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2nd International Conference on Data science and Network engineering, ICDSNE 2024

作者： De, Anurag Mugesh, C. Lalitesh, Battula Joshua, J. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819783359

One of the primary worries for the loved ones of elderly individuals is the accidental fall that can sometimes even lead them to death. Recent studies have shown that sensor-based methods in identifying falls mark a significant advancement in proactive fall detection, facilitating prompt assistance from concerned family members or caregivers to aid elderly individuals. Our primary objective revolves around the design and refinement of an arm-worn sensor-based fall detection method. Intelligently integrating accelerometer and gyroscope data into a wearable device, the model can efficiently analyze variations in different human daily activity movement patterns. This multifaceted approach enables our model to discern with precision between falls and activities of daily life (ADL). Through extensive experimentation and analysis, our results demonstrate the effectiveness of the proposed model in accurately identifying fall events while minimizing errors in detecting ADL. Achieving an impressive accuracy score of 98.66%, our novel approach proves to be more effective and beneficial in the realm of fall detection among elderly individuals compared to existing state-of-the-art methodologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accelerometers

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An Evaluation of Pre-trained CNN Architectures for Brain Tumor Segmentation and Detection 5th

An Evaluation of Pre-trained CNN Architectures for Brain Tum...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Kollipara, Venkata Kiranmai Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India

ISBN: (纸本)9789819771776

The foremost central body part is the brain in human nervous system. It accommodates 86 billion nerve cells. Brain tumors are dangerous magnification of abnormal cells that can be homicidal in the human brain. Some common symptoms of brain tumors may comprise: Headache, Seizures, nausea, vomiting, vision changes and numbness, and some other issues. The challenge of segmenting brain tumors manually is huge, so a method for instinctively segmenting is required. MRI images are often used in the early stages to identify brain tumors using convolutional neural networks (CNN), which lean on medical imaging data. The Important challenges in computer-assisted diagnosis (CAD) for medical purposes encompass brain tumor segmentation and identification. This study aims to evaluate and compare the accuracy of pre-trained DL models for grouping brain MRI images, especially AlexNet is 98.95, Google Net is 99.4, VGG16 is 99.0, and YOLO V7 is 99.5. The analysis evaluates a variety of unique purposes, such as computational effectiveness, model complexity, and classification accuracy. A seldom used technique for brain tumor detection uses deep learning-based methods like the YOLO NAS (You Only Look Once) family of models. Enhance brain tumor segmentation accuracy by analyzing and implementing the Red Fox Optimization. The accuracy of pre-trained YOLO-NAS object identification and segmentation model is 99.7, to generate an effective Brain tumor detection in an early-stage. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Magnetic resonance imaging

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Neural Style Transfer Using Convolution Architectures 3rd

Neural Style Transfer Using Convolution Architectures

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Afraa, Shaik Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819743582

Neural style transfer is the concept of blending image's content with a different image's style. This technique helps us generate images that are the same as the base image but with the style of the image. Due to this technique's capacity to produce eye-catching and beautiful visuals, it has experienced substantial growth in popularity. NST, which enables the fusion of content from one image with the creative style of another, has become a fascinating approach in the fields of computer vision and artistic expression. This paper aims to examine how different neural network models and optimization techniques affect the effectiveness and attractiveness of neural style transfer. We specifically compare Adam, Adamax, SGD, RMS Prop, AdamW, Adadelta, Adagrad, and Nadam optimizers with VGG19, ResNet50, ResNet152V2, and Xception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Early-stage Alzheimer's Detection Using Flexible Representation of Quantum Imaging Technique

Early-stage Alzheimer's Detection Using Flexible Representat...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Mishra, Deepasikha Sahu, Ansuman VIT-AP University School of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331508432

This paper presents a novel methodology for the early detection of Alzheimer's disease through MRI brain scans, utilizing quantum image processing. This approach utilizes the Flexible Representation of Quantum Images (FRQI) algorithm to encode, process, and analyze images for early-stage Alzheimer's detection by identifying subtle patterns indicative of the disease. Quantum encoding, central to this methodology, transforms pixel intensities into quantum states, enabling efficient data manipulation and storage. The FRQI algorithm associates each pixel's intensity with a quantum angle, encoding the entire image as a superposition of quantum states. This encoding allows for compact and efficient representation of high-dimensional image data, facilitating advanced quantum operations. Fidelity measures, including state vector fidelity and shot noise fidelity, are employed to assess the similarity between test and reference images. Results indicate that quantum metrics significantly outperform classical techniques in terms of reliability and consistency. Quantum metrics exhibit minimal overlap between similar and different images, ensuring accurate classification. This methodology aims to enhance the sensitivity and precision of early Alzheimer's disease detection, offering a promising approach for advanced medical image analysis. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Music Recommendation System Using Facial Detection Based Emotion Analysis 4

