, fulfillment processes, technology integration, and customer-centric strategies. Through a thorough analysis and comparison of the many supply chain methods that Amazon and Flipkart have used, this research seeks to clarify the distinctive operational effectiveness, creative approaches, and possible areas for improvement in each of their supply chain ecosystems. Not only do the findings clarify the subtle nuances of supply chain dynamics in the e-commerce space, but they also have important ramifications for practitioners, scholars, and industry participants who are trying to make their way through the changing terrain of contemporary supply chain management. © 2024 IEEE.

关键词： Inventory control

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Neural Networks-Based Predictive Models for Self-Healing in Cloud Computing Environments 2

Neural Networks-Based Predictive Models for Self-Healing in ...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sujatha, d. Michael Raj, T.F. Ramesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. St.Peters College of Engineering and Technology Department of Computer Science and Engineering Avadi Chennai India Sclas Smats Department of Computer Applications TamilNadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350306415

due to the widespread adoption of cloud computing, protecting the security and uptime of cloud services is of crucial importance. Unforeseen malfunctions and performance bottlenecks in cloud settings can lead to considerable downtime and financial losses. Systems that can automatically detect, diagnose, and recover from errors using self-healing mechanisms are a promising new approach. This study investigates how neural network-based prediction models can be used to improve cloud-based systems' inherent capacity for self-healing. This research introduces a novel method of using neural networks to forecast cloud system failures and performance irregularities. The proposed prediction models are able to spot warning signs by assessing past data and current metrics in real time. Neural networks may accurately anticipate outcomes by recognizing complicated patterns and learning from large, varied information. Various forms of neural networks, including deep learning designs, are evaluated to determine their efficacy in diverse contexts. The study also looks into proactively reducing faults by incorporating these predictive models into self-healing systems. Responses, such as resource reallocation, workload migration, and backup procedures, are triggered automatically when problems are predicted. This preventative method drastically lessens the amount of time users spend waiting for their cloud-based systems to restart. Extensive simulations and real-world case studies yielded experimental data that prove the prediction models based on neural networks proposed for self-healing in cloud computing are effective. The models' proficiency in failure prediction enables corrective measures to be taken promptly and precisely. Implementing these predictive models in massive cloud environments is also discussed, as are their scalability, flexibility, and real-world consequences. by tapping into the potential of neural networks for predictive analysis, this study adds to the develop

关键词： Cloud Computing lSTM Neural Network Self-Healing

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Multilevel Credit Card Fraud detection Using Face Recognition and Machine learning 1

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： deshpande, Tushar datta, Shourjadeep Shah, Rushabh doshi, Vatsal dongre, deepika Department of Computer Engineering D. J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9789819923229

ISBN: (纸本)9789819923212

Technology in today’s world has wholly modified payment methods from cash to digital or card payments. Therefore, in these situations, the authentication system is essential for guaranteeing the legitimacy of a transaction. This is because such payment systems are susceptible to fraud or false transactions. As a result, numerous techniques to authenticate the user’s credentials have been developed and practiced. We use machine learning to predict the fate of the transaction by training our model on past or existing transactional data. despite the fact that these solutions boost security, they could not be more reliable. These systems may predict that the transaction is legitimate when it’s not. This paper intends to illustrate the idea that helps to increase the credibility of such a machine learning system with the help of integration with a face recognition system. The final decision of whether the transaction is fraudulent or not will be taken based on outputs obtained by the machine learning module and face recognition module. The face recognition module uses the lBPH algorithm. To decide the threshold in the face recognition module and test it, we experimented with 30 users for fraudulent and non-fraudulent face data. For the machine learning module, to select the algorithm to use in our system, we performed a comparative analysis between different algorithms and chose the one that had the best performance on the input dataset. In this way, a multilevel authentication system is proposed to help increase a transaction’s truthfulness. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Face recognition

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Evaluation of Supply Chain design decisions under Uncertainty - A Case Study Analysis

Evaluation of Supply Chain Design Decisions under Uncertaint...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Joe Prathap, P.M. Beevi, l. Sherin dani, W. Vinil Vivek, Panguluri Venkata Jubilson, E. Ajith R.M.D. Engineering College Department Of Computer Science And Engineering Kavaraipettai India St. Peter's College Of Engineering And Technology Department Of Eee Chennai India School Of Computer Science And Engineering VIT-AP University Andhrapradesh India

ISBN: (纸本)9798350371567

This study digs into the strategic supply chain decision-making process of Bridgestone Tires, a global tyre manufacturing leader, demonstrating the effectiveness of decision tree analysis in dealing with complex uncertainty. Bridgestone Tires has a variety of problems, including fluctuating exchange rates and uncertain demand patterns, demanding strategic plant placement across its global network. Using decision tree methodology, this study examines optimal choices under various current and future situations, providing light on the importance of addressing demand unpredictability and economic variables in financial supply chain design decisions. The findings emphasise the importance of thoroughly integrating financial data, identifying risks, calculating cash flows, and considering alternative scenarios. While decision tree tools are cost-effective, simulation models enable nuanced assessments of complex scenarios, but at a greater setup and operating cost. Finally, this study explains how firms can use decision tree analysis and simulation modelling to effectively manage uncertainties, allowing for more informed and optimised supply chain design decisions in dynamic global environments. © 2024 IEEE.

关键词： decision trees

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Multimodal Emotion Recognition in Video, Audio, and Text using deep and Transfer learning 3

Multimodal Emotion Recognition in Video, Audio, and Text usi...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Panchal, Vidhish deshpande, Tushar Kandoi, Tanishq deulkar, Khushali Narvekar, Meera D. J. Sanghvi College of Engineering Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Emotions play an important role in an efficient communication process. Many researchers have studied emotion recognition, however, the majority of their work has concentrated on one modality like text, or numerous modalities like text and speech, but just for one language. This imposes a restriction on the ability to recognize speech, text, or video. Considering multiple modalities at once is the answer to this. There is little study that takes into account all three modalities simultaneously. Therefore, in this paper, we propose a multimodal approach that detects the emotion expressed by a user in a video input where we will not only analyze the facial expression but also take the speech and the text into account while predicting the emotion and expand it to cross-language scenarios. The video input taken detects facial structure and the respective emotion by using some well-known deep learning models. For speech emotion prediction we use three datasets out of which one of them is a different language to implement the cross-language and cross-corpus scenario. The text extracted from audio is given as input to emotion recognition models within the Machine learning and deep/Transfer learning domains. Our results suggest that the multimodal system has a lot of credibility due to the consideration of all 3 modalities and give us a much more accurate context or breakdown of the emotions interpreted from the user input. © 2023 IEEE.

关键词： Emotion Recognition

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