Music Recommendation System Using Facial Detection Based Emo...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Guthula, Manvitha Sri Bordoloi, Monali School of Computer Science and Engineering Department VIT-AP University Amaravati India

ISBN: (纸本)9798350387063

Music is intrinsically important to humanity due to its significant connections with the brain. Behaviors and emotions of humans are driven by music, which generates a unique sense of unity in the brain. This paper introduces an innovative Music Recommendation System (MRS) that uses real-time face emotion detection to personalize music choices in real-time. The proposed model is based on Convolutional Neural Network (CNN) in order to recognize emotions from users' facial expressions. The emotions are then combined with a sophisticated music classification system to suggest songs that match the user's current emotional state. To enhance the user experience, user-friendly web application is created for the working of the proposed model. It allows for real-time video feed processing and instant music recommendation updates. Additionally, the system employs emotion music mapping, where musical characteristics such as pitch, tempo, and volume are analysed to correlate specific emotions with appropriate music selections. This study enhances affective computing and music information retrieval by showcasing the effective application of real-time emotion recognition in music recommendation, leading to the development of more intuitive and responsive digital media platforms. © 2024 IEEE.

关键词： Convolutional neural networks

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Chili Leaves Disease Identification Using Artificial Neural Network Algorithms 8th

Chili Leaves Disease Identification Using Artificial Neural ...

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8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Kantharaju, Kanaparti Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9783031511660

Deep learning (DL) and machine learning (ML) approaches have made it easier to recognize and identify objects in photos. Following in the footsteps of their success in other industries, neural networks have lately entered a few agricultural and farming applications. Plant disease detection software may help farmers manage their crops more effectively and increase yields. Utilizing images to diagnose plant disease in crops is a difficult task in and of itself. The use of specialist control measures requires both the detection and identification of species. In this chapter, a study of research initiatives that used Google Net, a kind of DL, to solve different plant disease detection difficulties was undertaken. One hundred eighty images of chili illness from the Kaggle public domain were given to the Google-Net convolutional neural network (CNN) in order to assess training accuracy. The final training accuracy ranges from 30% to 100% by taking into consideration various learning characteristics, including CNN optimizers SGDM, ADAM, and RMSProp. Max Epochs and giving Dropout probability, Strides, Dilation factor, and padding values as constants. The Google-Net CNN architecture achieves 98.61% accuracy in SGDM and ADAM with Max Epochs of 20, 30, and 40, respectively, according to the simulation. Moreover, it was claimed that SGDM is suitable for training the Chili Dataset, whose Max Epochs are substantially smaller than in SGDM and ADAM. The two optimizers have three epochs: 20, 30, and 40, with low epoch 20 producing superior accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crops

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A Comparative Study on Natural Language Processing approaches for Medical Prescription Classification Using Deep Learning Techniques 3

A Comparative Study on Natural Language Processing Approache...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sravanthi, Velavolu Malla, Sree Jagadeesh School of Computer science and Engineering VIT-AP University Andhra Pradesh India

ISBN: (纸本)9798331518592

In the medical field, knowing prescription drugs is essential to correctly diagnosing illnesses. Examining medical prescriptions has gained significance due to the rise in new diseases and prevalent health issues. This article investigates the application of natural language processing (NLP) and deep learning models in interpreting medical prescriptions, shedding light on the diverse methods and strategies involved. Researchers can effectively extract valuable information from prescription texts by employing deep learning algorithms and NLP techniques, leading to enhanced medication comprehension and categorization. The study emphasizes the essential roles of deep learning and NLP in analyzing prescription medications, assessing the effectiveness of these methods through various datasets and technological approaches. The review emphasizes how important they are to raising diagnostic and therapeutic efficacy. Using deep learning and text categorization techniques in prescription analysis improves patient outcomes and medical care, enhancing healthcare delivery and overall well-being. © 2024 IEEE.

关键词： Contrastive Learning

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Keratoconus Eye Disease Detection Using CNN with VGG-19 3rd

Keratoconus Eye Disease Detection Using CNN with VGG-19

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Kurra, Yaswitha Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819743582

The eyes are among the most important organs in the human body. We rely on our sense of sight to see the world and comprehend what is going on around us. It can be challenging to detect and identify some eye conditions. Keratoconus is one of the rarest and most challenging to diagnose eye conditions. A progressive corneal condition called Keratoconus causes corneal scarring and thinning (Zhou et al. in Invest Ophthalmol Visual Sci 39(7), 1117–1124, 1998). As the etiology of Keratoconus is yet unclear, early detection is highly challenging. However, doctors do believe that aggressive eye rubbing and a mix of hereditary and hormonal factors may contribute to the condition. Early Keratoconus detection makes it easier for ophthalmologists to identify the eye, and the patient is better equipped to adjust to the changes. As a result, early Keratoconus detection is necessary, and we created a model for that aim. Even though there are previously many Machine Learning and Deep Learning techniques existed, which were used in the detection of Keratoconus but the model we used is more efficient and reliable than the existing algorithms. The model which is used is a Deep Learning algorithm known as VGG-19. The VGG-19, often referred to as the Visual Geometry Group, is used to identify Keratoconus. With 19 convolution layers, the deep convolution neural network architecture VGG-19 is ideal for accurate object identification and picture categorization. As opposed to VGG-16, which has 16 Convolution layers, it is an upgraded version. Due to its higher accuracy and quicker training pace, VGG-19 outperforms the currently used techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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A Novel Hybrid Optimization Algorithm for Materialized View Selection from Data Warehouse Environments

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computer Systems science & engineering 2023年第11期47卷 1527-1547页

作者： Popuri Srinivasarao Aravapalli Rama Satish School of Computer Science and Engineering VIT-AP UniversityAmaravatiIndia

Responding to complex analytical queries in the data warehouse(DW)is one of the most challenging tasks that require prompt *** problem of materialized view(MV)selection relies on selecting the most optimal views that can respond to more queries *** work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from *** proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique(ECHT).The constraints such as self-adaptive penalty,epsilon(ε)-parameter and stochastic ranking(SR)are considered for constraint *** two constraints helped the proposed model select the finest views that minimize the objective ***,a novel and effective combination of Ebola and coot optimization algorithms named hybrid Ebola with coot optimization(CHECO)is introduced to choose the optimal *** and Coot have recently introduced metaheuristics that identify the global optimal set of views from the given *** combining these two algorithms,the proposed framework resulted in a highly optimized set of views with minimized *** cost functions are described to enable the algorithm to choose the finest solution from the problem ***,extensive evaluations are conducted to prove the performance of the proposed approach compared to existing *** proposed framework resulted in a view maintenance cost of 6,329,354,613,784,query processing cost of 3,522,857,483,566 and execution time of 226 s when analyzed using the TPC-H benchmark dataset.

关键词： Materialization ensemble approach stochastic ranking optimization optimal view selection

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Age Prediction Using Quantum Fourier Transform and Convolutional Neural Network

Age Prediction Using Quantum Fourier Transform and Convoluti...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Mishra, Deepasikha Naman, Sreajan VIT-AP University School of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331508432

This paper presents the utilization of quantum computing in conjunction with deep learning, which constitutes a novel approach to surmounting the constraints of conventional methods in age prediction. This proposed method aspires to substantially enhance the efficiency and reduce the computational time of age prediction models by harnessing the inherent traits of quantum computing, such as superposition and entanglement. The development of quantum feature encoding techniques facilitates the representation of classical age-related data in quantum form, enabling more effective processing and analysis. Furthermore, implementing a hybrid classical-quantum architecture supplemented by quantum-enhanced classical algorithms highlights the synergistic relationship between classical and quantum computing paradigms. A comprehensive performance assessment has been carried out against conventional deep learning techniques. This paper demonstrates the current potential of quantum fourier transform and convolutional neural network in age prediction and lays a robust foundation for future advancements in quantum-enhanced applications across various domains. © 2024 IEEE.

关键词： Quantum computers

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